Business Intelligence Strategy and Roadmap for the Institutes of Higher Education. A report commissioned for An Chéim Computer Services

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1 Business Intelligence Strategy and Roadmap for the Institutes of Higher Education A report commissioned for An Chéim Computer Services December 2007

2 Table of Contents 1 Management Summary Context Identifying and Addressing Business Challenges IBM Information Maturity Model What Implementation of the BI Strategy Will Deliver What the BI Blueprint will Look Like A Phased Delivery Approach Target State 3 Year BI Blueprint for the Sector Implementing the BI Strategy 11 2 Business Strategy / Drivers Introduction Global Drivers for Business Intelligence in the HE Sector Business Drivers for the HE Sector in Ireland Overview Specific Business Drivers / Requirements in Ireland HEA requirements Comparison of IoT Sector Business Intelligence Requirements with UK and US Conclusion 23 3 Baseline Assessment Introduction Baseline Assessment Findings Performance Management Functional Management Information Management Technology Infrastructure Organisational Management IBM Information Maturity Model Conclusion 35 4 Future State of Business Intelligence Introduction Key features of the BI Blueprint What the strategy will deliver Key Challenges Enablers Target State BI Blueprint for the Sector Functions provided in the BI Blueprint Major components to be included BI Blueprint Target State Data Architecture Architecture overview Architectural Options Option 1: Centralised approach Option 2: Distributed approach Logical Architecture recommendation Target State Data Architecture Components Overview Data Sources and data quality Data Integration Data Repositories 51 2

3 4.5.5 Analysis and User Access A phased delivery approach Target State 3 Year BI Blueprint for the Sector Laying the foundations Sector Alignment and Adoption The Role of HEA and An Chéim BI Guiding Principles Business Intelligence Centre of Competence (BICC) Data Governance / Data Quality Management 59 5 BI Roadmap and Implementation Planning Introduction Integrated BI Roadmap Wave 1 (Years 0-3) Lay BI Foundations Programme Detail Stream 1: Data Infrastructure Stream 2: Reporting Delivery Stream 3: Performance Management Stream 4: Alignment and Adoption Stream 5: Programme Management Wave 2 (Years 4-5) Develop BI Capabilities Programme Overview Wave 3 (Year 6) Embed BI into Business Processes Programme Overview Implementing the BI Strategy 80 Glossary & Large Format Figures 81 Glossary 81 IBM Information Maturity Model 82 BI Blueprint 83 Integrated BI Roadmap 84 Wave 1 Project Portfolio 84 Wave 1 Project Portfolio 85 3

4 1 Management Summary 1.1 Context In increasingly competitive business environments one of the key characteristics of the most successful organisations is their ability to recognise the value of the information at their disposal and to leverage it appropriately. The decision making process in such organisations is supported by the provision of tailored reports to key decision makers drawing relevant information from across multiple information repositories. The term Business Intelligence describes an environment where relevant, accurate information is provided to decision makers in time to respond with speed in making decisions and taking action to drive organisational success. In the Higher Education Sector internationally leading institutions are implementing enterprise-wide Business Intelligence systems that meet their external needs for accountability and support the increasing internal requirement for more targeted information and analytics. In June 2007 IBM were commissioned to help define a Business Intelligence (BI) Strategy for An Chéim and the Institute of Technology (IoT) sector in Ireland. The BI strategy aims to provide the sector with a tailored, high quality Business Intelligence solution to cover a period of six years. This document is the final report from that study. 1.2 Identifying and Addressing Business Challenges A successful Business Intelligence Strategy must be set in the context of business goals and strategy of the organisation / sector concerned. This BI Strategy has been developed in the context of the business challenges facing the IoT sector in Ireland drawing on international experience where appropriate. The Strategy has been developed to help managers, academics and other key stakeholders to address the challenges they encounter in managing their institutes. Requirements and needs were gathered through a series of in-depth interviews and work-shops with various members of staff from three Institutes: Athlone; Galway-Mayo; and Tralee. Interviewees were drawn from a wide spectrum of grades and functional areas including: Directors; Registrars; Finance Directors / Controllers; Human Resources Managers; Heads of School and Department; Administration; Time-tabling; Librarians; Student Services and IT. In addition, follow-up interviews were held with key staff from Cork and Dublin Institutes. We also reviewed the findings from the Systems Exploitation Survey carried out by An Chéim in February and March 2007, had a number of meetings with the HEA, and carried out a short survey inviting institutes to add their own specific requirements and comments. 4

5 1.3 IBM Information Maturity Model The IBM Information Maturity Model was used to assess the current baseline in the sector. This model is used globally by IBM as a means of determining how effectively businesses in various industries manage and make use of the information they collect, and to indicate how sophisticated the maturity needs to become to help organisations meet their strategic goals. The model has five levels, from 1 (lowest) to 5 (highest). These are shown with descriptions in the chart 1 below. The line between levels 2 and 3 indicates that this is the point at which integrated information is made available to the organisation. It is a significant step to achieve this level. On a scale of 1 (lowest) to 5 (highest), we rate the information maturity of the IoT at Level 1, primarily due to the fragmented nature of reporting and the difficulty in meeting business needs for information. The target state on implementation of the BI Strategy is Level 3. This indicates both the scale of the challenge and of the potential benefits that can accrue to the sector on implementation of the BI Strategy. 1.4 What Implementation of the BI Strategy Will Deliver The Business Intelligence Strategy provides a blueprint that meets the reporting needs of the sector. The blueprint not only addresses the immediate stated needs of the sector identified during the consultation phase of the project, but 1 Please note that a large format version of this chart is available in the appendix of this report. 5

6 also takes into account the impact of emerging international best practices in the application of Business Intelligence in the Higher Education Sector. Based on the findings from the Baseline Assessment above, IBM considers that the following eight capabilities need to be enabled by the BI Strategy: 1. Improved reporting on student There is an urgent requirement to improve the ability to provide reports on students. Initially this can be considered an operational reporting need consisting largely of lists and counts of particular groups (e.g. during registration period) but should also include analytical reporting. As student information is central to what the institutes do, improved access to student information will be the core of any improved flexible reporting environment covering multiple systems. 2. Improved cross-system reporting The ability to generate accurate information from more than one system (e.g. Banner, Core, Agresso) more quickly and with less effort is a current need to support Unit Costing and similar analytical reports. Based on stated requirements and knowledge of the sector, it is clear that the need for crosssystem reporting will grow over the coming years, though the majority of information needs are still likely be from single systems. Implementation of the strategy will also need to enable the addition of data from other sources in order to support external data requirements or the use of different operational systems within IoT. 3. Widespread access to reporting suited to users The information within the sector needs to be made available easily and in a timely manner to different groups of users in ways that are appropriate to that community. Access should be web-based, providing graphical and text information, and ideally with a single-sign on to reports. Access to data will be governed by user profiles to ensure that data security is maintained. Examples of the types of access will include: Senior managers and most teaching staff need simple access to standard reports, with flexibility for selection and drill-down, to include a dashboard showing Key Performance Indicators. Functional staff users will need more flexible access with ability to create and share their own reports. There will be a small number of power users with the ability to produce complex reports or answers to ad-hoc queries. Providing ready access to the types of information and reports described above will be a key enabler in promoting a step change in the decision support capabilities available to the IoT. 4. Improved data standards and quality The sector needs to improve its understanding of data in various ways in order to gain the benefits from the BI Strategy and to enable the cross-system reporting required, and where appropriate make improvements to data quality within the operational systems. The factors necessary to achieve this will include: 6

7 The development of a clear understanding and definition of what data quality means within the sector. Commitment to data quality by IoT down to operational level ensuring that all end users understand the importance of data quality at source and are encouraged to deliver to agreed standards. Agreement on Data standards, stewardship & ownership. Improved accuracy and completeness of information within the operational systems. Improving understanding of the underlying data, so that users can make correct interpretation of the information provided, or define queries to achieve results required. Data definitions and coding need to be more consistent across the sector, in order to allow for the creation of a common reporting platform. This should not reduce the flexibility for IoT to operate differently as appropriate, but would encourage those working in a similar way to follow the same standards. As the HEA are in the process of defining what information they will need from the sector, there is an opportunity to work with them to agree the specific content to be provided. Taking advantage of this opportunity could help ensure that the information provided is both relatively simple to produce and meaningful to the sector for its own use. 5. Agreed Key Performance Indicators (KPIs) In addition to defining what reports are required, there is an opportunity for the IoT to define what Key Performance Indicators they will track to monitor progress against their business objectives. These typically take the form of indicators to be tracked (e.g. progression), and measures that will be used (e.g. percentage of students that pass exams on schedule and continue on the course). Defining these at the outset of the BI Strategy will allow the most important measures to be included in the design, and reduce the likelihood for additional effort later. Agreeing a standard set of KPIs and measures will simplify the overall design. There will be the ability for IoT to tailor the KPIs and measures or to add their own, as it is recognised that the IoT will not all have the same objectives and priorities, and that in any case the KPIs are likely to change over time. Dependent on the indicators and the timing, the results of the KPIs can be presented in reports or management dashboards that provide managers with visibility of the most important information, typically in a graphical format that can highlight whether targets are being achieved. 6. Simplified external reporting requirements The BI Strategy should support reporting for external stakeholders, such as the HEA, CSO and other bodies, and reduce the effort required to provide information to these stakeholders. In the timescales of this strategy, there is an opportunity to encourage the external bodies to coordinate their needs for information, potentially reducing the overall work required to provide it. For instance external stakeholders could coordinate the items of information required, as well as the time periods covered. The external content can also 7

8 be linked to the definition of KPIs, so that the IoT focus on the same items that they are measured on. 7. Flexible architecture and technology The BI strategy will require an overall architecture that is robust and flexible enough to provide a common reporting capability while also supporting the needs of the individual institutes. As well as the need to support requirements that are currently anticipated, flexibility is needed to allow individual IoT to produce different or additional reports, on top of a common base. Given the six year duration of this strategy and the on-going life of the system beyond that point, there will need to be flexibility to allow for changes to the operational systems, including upgrades, functional changes by the vendors, and even possibly changes to the selected source applications. 8. Improved support model Specialist support to the sector will need to be provided to ensure the efficient development of the BI capabilities needed across the Institutes, and also to enable individual institutes to enhance reporting if they have additional needs. 1.5 What the BI Blueprint will Look Like Our recommended blueprint for BI in the IoT Sector is a fully integrated environment that provides the eight capabilities described above. A key part of any BI Strategy is the architecture, as this defines the technical framework for the achievement of the series of projects that deliver the strategy. The architecture provides technical direction for all the projects, and allows individual projects to deliver components that support the whole. As described above, there needs to be a reporting architecture that can support the major needs of the sector and support the eight capabilities described earlier. The architecture needs to: Provide functionality to combine data across the source systems and deliver reports to the various user communities as and when they need them. Support improved reporting from each operational system, particularly the student system. Be sufficiently flexible to meet needs of institutes and life-cycle changes to source systems. The recommended high-level architecture for the Business Intelligence Strategy follows the schematic below: 8

9 Test Scores Records Student Data Data Transmission Financial HR Timetable Library Data Transformation Institution Data Warehouse Internal data External Data Analytic/Data Mining Tools Integration to Other Systems Input of Student, Financial, HR & Library Data Institutions often provide access to data via manual tools, like a portal. In the future, access and delivery of data will be automated. Transformation of data Data is transformed to common standards reflecting the needs of the organization for analysis and reporting Institution Data Warehouse The data needs to be loaded into a robust data model that will support a broadrange of analytics 4 Analytic Tools An analysis tool to interrogate the data and find key business issues and insights helps administrators to make educated decisions and drive innovation 5 Integration to Other Systems Many applications can leverage other data, including financial planning, and demand planning This schematic provides for consolidation of information through a data warehouse layer and provision to users through analytical and data mining tools. The data warehouse layer is necessary to provide the cross-system reporting capability. A number of architectural alternatives that fit this schematic were considered in order to determine which is most appropriate to the IoT Sector. Based on the costs, complexity and implementation timescales of a centralised approach and the lack of a packaged solution, and in the knowledge that the majority of reporting will remain specific to single source, IBM recommends a distributed architecture approach for the sector. Chapter 4 of this report sets out a full outline of the analysis conducted and recommendation reached in arriving at this decision. 1.6 A Phased Delivery Approach Having determined the long-term BI Strategy for the IoT sector, it is necessary to consider the path to achieving it. In IBM s experience, it is not possible move from Maturity Level 1 to Level 3 in one step. Historically many organisations have tried this, and there are countless examples of failed data warehouse projects, which in hindsight have attempted to deliver too much too quickly. Typically these projects have built a large data warehouse without paying sufficient attention to the underlying data issues, and many data-related problems appear only after a lot of time and effort have been spent building a complex solution. Taking this into consideration, IBM recommends a phased approach to the delivery of the BI Strategy, as set out below: Initiatives to be delivered within these waves have been defined and grouped into logical projects, and sequenced based on benefits, costs, level of complexity and dependencies. Details on these initiatives are set out in Chapter 5 of the Strategy. 9

10 1.7 Target State 3 Year BI Blueprint for the Sector Over the next 3 years, the sector should target a number of initiatives which address immediate reporting needs while developing the basic foundations for the 6 year BI target state. This should enable Improvements to student reporting and a cross-system view targeted at cross system reporting This should be addressed as a priority through the development of a Student Data Repository populated with currently available student data from Banner and structured to simplify the access for operational reporting and analysis. A BI user presentation layer should remove the technical complexity of accessing student data and support a cross system view targeted at Unit Cost reporting. Efficiency improvements to the reporting process This involves efficiency measures to reduce the manual overhead of data collation, freeing up resources to focus on more value-added analysis activities. This can be achieved through the deployment of a single BI Reporting platform which automates generation and distribution of standard reports and a flexible, user friendly self-service interface for faster turnaround of ad-hoc requests for information. Quick wins can be made through automating external sector reports and exploiting this information for internal management decision support. Increasing Business Intelligence Competence Business Intelligence knowledge and skills need to be developed and shared across a wider community, reducing the dependence on 1-2 people within each IoT. This can be achieved through the development of a Business Intelligence Centre of Competence aimed at achieving greater collaboration across IoT, developing individual IoT with the requisite knowledge and skills and exploiting shared economies of scale. Greater Information Accuracy, Reliability and Control It is essential that greater exploitation of information is supported by improvements in data quality, data standards and process improvements in the management of data. This is a prerequisite to improving the information maturity of the sector and meeting the 6 year vision. Achievement of this will require a shared service for data management incorporating data management as a key pillar of the Common Standard Design. This shared service competence will include defining data stewards and data quality and transformation services. Greater transparency and accountability The drive for more transparency and accountability across Higher Education sectors internationally has led to the need to develop capabilities in performance management. Addressing this challenge in the context of this strategy will require the definition of common KPIs agreed across major IoT processes, IoT specific KPIs and the support of HEA KPIs. 10

11 The Sector s Business Intelligence strategy involves a 6 year journey from improved operational reporting on transactional data to more integrated reporting and analytics. The first 3 years should involve addressing immediate needs outlined while laying the foundations for the eventual 6 year BI Roadmap. The following architecture outlines which of the overall major functions from the six year blueprint should be enabled in the first 3 year period. Wave 1: Data marts and limited cross-system reporting PROD PROD CORE HR ETL HR HR Mart Mart Web Agresso Finance Banner Millennium Syllabus Plus ETL ETL ETL ETL Fin Fin Mart Mart Student Student Mart Mart Millennium Millennium Mart Mart SchedPlus SchedPlus Mart Mart Limited Federation Layer Analytics Query Report OLAP Other Limited Crosssystem Analytics U.Cost Cube Graphics Dashboard Self-Service Reporting Data Sources Data Integration Data Repositories Analytics Access The interim architecture will be a subset of the full architecture to be developed. The emphasis will be to provide improved student reporting followed by improved reporting on the other systems (Agresso, Millennium and Syllabus Plus) there is already a reporting database for Core, though that may need upgrading. A Student reporting database will be implemented, while the current HR repository as part of the CORE BI Suite will be transitioned to the new environment. New analytical reporting databases for Agresso, Millennium and Syllabus Plus will also be implemented, following the student reporting database. A single reporting and analytics tool set will be selected and implemented for use across all data repositories hosted on the new BI data infrastructure. This tool set will allow a wide range of web-based user access. The standard reporting and self-service flexible reporting elements of the tool will be implemented to meet the 3 year BI goals. There will also be emphasis on data quality in the operational systems as already described, though this is not visible in the architecture. 1.8 Implementing the BI Strategy Implementation of the BI Strategy should be seen as a continuation in the evolution of reporting / BI in the IoT Sector. This evolution is characterised by a number of distinct stages of development: 1 st Generation BI - the predefined reports that were bundled with Application Packages. These reports are written in a programming language resulting in 11

12 the need for specific programming skills to amend existing or create new reports whilst minimising the effect on performance of production systems. 2 nd Generation the provision of tools that allow users to have direct access to data, to generate ad-hoc queries and to produce limited graphical and other analytical reports. Queries and reports can be written by users, thereby alleviating some of the impact on IT Departments, but introducing a potential risk to production system performance. Data Warehouses/Marts can be used in 2nd Generation BI Implementations to eliminate the performance impact of these reporting tools on the production systems and to store data in a manner where it could be accessed more easily. Recent successes in pilot projects using Oracle Discoverer 10g for the Banner and Core applications have demonstrated that 2 nd Generation BI implementations can be run from An Chéim s centrally hosted environment for ad hoc reporting 2. Implementation of this BI Strategy will consolidate progress to date and will move the IoT sector towards the 3 rd and 4 th Generation BI capabilities required for Maturity Level 3 on IBM s Information Maturity Model. It should be noted however, that this progression will not happen without appropriate funding being in place and sustained throughout the full six years of the implementation programme. The chart 3 below illustrates the integrated approach being adopted at sector level to the implementation of BI across the IoT: The first three years will see significant progress in the overall implementation of the BI Strategy with numerous projects aimed at laying the foundation for effective BI in the sector. The intention is to incrementally roll-out enhanced BI functionality to IoT during this period as it is developed rather than waiting to the end of the period to do so. 2 One of the early projects in the Strategy will be the roll-out of this 2 nd Generation BI functionality to the IoT using Oracle Discoverer 10g (Project 2d - p.71). 3 Please note that a large format version of this chart is available in the appendix of this report. 12

13 BI projects will be delivered as part of three waves : Wave 1 Lay BI Foundations Wave 2 Develop BI Capabilities Wave 3 Embed BI into Business Processes Wave 1 will commence with a mobilisation phase aimed at ensuring appropriate governance, planning, involvement and risk management protocols are in place for the BI implementation programme. Significant detail on the make-up of Wave 1 implementation projects and work streams is set out in Chapter 5 of this strategy. Waves 2 and 3 are also outlined toward the end of Chapter 5. 13

14 2 Business Strategy / Drivers 2.1 Introduction The first stage of the strategy is to determine and gain agreement on the business requirements and drivers for Business Intelligence. A successful Business Intelligence Strategy must be set in the context of business goals and strategy of the organisation / sector concerned. This BI Strategy has been developed in the context of the business challenges facing the higher education sector in Ireland drawing on international experience where appropriate. The Strategy has been developed to help managers, academics and other key stakeholders to address the challenges they encounter in managing their organisations. Stage 1 of the study focussed on understanding the particular business drivers for BI in the IoT Higher Education sector in Ireland. Requirements and needs were gathered through a series of in-depth interviews and work-shops with various members of staff from three Institutes: Athlone, Galway-Mayo and Tralee. In addition, follow-up interviews were held with key staff from IoT in Cork and Dublin. We also reviewed the findings from the Systems Exploitation Survey carried out in February and March 2007, had meetings with the HEA and carried out a short survey inviting institutes to add their own specific requirements and comments. In this section we start by looking at the global drivers for Business Intelligence in the Higher Education Sector. We then, based on our interaction with the IoT and the HEA, and our knowledge of the sector in Ireland, outline an analysis of the BI drivers in Ireland. We also consider the impact of the HEA on reporting requirements, and then compare the sector with some leading Higher Education institutes internationally. 2.2 Global Drivers for Business Intelligence in the HE Sector The IBM study Higher Education 2012: Executing to Lead in the Learning Ecosystem (HE2012) considers the strategic issues facing the Higher Education sector and makes recommendations for addressing these issues. This paper, developed by IBM s Global Higher Education Practice, provides an analysis of the emerging challenges facing educators and managers in the Higher Education Sector over the period to 2012, including many challenges that require much better use to be made of Business Intelligence. For reference, it has been included as an appendix to this report. The HE2012 study shows that Higher Education Institutions globally are facing a number of key challenges ranging from fundamental changes to the manner in which they educate, ever increasing demands from students, a changing student profile away from what had been the norm in the past and an increased pressure on accountability. All of these pressures require Institutions to achieve more with less and are all dependent on management and academics leveraging the best possible use of the information at their disposal. At the same time these global higher education Institutions are facing a large increase in the amount of data 14

15 available, generated by increases in departmental and enterprise systems that are not always integrated. The report identifies five mega trends that are shaping the Higher Education sector over the next five to seven years. These are: 1. Students will become consumers with choices 2. There is an ongoing and increasing focus on accountability 3. Demographic profiles are continuing to change 4. There is an increased use of and reliance on technology 5. Faculty roles and responsibilities are changing Responding to these challenges is complicated by a number of internal factors that apply in many HE Institutions: Increased management complexity in effectively responding to evolving business requirements Functional and departmental silos that lead to operating inefficiencies and organizational fragmentation An inflexible IT infrastructure that is expensive and overly complex Traditional attitudes and management practices that conflict with the need for rapid, insight-driven decision making and execution. The increased focus on accountability together with an explosion in new disparate, siloed applications and data 4 across all areas of institutions has led to a need to pull this information together in a manner that is meaningful and useful for management, staff and students. Leading Higher Education Institutions are creating enterprise-wide data management systems that meet the external needs for accountability and support the increasing internal needs for more information and analytics. This is shown in the slide below: 4 While from a technical perspective the IoT sector does not have the spread of departmental systems that occur elsewhere in the world, there are challenges in effectively combining information across the best of breed applications in place in the sector. These are addressed in the suggested architecture set out in Section 4 of this strategy. 15

16 With new levels of insight, institutions can optimize their business processes, improve their efficiency and respond effectively to the growing demands for accountability Business Process Improvements Student, operational, and competitive data Market research Student Systems HR Enterprise Data Management Research Library Finance Campus Vendors Source: IBM Institute for Business Value Improved Processes via Systematic Intelligence Real-time analytics, decision support, and predictive intelligence Accommodation augmenting traditional creative/intuitive methods Business insights Students Educators Management Optimized operations Marketing Focused recruitment Improved Development Teaching & Learning / Research Improved faculty / course assessments Improved courses More Targeted Research Business Performance Management Better utilization Reduced costs Improved satisfaction 2.3 Business Drivers for the HE Sector in Ireland Overview The business strategy and drivers review for the sector in Ireland is based on interviews conducted with members of staff from three Institutes: Athlone; Galway-Mayo; and Tralee. Interviewees were drawn from a wide spectrum of grades and functional areas including: Directors; Registrars; Finance Directors / Controllers; Human Resources Managers; Heads of School and Department; Administration; Time-tabling; Librarians; Student Services and IT. In addition, follow-up interviews were held with key staff from Cork and Dublin Institutes. We also reviewed the findings from the Systems Exploitation Survey carried out by An Chéim in February and March 2007, had meetings with the HEA, and carried out a short survey inviting institutes to add their own specific requirements and comments. Many of the key challenges being faced in the Higher Education Sector globally are also impacting Institutes of Technology in Ireland. The role of IoT is changing with increased focus on meeting the challenges and opportunities presented by issues such as: changing demographic profiles; an increasingly competitive higher education sector; the strive to enhance the overall student experience; increased student expectations on the learning environment; an increased focus on life-long learning; developing enhanced linkages with Industry; responding to the needs of the knowledge economy; and the need to be innovative in responding to evolving funding models. 16

17 One of the key factors to be addressed in effectively responding to these challenges is enhancing the ability of IoT to make informed decisions based on the best possible use of the information available to them. In this regard a key enabler will be the development and implementation of an integrated BI environment Specific Business Drivers / Requirements in Ireland Some of the specific business drivers / requirements identified during the development of this BI strategy are set out below 5. Importance of Business Intelligence: Reporting is seen as important by all IoT. This was a consistent message from all interviews, from the System Exploitation Survey and from our minisurvey. The need for reporting covered both operational reporting, relating to reports such as the generation of lists and counts of people who have registered by course at a point in time, and analytical reporting, for instance the ability to provide enhanced analysis of the enrolment profile or capability to analyse programme and learner performance in respect of progression.. Typically, improved information was seen as critical or very important. External formal requests are resource intensive and time consuming, due to lack of automated solution. Many or most IoT are investing in people and tools to get information from their operational systems. At critical periods (e.g. registration, examinations) there is a need for more frequent and widely available information without calling on the staff involved in the relevant process. Need for new report content Examples of requests for additional reports identified included: Definitive analysis of costs broken down per course and student. Adding more cost elements to unit cost; for instance cost of space, use of resources. Analysis of marketing effort and success in both recruitment and student progression and retention. Analysis of recruitment by course and geography, including enrolment profile and targeted cohorts. Ability to manipulate transfer/progression data to enable further analysis and to support strategic decision-making. Analysis of student retention, progression and results by socio-economic and demographic background, school, grades and other characteristics including the ability to identify "at risk" students at an early date. Tracking of staff credentials, research, papers written and presentations 5 The functional requirements identified from our survey are shown in Appendix Z covering what information is needed for internal and external users. 17

18 Provision of tools to measure participation of various categories of lifelong learners against strategic targets. Tracking mechanisms for life-long learning, with a need for an improved capability to track distance learners and mature students. Tracking and liaison with alumni, though there is no data to support this yet. Notwithstanding the identified requirements for the above listed operational and analytic reports, there is limited knowledge of and no clearly defined or apparent immediate need for the early implementation of advanced analytics tools, such as data mining. Need for KPIs and flexibility Some but not all IoT are starting to use Key Performance Indicators (KPIs) as a management tool. There is no agreed definition of standard KPIs between IoT, apart from the measures defined in external reports provided to HEA, CSO etc. There appears to be a desire to make greater use of KPIs, though an inhibitor to adoption may be that that possible measures are difficult to produce using the current reporting systems. In-built flexibility in reporting / BI is required to allow IoT to develop and measure institute specific KPIs. Flexible and self-service reporting As well as a set of standard reports, there will be a need for flexible reporting, allowing each IoT to determine what information it needs. Reporting needs are likely to vary due to different priorities across the Institutes. Any recommended BI solution, tools, support and documentation all need to support this. There is a need for ad-hoc reporting within the IoT, to meet management needs that may not be predictable, and to answer external questions such as parliamentary questions. Many of the reports required are lists, which should be available as operational reports from the source systems, including reports for individual staff members on e.g. class, performance. These should be available in a self-service model to appropriate members of staff. Centralised reports exist, but new reports take a long time to be created as all IoT need to agree before they are developed. There will need to be a mechanism that allows for more timely development of central reports Reporting on Banner The student management system in the sector is Banner. There are a number of reports that have been created centrally, but there is no reporting system currently implemented. Extracting data from the current Banner implementation is complex. The underlying table structure within Banner is not widely understood or fully documented. 18

19 Banner data contains time-stamps, which allows reports to be produced for an effective date or period (e.g. how many students were registered on 1 st March). However these timestamps are either not understood or not fully utilised. There are examples where complexity has led to misleading output, which is perceived as inaccurate or relating to data quality problems. The most prominent example is that a report on staff included recently deceased staff, as their terms and conditions meant that their estates still received payments. Items such as this have led to some loss of confidence in the data in the system, whereas the problem is caused by complexity in the data and business rules. Cross-system Reporting Reports using information from multiple sources are complex and currently require a lot of effort to produce. THAS and Unit Cost reports require data from Banner, Agresso and timetabling (Syllabus Plus). Our study has initiated discussion about whether the THAS report will need to be produced in future. Reports to other bodies such as the CSO require similar cross-system reporting. Consistency of definitions and data standards There are differences in definitions across IoT. For instance there is no consistency in the way that hours for course development are applied, definition of a student, definition of a module etc. The HEA is working with the IoT to improve the definitions of staff and teaching hours for their reports Modularisation is currently implemented in varying degrees, and is likely to be a source of differences in definition in the future. E.g. what is a full-time student on a modular programme? Consideration should also be given to the potential use of interim data definitions for transitional modularisation arrangements and the level to which these may or may not be supported. The differences in definition described above are an issue currently only when reports are used to make a comparison between IoT. In future the differences would cause difficulties when implementing a common integrated reporting system. An opportunity exists to streamline reporting to outside bodies such as CSO and HEA, for instance to standardise on timing and report content. Resources and knowledge within the IoT IoT are heavily dependent on key skills of one or two people for reporting, which creates a significant risk if people move or change role. It was felt that there should be more self-service capability and automation for generating reports. There are different degrees of knowledge and skill in using the systems and reports across IoT. Some IoT are stronger on one system than another, often with the pilot sites having the most skill and confidence. There is only limited sharing of experience or of reports generated in an institute across the sector. 19

20 While the pilot sites have more knowledge of the systems that are implemented, they often felt that they did not receive the latest updates or guidance following roll-out across the sector. Helpdesk support from suppliers received excellent feedback where used. Sites that have this level of support are very reluctant to lose it. A downside to this is that there is no means of tracking to what extent it is used, or sharing knowledge of problems and solutions. 2.4 HEA requirements The HEA s primary means of monitoring sector performance is through Unit Cost analysis, performed using their own SRS system. The HEA takes feeds twice per year from the Institutes for this system. As Unit cost analysis is based on audited accounts, this analysis is typically one year behind the current academic year. Over the coming years it is likely that an increasing proportion of each Institute s funding will come from its success in meeting specific goals. These objectives will be individually agreed in advance with the HEA, and so are likely to vary across the sector. It is not yet clear what form these additional objectives will take and how they will be measured, though IoT should be ready for the scenario where institute indicators are used to measure performance. It is clear that this process will add to the reporting needs of the Institutes, so that they can predict what outcomes are achievable before making a funding request, monitor progress towards the achievement of agreed targets, and demonstrate in a transparent manner that the targets have been met. 2.5 Comparison of IoT Sector Business Intelligence Requirements with UK and US As part of this study, a number of recent Business Intelligence implementations in the UK and US were reviewed for comparison with the Irish Sector. The Higher Education Institutions referenced are the leading published implementations in this area, and so are likely to be among the more advanced in this their use of information. Of the 13 Universities included, there were 8 from the UK with between 3,000 and 26,000 students and 5 State Universities from the USA Business Drivers for BI Initiatives The US and UK Universities reviewed prioritised the following as key business drivers in adopting BI: Reductions in funding driving a requirement for increased efficiencies and the development of alternative revenue sources Lifelong learning - track students over student lifecycle and through their career 20

21 Course development to attract future students Understand the supply and demand for courses Tailor course offerings to meet changing student demand Increasing demands for accountability and productivity Drive to increase academic and student outcomes Process time and cost reduced for report generation and distribution with limited IT resources The graph below consolidates the key business drivers for BI implementations across the Universities analysed. Each may have had more than one priority. BUSINESS DRIVERS FOR BI IMPLEMENTATIONS Time and cost of report production Accountability and productivity Themes Other Lifelong learning Reductions in funding Count The key business drivers identified above are largely consistent with those identified in the IoT Sector, one exception being the much more developed focus on target driven performance management evident in the UK and USA where performance management systems are a core part of their strategy and budgetary development processes. Information Requirements addressed by BI Initiatives The Information Requirements across the BI Implementations analysed focused on areas such as the Student Lifecycle, (recruitment, enrolment, admissions, registration and retention), KPI reporting and external government reporting. UK universities are moving ahead with more integrated data warehouses and executive dashboard initiatives aligned with improving performance management capabilities. These Key Information Requirements from the referenced Universities have been summarised in the chart below. 21

22 INFORMATION REQUIREMENTS FOR BI IMPLEMENTATIONS KPI Reporting Other Category Funding Examination performance Student lifecycle Student preferences Cost and Time Analysis External government Count Solution Focus of BI Initiatives The BI implementations reviewed illustrate an emphasis on achieving an integrated data warehouse supporting good standard reporting capabilities. The solution focus in the larger state universities in the US has been on implementing powerful analytical tools. Focus on cost efficiencies and alternative revenue sources have also been a driver due to reductions in traditional sources of funding. In the UK the need for transparency and accountability has driven the requirement for enhanced KPI reporting capability utilising tools such as executive dashboards. The chart below summarises the Solution focus for the UK and US institutions SOLUTION FOCUS FOR BI IMPLEMENTATION Executive Dashboards BI Component Analytics Integrated Dataw arehouse Standard Reports Count 22

23 2.6 Conclusion The HE2012 study shows that Higher Education Institutions globally are facing a number of pressures that require them to make better use of the information at their disposal. Universities in UK and US are investing in initiatives to improve their access to information. Many of the issues being faced globally are also impacting Institutes of Technology in Ireland, who all see improved access to information as of high importance. In the next section we assess the baseline reporting available to the IoT today. 23

24 3 Baseline Assessment 3.1 Introduction IBM conducted an assessment of the existing reporting systems in the sector in order to provide a baseline for future planning and to highlight existing issues that the BI Strategy aims to resolve. The findings were based on the interviews and survey described in Section 1, as well as meetings with An Chéim and Hewlett Packard (HP). The feedback is described in five sections, which correspond to IBM s standard BI capability assessment framework: Performance Management Functional Management Information Management Technological Infrastructure Organisational Management We finally rated the overall level of Business Intelligence in the sector against the IBM Information Maturity Model. This model and the rating are described in section Baseline Assessment Findings Performance Management Performance Management assesses whether measures and targets are cascaded consistently down the organisation, processes are aligned consistently with business strategy, and information is available to support the measures and is relevant to ensure the right action is taken to achieve the targeted performance. In this section in the context of this study we focus on the need for cross-system reporting. The investment made in Banner, CORE, Agresso, Syllabus Plus and Millenium has enabled a wealth of information to be captured. However, exploiting the strategic value of this information currently requires a significant manual effort. Comments include Substantial time and effort is spent sourcing, correcting, cleansing and manipulating data and producing the final reports The big picture view is not currently available across the silos of data Others comment on the slow and costly collation of data for the purpose of strategic reviews. Monitoring historic trends, for example, exam results by course, teacher, year, are not readily available to inform and monitor progress against targets such as teaching standards. 24

25 The sector lacks an effective performance management discipline, and does not have timely and accurate information currently to monitor performance. The impact is that: - Executive board level and middle management decision making is not supported by qualitative and timely information. Information that is required by external bodies such as the HEA and the CSO frequently has to be collated manually from more than one source. The cost of manually collating data for performance management is high, for example, the increasing requests from external bodies are being addressed in an ad hoc way. IoT are unable to respond effectively during periods of high activity, for example, there are resource bottlenecks during the enrolment and exam periods. The impact of improvement initiatives funded by the HEA can not be monitored effectively in their impact upon strategic objectives and attainment of strategic targets Functional Management Functional Management considers the core functional systems in their role providing information to the relevant decision makers and information users on a just-in-time basis. The information should possess the attributes of accuracy, relevance, usefulness and timeliness. In the context of this study we are use this section to describe how well each major system is able to provide information in its functional area Student Management Functions While the implementation of Banner across the sector has enabled a richness of student information to be captured some IoT are concerned at the level and complexity of process overhead required to effectively extract and use the data available. The Banner repository is not readily accessible for reporting and analysis, and a reporting front-end for Banner has yet to be implemented. Significant time is invested by IoT extracting the data from the complex underlying Banner data structures. For some IoT, this has required the employment of a specialist resource. In addition the Banner production environment is experiencing an increasing number of queries run against its operational tables, some requiring joins across multiple tables, resulting in lower performance levels particularly at peak periods. Banner data is operational in nature and all data is time-stamped. In theory this should allow historical and trend analysis. However the common perception in the IoT, whether because of complexity or lack of understanding, is that historical analysis is not possible. 25

26 The completeness of the student profile captured in Banner and the software modules used vary across the sector. In some instances this leads to centrally created reports being incomplete or not working. At present potential key future areas of data are not currently captured in Banner, e.g. Alumni data. This requirement has been identified and projects are underway to address this. The Common Standard Design (CSD) provides consistent Banner processes for data entry across the IoT. The potential benefits associated with this consistent process however are eroded because different data definitions for core entities can be applied across the sector e.g. definition of full time and part time student. In addition to the above we identified that Student Services require a range of reports and a repository for access to confidential student information. A number of projects have recently been successfully piloted on the potential for using Oracle Discoverer 10g to address reporting requirements for Banner Timetable Management Function Syllabus Plus provides a centralised repository for timetabling based on a common design, holding both activities and resource data. Not all IoT currently use Syllabus Plus, and there is a range in the sophistication of IoT in their use of it. Most currently replicate previous timetables and adapt to the current year. Further timetable adjustments, which may be continuous over a semester, are not always updated to Syllabus Plus. Syllabus Plus therefore captures snapshots of planned timetables valid at a certain points in time. Syllabus Plus is generally viewed as a flexible system which is not currently exploited to its full potential. For example, cost analysis could be delivered through interfaces with Banner and CORE, however, these interfaces do not currently exist. Users require additional standard reports, however data can easily be accessed through a well documented set of APIs and future versions of the package includes a BI capability for reporting and analysis Financial Function As part of the CSD there is a common chart of accounts, with consistent account groups, and flexible range of detail accounts within each group implemented across the IoT. The Agresso application is based on a flexible number of attributes. Primary attributes, e.g. account/sub-account/company are commonly defined across IoT whereas user defined flexi fields are used by IoT to capture additional attributes specific to their needs. Agresso provides two reporting and analysis mechanisms. The browser interface includes basic ad-hoc querying of data, slice and dice, drilling to the appropriate level of summarisation and traffic lighting. The browser also supports federated queries across multiple data tables, templates for reuse, run time variables and output in multiple formats, including MS Excel. 26

27 The Excelerator interface is a more sophisticated reporting tool which provides direct integration from Agresso into Microsoft Excel as well as MS Word, more complex ad hoc reports and the automatic generation of a reports, e.g. for regular management report packs. In many cases, information is extracted to Excel for further analysis. The bulk of the customisation for Agresso has related to the development of reports. 70% of these reports are common across IoT. Users expressed some concern on the user friendliness of the reporting tools HR Function CORE includes a common configuration across the IoT for each module, including payroll, expenses, time and attendance, etc. There are complexities in business rules around headcount, for example a single post may be made up of several resources. Standard reports are available within each module based on Oracle forms. In addition, the CORE BI suite has been implemented for the IoT. This involves Collections of views on top of transactional schema An Oracle based interface, though the repository is open to alternative BI tools IoT prefer to be self-sufficient in developing their own reports, however CORE provide a service to develop the more complex reports. There is generally good feedback from IoT regarding CORE, however integration with other systems is seen as a weakness. Not all IoT are at the same level in terms of reporting from CORE Information Management Information Management assesses whether BI applications and data are organised around the organisation s strategy, key performance metrics, and core processes. Based on their roles, people and groups should be able to easily access key information specific to their role that will aid in decision-making and understanding. Integrated BI systems are integrated into business processes and systems to provide an effective feedback loop. Data is organised by subject matter according to applications aligned with functional silos, e.g. Student Banner, Finance Agresso, CORE HR, each with a common framework for configuration. Each application provides a report generator to support operational requirements, and the CORE BI suite has been implemented in addition to its core operational reporting features. However, there is no common glossary of terms and there is generally a lack of consistent data definitions and business rules across IoT resulting in separate versions of the truth across the sector. For example, a given IoT may follow similar processes for registering students on Banner, but will interpret attributes common to students across the sector in different ways. There are however 27

28 examples with some applications, such as Agresso, where a range of core attributes, e.g. general ledger codes, are maintained as a common standard across the sector, and a range of additional attributes have been further defined for specific IoT analysis in a controlled way. The context in which information is used across the sector varies greatly. Simple definitions, for example what is a student vary widely from one IoT to another and again when compared to what the HEA defines as a student. The IoT are also faced with reporting information to different external bodies (HEA and CAO) with conflicting definitions of key attributes such as full-time and part-time student. Ownership of definitions has tended to exist only for the duration of strategic projects such as implementation of core applications. Post implementation definition changes are driven on a project by project basis, e.g. modularisation. Few staff are allocated to data standards, rules management and definition outside of core project work. There are no active set of standards activities ongoing and few rules are written down for re-use outside of project documentation for the common standard design. In general terms, high quality data exhibits the following characteristics: It is accurate: the data captured is correct and can be trusted It is consistent: the data captured in the same way for all records by all users / applications It is standardised: pre-defined sets of values are used rather than free text fields It is complete: all required fields have been identified and correctly populated e.g. do not have default dummy values It is unique: multiple records do not exist for the same uniquely identifiable element It is current: the data captured accurately reflects the current status of the record as element progresses through its natural lifecycle e.g. student application submission, acceptance, and enrolment. There is a variation in the quality of information output across the sector owing to a lack of automation with significant manual processing of data, and a lack of data quality management processes. There are few standards for the management of data quality across the sector and poor awareness of the impact of data quality. Data quality issues tend to be discovered at exception time, e.g. on submission of data to the HEA. There is a lack of clearly communicated roles and responsibilities, e.g. data stewardship at IoT and sector level, with no budget or staff monitoring data quality or following established processes for auditing data Technology Infrastructure Technology Infrastructure assesses existing technology and tools in their ability to establish a standardised platform for integrated BI application development, implementation and management The sector recognises the value in an integrated architecture that links data, technology, tools and processes to present meaningful information to all users, however there are significant gaps in capability. 28

29 In line with wider Higher Education trends, there has been an increase in institutional, department and student data but it is poorly integrated and is primarily focused on providing reports to a limited set of internal administrators. There is no sector standard tool for reporting and BI, however there are a range of tools being supported for each functional application, e.g. Excelerator and Oracle Forms, and a growing proliferation of tools across the IoT outside of the Common Standard Design, e.g. Oracle Discoverer and Microsoft Access for reporting student data from Banner. There is wide use of SQL query tools with a large number of queries being run against the production environment, particularly during seasonal peaks. This can cause high levels of network traffic across a limited network bandwidth. There are no shared tools for data modelling or meta data management, or any data quality management tools. There are no Integration tools (EAI or ETL) for data collection, transformation, and integration, or tools to support business rule implementation to derive metrics and complex calculated attributes. Data usage is being monitored on a case basis driven by business need with the development of new data usage monitoring processes. The current database environment for operational systems consists of database instances partitioned for each IoT but managed through a central environment, for example central administration of security. Analysis of Technology Infrastructure against IBM s BI Reference Architecture The sector s existing BI Architectural Assets have been listed in the tables below. The tables are organised by layers of the architecture, which are shown as the columns in the diagram below, moving from left to right. To consider the sector s existing BI architecture assets, we have compared them against the IBM Business Intelligence Reference Architecture. This reference architecture places the primary elements of a Business Intelligence architecture into layers (shown as columns in the diagram below). By using the reference architecture as a model, it is possible to compare the existing assets against common and leading practice in the BI industry. 29

30 In the following analysis, each layer is considered in turn, from left (data sources) to right (user access). BI reference architecture high level Data Sources Data Integration Data Repositories Analytics Access Extraction Collaboration Enterprise Unstructured Inform ation al External Transformation Load / Apply Synchronization Transport / Messaging Information Integrity Data Quality Balance & Controls S t a g I n g A r e a s DW ODS D a t a M a r t s Business Applications Query & Reporting Data Mining Modeling Scorecard Visualization Embedded Analytics Web Browser Portals Devices Web Services Extract, transform, load, feedback and workflow Metadata Security and Data Privacy Systems Management & Administration Network Connectivity, Protocols & Access Middleware Hardware & Software Platform s Data governance Figure 1. IBM Business Intelligence Reference Architecture Data Sources Layer The data sources layer corresponds to the operational systems in the sector (Banner, Agresso, Core, Millennium and Syllabus Plus). It may also include external sources of data. Data Integration Layer The data integration layer covers the functions required to gather data from operational systems, and reformat the data and load them into any subsequent databases. Component Component Description Existing or Planned ETL Synchronisation ETL is the process of converting the information on source files into a consistent, clean format in the Data Warehouse. Synchronisation tools are used for replicating data between environments The sector currently has no ETL tools. The sector has the Oracle Data Guard utility available for data replication. Information Integrity Information Integrity component includes tools for analysing and monitoring data quality No standard tools are available. Adhoc queries can be developed using Oracle toolset but these are manually intensive. 30

31 Data Repositories Data Repositories are the data stores that are used for analytical reporting. Component Component Description Existing or Planned Data Repositories Layer Data Repositories contains the atomic and/or summary levels of Student, Institute, Lecturer, Financial Management, Human Resource details. The data is organized to support various analytical processing applications. Banner No analytic repository for student information with current installation, however, Banner ODS and EDW repositories are available from SunGuard. Agresso v 5.4 Analytic tables are available with the current version but not implemented. These load balance table snapshots into a data repository for comparative analysis. Syllabus Plus v3.5 No analytic repository available, however, v3.6 Enterprise Edition provides repository for exploration of information on timetables, activities and resource utlisation. Core BI Suite is implemented including repository of payroll, expense, time and attendance, etc summary data. Analytics Layer The analytics layer describes the tools that are used to develop reports or by end users to access and analyse the data Component Component Description Existing or Planned Analytics Layer The Analytics Layer provides the business analytic applications and their underlying capabilities and services. No Sector standard reporting and analytics tool. Limited cross functional use of Oracle Discovery with 3 IoT. Core BI Suite provides Oracle Discovery web interface. Agresso includes Browser and Excelerator reporting tool. Core and Banner include Oracle Forms for operational reports. Excel spreadsheet and Access widely used across sector. 31

32 Access layer The access layer is the means through which users access the analytical tools. Component Component Description Existing or Planned Access Layer The user interface Web Browsers are widely used across the sector for accessing standard operational reports and report generators, e.g. Agresso, Core. The Luminous portal product is being piloted as a student portal, but may be available for internal information consumers. Common Layers Common layers are the under-pinning of the solution and apply across the architecture. Component Component Description Existing or Planned Metadata Security and Privacy Systems Management & Administration Network, Middleware, Hardware and Management Software Business, technical and operational information needed to use, access, develop and operate the environment Needed at every layer to protect the organization s information Policies, procedures, processes, standards, guidelines for managing data, technology, processes Includes performance monitoring, backup/recovery, version management, change control, problem management, software distribution, data archival Standards and protocols for connection, transportation & delivery of multi-platform solutions Must be scalable, reliable, efficient No glossary of business terms. No metadata beyond source system level. Oracle database security and privacy for production repositories. No single sign-on security model. Current BI assets, e.g. Core BI suite share these components with other applications within the environments supported. Current BI assets, e.g. Core BI suite share these components with other applications within the environment supported. Note that hardware and software is not shown as the strategy is independent of the hardware used in the sector. 32

33 3.2.5 Organisational Management Organisational Management assesses whether organisational elements are working together to build and sustain an environment of gathering, accessing and acting on business intelligence. During this transition period to the HEA, the sector clearly lacks coherent and clear structures to facilitate the cascade of business intelligence through welldefined roles, clear reporting lines and the link to business performance. This will clearly represent an inhibitor to the success of any overall business intelligence and performance management strategy for the sector. Ownership of business intelligence has been distributed and informal across the IoT with some resistance on cross IoT collaboration for competitive reasons. There is an overwhelming reliance on a key set of individuals within each IoT and a general lack of knowledge and skill levels in the use of analytical tools, e.g. poor appreciation of more sophisticated tools such as Data Mining. Business intelligence assets are also closely aligned with department silos and not viewed as enterprise systems in line with most other Higher Education institutions. Collaboration, for example the use of communities of interest, exist but tend to be aligned to project-specific goals rather than ongoing organisational initiatives such as knowledge sharing or monitoring and improving data quality on an ongoing basis. Effective networks are in place across IoT for Syllabus Plus and CORE. Some IoT are frustrated with the need to gain a sector consensus on reports before these will be provided centrally by An Chéim. In circumstances where certain reports are viewed as a priority by those IoT, they have sought their own solution. There are further signs of IoT increasing direct interaction with application vendors, with less dependence on the An Chéim central support organisation Training on existing reporting tools tends to be tool focused and lacks the business context of using it. 3.3 IBM Information Maturity Model The IBM Information Maturity Model was used to assess the current baseline in the sector. This model is used globally by IBM as a means of determining how effectively businesses in various industries manage and make use of the information they collect, and to indicate how sophisticated the maturity needs to become to help organisations meet their strategic goals. The model has five levels, from 1 (lowest) to 5 (highest). These are shown with descriptions in the chart below. The line between levels 2 and 3 indicates that this is the point at which integrated information is made available to the organisation. It is a significant step to achieve this level. 33

34 The diagram 6 below outlines the Information Maturity Matrix. On a scale of 1 (lowest) to 5 (highest), we rate the information maturity of the IoT at Level 1, primarily due to the fragmented nature of reporting and the difficulty in meeting business needs for information. We rate the UK Universities we reviewed at a Level 2 maturity, and the US State Universities at or close to Level US State Unis 2 Leading UK Unis 1 IOTs Source: Review of recent published HE Sector BI implementations 6 Please note that a large format version of this chart is available in the appendix of this report. 34

35 3.4 Conclusion Based on the requirements identified in our interviews and analysis of similar higher education sectors internationally, we judge that over the six years of this strategy the IoT need to move to a maturity level 3. At level 3, integrated information with agreed and consistent definitions will be available across the organisation. Features of Levels 4 and 5 in this model would include features such as the provision of real-time information fully integrated into operational processes (such as in a call-centre). We do not believe this level of development is required for the IoT sector. As indicated above, the jump from level 2 to level 3 maturity is significant, so we do not recommend moving from Level 1 to Level 3 in one step. This is why in the later stages of this report we recommend the strategy be implemented in a series of two-year waves, aiming to achieve level 3 maturity at the end of wave 2. Section 4 considers a solution that builds upon the existing systems and will deliver the reporting requirements and help the sector achieve maturity model level 3. 35

36 4 Future State of Business Intelligence 4.1 Introduction The purpose of this section is to describe the future state that will exist following implementation of the Business intelligence Strategy. This is described as a blueprint, followed by recommendations covering the main architectural choices that need to be made. This section starts by describing the features necessary for the overall Business Intelligence Blueprint, and solution that will be provided once the Business Intelligence strategy is implemented. Eight major capabilities are described that need to be provided by the solution, and a number of key challenges and enablers are also described. These capabilities are used to determine the overall blueprint, which is the nature of the overall solution to be provided. Following the description of the overall solution, the technical alternatives in delivering it are outlined. The two alternatives considered were a centralised data warehouse and a distributed data warehouse, and we explain why we recommend the distributed warehouse option. Having selected the distributed warehouse option, we consider the individual elements that will make up that architecture, at a non-specified level (i.e. not product specific). We recommend a subset of the architecture to be implemented in the first wave, that will provide improved student reporting and lay the foundation for subsequent waves. Finally we consider some key elements of the implementation process. These are sector alignment and adoption, which describes the roles of An Chéim and the HEA and a set of BI guiding principles; provision of a BI Centre of Competency to help coordinate implementation and enhance exploitation of the reporting system; data governance and quality, as disciplines to ensure the data is properly managed. 4.2 Key features of the BI Blueprint What the strategy will deliver The Business Intelligence Blueprint will provide a solution that meets the reporting needs of the sector. As the blueprint looks out beyond the immediate stated needs of the sector, it needs to take into account the impact of emerging business challenges such as those described in the HE2102 study. Based on the findings from the Baseline Assessment above, IBM considers that the following eight capabilities need to be enabled by the BI Strategy: 36

37 1. Improved reporting on student There is an urgent requirement to improve the ability to provide reports on students. Initially this can be considered an operational reporting need consisting largely of lists and counts of particular groups (e.g. during registration period) but this also includes analytical reporting. As student information is at the core of what the institutes do, improved access to student information will be the core of any improved flexible reporting environment covering multiple systems. 2. Improved cross-system reporting The ability to generate accurate information from more than one system (e.g. Banner, Core, Agresso) more quickly and with less effort is a current need to support Unit Cost Analysis and similar analysis. Based on stated requirements and knowledge of the sector, it is clear that the need for crosssystem reporting will grow over the coming years, though the majority of information needs are still likely be from single systems. It will need to be possible to add data from other sources, in order to support external data or use of different operational systems in IoT. 3. Widespread access to reporting suited to users The information within the sector needs to be made available easily and in a timely manner to different groups of users in ways that are appropriate to that community. Access should be web-based, providing graphical and text information, and ideally with a single-sign on to reports. Access to data will be governed by user profiles to ensure that data security is maintained. Examples of the types of access will include: Senior managers and most teaching staff need simple access to standard reports, with flexibility for selection and drill-down, to include a dashboard showing Key Performance Indicators. Staff users will need more flexible access with ability to create and share their own reports There will be a small number of power users with the ability to produce complex reports or answers to ad-hoc queries. Providing ready access to the types of information and reports described above will be a key enabler in promoting a step change in the IoT ability to adopt leading management practices. 4. Improved data standards and quality The sector needs to improve its understanding of data in various ways in order to gain the benefits from the BI Strategy and to enable the cross-system reporting required, and where appropriate make improvements to data quality within the operational systems. The factors necessary to achieve this will include: The development of a clear understanding and definition of what data quality means within the sector Commitment to data quality by IoT down to operational level ensuring that all end users understand the importance of data quality at source and are encouraged to deliver to agreed standards Data standards stewardship & ownership 37

38 Improved accuracy and completeness of information within the operational systems Improving understanding of the underlying data, so that users can make correct interpretation of the information provided, or define query to achieve results required. A glossary is one means of meeting this need. The data definitions and coding needs to be more consistent across the sector, in order to allow creation of a common reporting platform. This should not reduce the flexibility for IoT to operate differently where appropriate, but encourage those working in a similar way to follow the same standards. As the HEA are in the process of defining what information they will need from the sector, there is an opportunity to work with HEA to agree the specific content to be provided. Taking advantage of this opportunity could help ensure that the information provided is both relatively simple to produce and meaningful to the sector for its own use. 5. Agreed Key Performance Indicators (KPIs) In addition to defining what reports are required, there is an opportunity for the IoT to define what Key Performance Indicators they will track to monitor progress against their business objectives. These typically take the form of indicators to be tracked (e.g. progression), and measures that will be used (e.g. percentage of students that pass exams on schedule and continue on the course). Defining these at the outset of the BI Strategy will allow the most important measures to be included in the design, and reduce the likelihood for additional effort later. Agreeing a standard set of KPIs and measures will simplify the overall design. There will be the ability for IoT to tailor the KPIs and measures or to add their own, as it is recognised that the IoT will not all have the same objectives and priorities, and that in any case the KPIs are likely to change over time, Dependent on the indicators and the timing, the results of the KPIs can be presented in reports or management dashboards that provide managers with visibility of the most important information, typically in a graphical format that can highlight whether targets are being achieved. 6. Simplified external reporting requirements The BI Strategy should support reporting for external stakeholders, such as the HEA, CSO and other bodies, and reduce the effort required to provide information to these stakeholders. In the timescales of this strategy, there is an opportunity to encourage the external bodies to coordinate their needs for information, potentially reducing the overall work required to provide it. For instance external stakeholders could coordinate the items of information required, as well as the the time periods covered. The external content can also be linked to the definition of KPIs, so that the IoT focus on the same items that they are measured on. 7. Flexible architecture and technology The BI strategy will require an overall architecture that is robust and flexible enough to provide a common reporting capability while also supporting the needs of the individual institutes. As well as the need to support requirements that are currently anticipated, flexibility is needed to allow 38

39 individual IoT to produce different or additional reports, on top of a common base. Given the six year duration of this strategy and the on-going life of the system beyond that point, there will need to be flexibility to allow for changes to the operational systems, including upgrades, functional changes by the vendors, and even possibly changes to the selected source applications. 8. Improved support model Specialist support to the sector will need to be provided to ensure the efficient development of the BI capabilities needed across the institutes, and also to enable individual institutes to enhance reporting if they have additional needs Key Challenges Implementing a Business Intelligence strategy in any organisation has a number of hurdles to be overcome. In the IoT sector there are some specific hurdles that need to be considered when planning the future state. 1. Maintaining clear business sponsorship In IBM s experience and that of other service providers in the BI area, one of the most important factors in achieving a successful Business Intelligence solution is maintaining clear and focused business sponsorship. This is necessary to drive forward the project and also encourage the business to stop using previous methods or make necessary changes for the implementation to work. Given the multi-organisational nature of the sector, is will be particularly important to have a clear vision of the desired outcome, pro-active involvement of senior management and other key stakeholders and clear definition of overall governance and sponsorship. To ensure sufficient engagement and sponsorship, IBM recommends a strong programme management function. One of the roles of this function will be to manage coordination with the senior management of the IoT, and to promote and monitor engagement of all stakeholders. 2. Variation in use of modules and data Institutes currently use different software modules within their operational systems, and there is no register of data definitions or coding that each institute has implemented. Some of the differences in coding are due to differences in business model (e.g. degree of modularisation, use of numeric or alphabetical exam results) and the system should continue to support this, but it is our view that there is further potential for increased consistency. The differing use of software modules was explored in the April 2007 report The level of Exploitation of the An Chéim MIS Suite in Institutes of Higher Education. 39

40 To resolve this, IBM has proposed focus on data standards and on-going data governance, in order to improve coordination of data standards. 3. No established BI solution for the sector The sector uses systems from a number of suppliers, rather than from one individual organisation. Following discussion with the higher education sector application vendors and BI tool vendors, it is clear that there is currently no off-the-shelf BI solution in the sector that supports information across the systems used in the IoT Sector. A suitable solution architecture to handle this is discussed later in this section. 4. IoT Involvement and Effective Decision Making Implementing the BI strategy will require active involvement from key stakeholders across the IoT. These stakeholders are invariably busy people and a key challenge will be in establishing approaches to involving them that minimise the impact on their time and on the on-going work of the IoT while at the same time maximising the benefit of their involvement. The approach adopted will also need to clearly define ownership and decision making responsibilities for the BI implementation. We believe that the most effective approach would involve the establishment of a national BI implementation committee, drawing a representative membership from across the IoT with delegated decision making authority. This body would be responsible for strategic, technical and data decisions related to the implementation of the BI strategy. Agreeing the membership and governance of this body will be a key early step in the programme, and monitoring whether it works effectively will be one of the main responsibilities of the programme management function Enablers While there are challenges specific to the IoT sector, there are also some positive factors that increase the likelihood of success 1. Common Standard Design (CSD) While some of the data standards may have inconsistencies, the Common Standard Design provides a basis for developing a common reporting platform across the sector. 2. Economies of scale The IoT sector has an opportunity to develop a high quality solution for all the IoT that would not be possible if funded and delivered by individual institutes. The potential availability of SIF funding on a sector wide basis would further support this. 3. Common view of the problem IoT across the sector are at differing stages in the addressing their BI requirements. There is, however a high degree of consistency in the sector s description of the importance and nature of its information requirements, 40

41 demonstrated through the interviews and survey. This would indicate that there is significant potential for the appropriate solution to be of value across the IoT. 4. Good knowledge of systems There are individuals around the IoT sector that have considerable knowledge of aspects of the underlying systems and data. In so far as is possible, this knowledge should be leveraged to support a programme for the benefit of the sector as a whole. 4.3 Target State BI Blueprint for the Sector Functions provided in the BI Blueprint Our recommended blueprint for BI in the IoT Sector is a fully integrated environment that provides the eight capabilities described above. BI capabilities include a broad array of analytical tools and techniques oriented and adapted to the particular BI user profiles and supported by a data infrastructure responsible for the provision of consistent and reliable data. Given the particular importance of student information to the sector, we have shown the functional priorities as two blocks: Student Insight and Business Operations. Achievement of this blueprint will require commitment, engagement and adoption of BI with senior sector level sponsorship, and the establishment of the appropriate organisational structures and processes. This framework is depicted below: Sector Alignment & Adoption BI is a part of the organisational culture and is sustained by sector and institute commitment, engagement and adoption of BI capabilities. BI is championed by senior management and adopted at every level to ensure organisational alignment. Business Insight An environment in which users can apply a variety of analytical techniques against high quality integrated information to develop insights that can be leveraged throughout the sector and institutes to make informed strategic and tactical business decisions. KPIs, metrics, dashboards and reports are used to measure institute, operational and employee performance and drive accountability for results. Each level of the organisation understands how it impacts institute and sector performance and results. Established organisational structures and processes to ensure a valuebased, business-driven build out of BI capabilities. Data Architecture Student Insight Using BI to understand to develop deeper insights into the current and potential students values and needs and to develop services to satisfy them Business Operations Using BI for to improve the management of business operations, e.g. to identify time and cost savings through eliminating courses with declining interest, developing new courses and aligning staff skills and knowledge. A set of enabling technologies that collects disparate data, integrates that data into usable structures, and then presents a more useful representation of the data within views that are intelligent and fact based. 41

42 4.3.2 Major components to be included BI Blueprint The major functions required by the sector are broken down into components on the framework below. This illustrates the: Major information categories required by the sector across the student lifecycle and cross system requirements such as Unit Costing required for business operational improvements Components of the BI user toolset for exploiting this information, from standard reporting to executive dashboards Components of the enabling data infrastructure from data acquisition through to data delivery Major groupings of activities critical to the alignment, adoption and governance of BI for the sector such as Data Stewardship and Change Management Sector Alignment & Adoption Strategy & Planning Business Insight Change Management BI Governance Executive Dashboard OLAP Data Mining Self Service Reports Standard Reporting BI Program Management Learning & Competency Development Data Architecture Student Insight Enrollment Admissions Registration Retention Business Operations Unit Costs Staff Timetabling Funding Examination Budgeting and Planning Data Stewardship Data Sources Data Integration Data Repositories Analytics Access 4.4 Target State Data Architecture Architecture overview A key part of any BI Strategy is the architecture, as this defines what outcome will be achieved at the end of the series of projects that deliver the strategy. The architecture provides technical direction for all the projects, and allows individual projects to deliver components that support the whole. As described above, there needs to be a reporting architecture that can support the major needs of the sector and support the eight capabilities described earlier. The architecture needs to: Provide functionality to combine data across the source systems and deliver reports to the various user communities as and when they need them 42

43 Support improved reporting from each operational system, and particularly students Be sufficiently flexible to meet needs of institutes and life-cycle changes to source systems Unfortunately there is no available off-the-shelf reporting solution that supports the systems or data models that are implemented in the sector. Individual tools, such as Oracle Discoverer, can access data within Banner or the other systems, but they access the tables in the systems directly they do not provide users with a clearly defined and meaningful set of information definitions out of the box. As there is no available solution, it is necessary to define an architecture that meets these needs making best use of available components. The approach to defining the architecture is to start with a high-level view describing the concept that will be followed, progress into the key decisions on structure, and then investigate the major components to be incorporated into the design. The generally recommended high-level architecture for Business Intelligence across all industries follows the schematic below, which is taken from the IBM HE2012 white paper. This schematic provides for consolidation of information through a data warehouse layer and provision to users through analytical and data mining tools. The data warehouse layer is necessary to provide the crosssystem reporting capability. At this point, the nature of the data warehouse is not decided, and it may or may not require a physical store containing all relevant history. In the following parts of this section we consider two architectural alternatives that fit this schematic, in order to determine which is most appropriate to the IoT Sector. Test Scores Records Student Data Data Transmission Financial HR Timetable Library Data Transformation Institution Data Warehouse Internal data External Data Analytic/Data Mining Tools Integration to Other Systems Input of Student, Financial, HR & Library Data Institutions often provide access to data via manual tools, like a portal. In the future, access and delivery of data will be automated. Transformation of data Data is transformed to common standards reflecting the needs of the organization for analysis and reporting Institution Data Warehouse The data needs to be loaded into a robust data model that will support a broadrange of analytics 4 Analytic Tools An analysis tool to interrogate the data and find key business issues and insights helps administrators to make educated decisions and drive innovation 5 Integration to Other Systems Many applications can leverage other data, including financial planning, and demand planning 43

44 4.4.2 Architectural Options Organisations across the Higher Education sector and in other industries have implemented a range of architectures to achieve their data warehousing goals. These are broadly categorised into 4 types of architecture as illustrated below: - The Centralised and Distributed approaches are considered the primary options for meeting the IoT sector s business intelligence needs and are described in subsequent sections. The centralised approach involves bringing data from all operational sources into a shared data warehouse that is accessed for all reports. The distributed approach has a series of databases that are used for different purposes. For the IoT sector, these databases would be aligned to the operational sources, but not combined into one. There would be an additional federation layer beyond the distributed databases to allow analysis of data across source systems. Federation technology allows a user to access the data in multiple databases as if it were in a single store. The hub-and-spoke and federated approaches can be considered variations of these, and so are not separately discussed in this paper. For clarification, the hub-and-spoke model has additional data marts which receive data from the data warehouse, and are used directly by the users. This variation can provide greater performance and usability where there are large volumes of data or different types of analysis required. The federated approach here uses federation technology to allow users to access source data directly, as if it were in a shared data warehouse. This can be useful for occasional reporting, but it can cause performance issues with the source systems. Consideration of each architectural option has included analysis of: - Variation in Source data standardisation across source systems, taking into account lack of consistent data definitions and business rules Variation in Source system data quality, taking into account concerns over the completeness, accuracy, integrity etc of source data across the sector 44

45 Balance between central control and IoT flexibility to provide economies of scale whilst satisfying the needs of individual IoT Level of integration to enable cross-system analysis Use of existing assets and other industry assets available to accelerate deployment Data security at the IoT level and authorisation levels for different user roles Cost of ownership taking into account acquisition, installation and integration costs, training and support costs, and apportioned across IoT Performance to support concurrent users during peak times and scalability to support data volume growth over 6 year period Latency, granularity and time variance of data to meet operational and analytical user requirements Variation in user categories, including 1) Standard Reporting via web for maximum of 3000 users 2) Analytics for maximum of 150 users 3) Dashboards for executive users Conformity with industry and enterprise standards for interoperability, communications and networking, data management and transactions Incremental build over a 6 year period with regular benefit releases, including phasing options by IoT and functional area Extensibility to meet evolving sector needs Independent design from source systems data providers Option 1: Centralised approach With the centralised approach, all the data is stored in a central environment, under central management. Centralisation does not necessarily imply that all the data is in one physical location or in one common systems environment. That is, it may be logically centralised rather than physically centralised. This approach was considered for the sector with a central data warehouse repository supporting analytical reporting and analysis needs. This approach would require a common data integration layer across source systems as illustrated below: - 45

46 Sector logical data architecture option 1 Central Data Warehouse PROD PROD CORE HR Web Agresso Finance Banner Student Millennium ETL and Integration Data Data Warehouse Warehouse Analytics Query Report OLAP Other Graphics Dashboard Self-Service Reporting Syllabus Plus Data Sources Data Integration Data Repositories Analytics Access The layers in this diagram correspond to the IBM BI Reference Architecture shown in Section 3. In this approach, data from the source systems is combined using Extract, Transform and Load (ETL) processes, and stored in a data warehouse that is then used for all reporting. A variety of analytics tools can be used such as Oracle Discoverer, Business Objects or Cognos Reports, and users will access the information using web technology. The primary areas of difference between this and the decentralised approach are the merging of data in the data integration layer and the creation of the central data warehouse, and the The benefits of this approach include: Increased query performance as complex transformation and aggregation will be handled in the data integration layer therefore reducing complexity of the reporting layer Greater control over metadata, i.e. business rules and definitions with less fragmentation, enabling easier partition between common and specific rules and definitions to support an appropriate balance between the Common Standard Design and local IoT flexibility A single integrated platform for BI would be easier to support and maintain as part of a common standard design across the sector in the long term, help to drive conformance with standards and would be extensible to support meet sector needs A central data warehousing approach, however, requires significant early investment in defining common data definitions, business rules and mapping source data to new target structures. In the IoT sector this is made more difficult due to the best of breed approach taken for the operational systems environment. There are no packaged solutions available that could be used to help define this solution, so a bespoke model would be required, which would be costly and take considerable time to produce. 46

47 4.4.4 Option 2: Distributed approach The distributed approach to data warehousing shown above is an alternative option for the sector s target BI architecture. Here, the data warehouse can reside in multiple hardware and software environments. The multiple instances would need to conform to the same data model, and be managed as a single entity. The end user would access the data warehouse through a user metadata layer which would conceal any underlying complexities inherent from retrieving the data from multiple database instances. A distributed logical data architecture for the sector is illustrated below: Sector logical data architecture option 2 Distributed Data Repositories PROD PROD CORE HR ETL HR HR Mart Mart Web Agresso Finance Banner Millennium Syllabus Plus ETL ETL ETL ETL Fin Fin Mart Mart Student Student Mart Mart Millennium Millennium Mart Mart SchedPlus SchedPlus Mart Mart Analytics Query Report OLAP Other Crosssystem Analytics U.Cost Cube Federation Layer Graphics Dashboard Self-Service Reporting Data Sources Data Integration Data Repositories Analytics Access In this option, the end-user access is similar to that proposed in the first option. The main difference is that instead of a data warehouse combining data into one place, a separate smaller database (or data mart ) is used for data from each source system. The database would ideally be that provided by the vendor 47

48 (subject to due diligence that the solution is sufficiently capable), which means that it would be significantly simpler and quicker to set up. For cross-system reporting, a data federation tool is used to combine the data from the data marts. Tools exists to do this, though mapping the data will need to be set up. This will be less effort than mapping the source systems because The federated data will cover fewer data items than the ETL layer in the data warehouse approach The data marts will be better understood and documented, as they are already set up as reporting environments The major benefits of this option would come from: Acceleration of the design and implementation through the use of available and existing BI assets from the sector s strategic vendors, for example CORE s Business Intelligence Suite is already implemented and available to the sector. Each of SunGard, Agresso and Scientia have BI suites designed specifically for extracting the operational data and restructuring it for the ease of reporting and analytics. Reduced data integration effort, as each packaged solution includes the extraction and transformation of data into the analytical repository. The federation layer will integrate data from a defined structure (the vendors provided reporting databases). It should be highlighted, however, that the metadata, i.e. business rules and definitions would be dispersed across each functional data repository and data integration layer and the CSD would need an overall framework for managing this. A further consideration in this distributed approach is that there is no common layer in the architecture for standardising source data or addressing data quality issues. It is, however recommended that these are addressed at the operational system level as part of a data quality and governance initiative which will precede the development of the data warehouse. There will also be a significant dependence on the packaged vendors as part of this approach, although this will be an extension of existing relationships. There are inherent advantages, for example leveraging new functionality offered as part of package upgrades, which can be balanced against the potential impact of database changes on the overall integrity of the data warehouse environment. Each vendor BI suite will undergo a prototype to assess its functional and technical fit, and a controlled path for future upgrades to the database will need to be agreed Logical Architecture recommendation The central data warehouse approach is common in many industries where there are many operational systems containing similar data, and where there is an operational need for information integrated across sources. However as described above, it can take considerable time and effort to create such a system. A packaged data warehouse would be ideal for the central data warehouse approach as would contain pre-built components and data model that could reduce the effort involved and accelerate the development cycle. However the market is currently immature and the packaged choice is very limited. IBM have 48

49 reviewed this possibility with the major Higher Education sector application providers (including those represented in the IoT sector and others), as well as with the major reporting vendors, and none has an appropriate BI solution for the HE sector. One key consideration is the volume of cross functional domain queries that would be required to support reporting and analysis, as there is likely to be reduced performance on queries which join across the separate functional data repositories. Based on the interviews and survey, the majority of reports are likely to continue to be based on a single operational source, with a smaller though more complex set of requirements for cross system data. Where large cross functional queries are identified as part of the requirements and design implementation phase, there are a range of design options which can address the performance implications which may include: developing additional data marts or OLAP cubes loaded according to an agreed schedule assigning these reports to agreed schedules rather than allowing them to be run on an ad-hoc basis Based on the costs, complexity and implementation timescales of the centralised approach and the lack of a packaged solution, and in the knowledge that the majority of reporting will remain specific to single source, IBM recommends the distributed architecture approach for the sector. 4.5 Target State Data Architecture Components Overview Having selected a distributed approach for the appropriate overall architecture, the next stage is to consider the components that make up that architecture. The target state data architecture is made up of a number of enabling technologies that collects disparate data, cleanses and integrates that data into usable structures, and then presents a more useful representation of the data within views that are intelligent and fact based. These technologies are categorized into a number of layers according to the IBM Business Reference Architecture: - Data sources Data Integration Data Repositories Analysis and User Access (discussion of these is combined) Logical components for each layer of the sector s target state data architecture are described in this section Data Sources and data quality The data sources are the five standard systems used in the IoT, including Banner, Agresso, Core, Millennium and in many IoT Syllabus Plus. 49

50 The ultimate success of next layer, data integration or consolidation, ends with the delivery of reliable, relevant and complete information to end users. However, it starts with understanding source systems, and ensuring that they contain good quality consistent data. The architecture chosen using vendor databases to support reporting will simplify this activity. However there will remain differences in interpretation and coding that will need to be understood and reduced as appropriate. An aid to understanding the differences, as well as data quality issues such as gaps or duplication, is data profiling. Until recently, data profiling has been a labour-intensive, resource-devouring, and many times error-prone process. With recent technology a lot of the analysis can be automated, allowing more time to be spent on interpretation of the differences. IBM recommends that cross-iot data profiling be carried out before any improvement project for a particular source. This will lead to data quality improvement projects to improve the data quality and standards within the source systems. By correcting the data within the source systems and improving the use of data standards, there will be an improvement in the accuracy of reports and a reduction in the effort required to produce a reporting system. The primary focus for data quality will be the Banner system for two reasons. Firstly, it is the area where reporting needs to be improved, so is the natural starting point for data quality improvement. Secondly, it is known that there are inconsistencies in usage of the system that should be investigated and understood or reduced Data Integration Extract, Transform and Load (ETL) is the process of converting the information on source files into a consistent, format and integrating across multiple source systems for loading into the data warehouse. Even after a data quality activity as described in the paragraphs on Data Sources above, data extracted from source systems may contain inconsistent codes and references (due to valid business rules or legacy data issues). During the ETL process, it is possible to cleanse the data to help ensure valid data values reside in all fields, providing there are well understood rules that can be followed. The design and implementation of the ETL process typically makes up a large part of a data warehouse project and so are obviously tightly coupled with all other aspects of the data warehouse initiative. When ETL is done right it will not only meet the immediate need of the data currently defined, but will also produce a set of modular, reusable components requiring moderate customisation to accept new data sources or to be applied to new data uses. The recommended approach for the IoT sector will use repositories and extracts provided by the system vendors. These will need to be updated to handle any tailoring or configuration performed on the systems. Where possible, use of ETL technology rather than bespoke code should be encouraged or implemented. Decisions will be dependant on the front end tool selection and whether the vendors provide an ETL as a part of their product set. Acquisition of data from the production environment will be done using a change data capture process. This is the process of incrementally copying data from one environment to another. A database replication tool is currently available and in 50

51 use within the sector and will provide an efficient method of data acquisition. This will be used to replicate only the changed data using database triggers and log files to maximise efficiency and minimise any impact on the production environment Data Repositories The Data Repository Layer contains the databases and components that provide most of the storage for the data for reporting purposes. The Data Repositories Layer s repositories are not a replacement or replica of operational databases which reside on the Data Source Layer. The distributed data architecture recommended for the sector consists of a set of data repositories student, finance, HR, library and timetabling, each consisting of data reshaped from the operational system into formats necessary for making decisions and managing the business. These repositories will be provided by the application vendors, subject to due diligence that they are suitable. Within the data repository layer is the data federation tool, which allows a reporting tool such as Discoverer to access data from more than one store at the same time. This will enable users to create cross-system reports taking data from more than one operational source. To allow cross-system reporting, it will be essential for the sector to implement and manage the multiple functional data repositories as a single logical entity. An overarching data model will need to be developed and maintained. In particular, common standards will be required in the implementation of shared dimensions across the data repositories in order to support analysis across functional domains, e.g. maintaining consistent staff, location, department and course IDs. A high level representation of the subject areas are illustrated below, which over time will need elaboration into a full entity relationship data model: High Level Representation of Data Model Scope by functional domain STUDENT Grant Student Record Example Application Registration Staff-ID HR Employee Job Role Department Management Unit Qualifications & Training Department-ID Staff-ID FINANCE Purchase Order Account Cost Centre Supplier Customer Course-ID SCHEDULE Resource Timeblock Activity Resource-ID Customer Invoice Assets Location Equipment 51

52 4.5.5 Analysis and User Access The analysis and user access areas are closely linked, so in this report we will discuss them together. The data delivery layer consists of a range of BI front end tools within an environment which allows a more useful representation of the data for users. Users can apply a variety of analytical techniques against high quality information to develop the business insights to support more informed decision making. A variety of BI applications will be supported, from static reporting to performance dashboards to sophisticated quantitative models that are embedded within an operational process. This layer is typically composed of various technological components destined to meet specific needs. Requirements for business intelligence vary across IoT, departments, processes and users. A business intelligence matrix will be required to define analytics tools in terms of: business area; user type; types of analysis; levels of maturity; tool architectures; tools; support roles/cost and assist in determining the maturity of users and what technologies and training are needed. The following diagram highlights a high level mapping of users categories in the sector to categories of tools: BI tools aligned to user categories HEA & Heads of Schools Executive Executive Dashboards Dashboard -style interface requiring little or no training and data manipulation. Decision Support Analysts Expert / Power Users Data Mining: Discover meaningful new correlations, patterns, and trends by sifting through data in the warehouse Predictive Modelling: Generate predictive models based on statistical relationships in the data Ad-hoc Query: Users have the ability to define reports with existing data elements, and control content and layout. Users also have the ability to create metrics, filters, etc. Heads of Department General Users Flexible Self-Service Reporting: Predefined reports which users can run - have limited control over content by entering parameters for existing elements on the reports. Limited drilling capabilities may exist. Teaching Staff Basic Users Standard Reporting: Predefined reports available to users. Report content is predetermined, and can not be modified by users. The intention is to provide an integrated set of tools based on a single technology platform from one of the industry leading vendors. It is important that there is one standard tool provided to meet the requirements of each category of users, reducing the need for training and supporting users on a range of toolsets as is currently the case across the operational systems. 52

53 The reporting architecture should also support the following: - Multiple Data Source: Ability to source data from multiple data sources in one report, over multiple technologies, e.g. Oracle RDBMS, Cubes and Excel Web Availability: Reports will be primarily viewed/created/distributed via a Web Browser The future vision will be to embed reporting and analytics into business processes via the sector s strategic portal platform Server based processing: Allowing complex queries to be processed on the server therefore reducing network traffic On-Line Analytical Processing (OLAP): Allow one or more of the following slice and dice capabilities: Multi-dimensional On-Line Analytical Processing (MOLAP) where data is stored in a "cube", where dimensions are the axes and the metrics (or measures) are represented by "cells" Relational On-Line Analytical Processing (ROLAP) where data stored in relational databases is stored in a "dimensional" model and queried for analysis. Desktop On-Line Analytical Processing - analysis is conducted on the users desktop, does not require connections to RDBMS or MOLAP database. Distribution of standard reports to users over web or . Powerful administration features for setting up users and security 4.6 A phased delivery approach Having determined the long-term BI Strategy for the IoT sector, it is necessary to consider the path to achieving it. As shown in Section 2, the strategy will take the IoT sector from Information Maturity level 1 to level 3, where common information can be used as a strategic asset by each Institute. The BI Blueprint above describes a solution which will deliver the required level of information and reporting. In IBM s experience, it is not possible move from maturity Level 1 to Level 3 in one step. Historically many organisations have tried this, and there are countless examples of failed data warehouse projects, which in hindsight have attempted to deliver too much too quickly. Typically these projects have built a large data warehouse without paying sufficient attention to the underlying data issues, and many data-related problems appear only after a lot of time and effort have been spent building a complex solution. In the IoT sector, we have already established that there is a need to resolve the data standards and quality, as well as an urgent need to provide improved information on students. In Section 5 we describe a series of two-year waves to deliver the BI Strategy. 53

54 4.7 Target State 3 Year BI Blueprint for the Sector Laying the foundations Over the next 3 years, the sector should target a number of initiatives which address immediate reporting needs while developing the basic foundations for the 6 year BI target state. This should enable Improvements to student reporting and a cross-system view There is an urgent need to address the current poor level of student reporting from the Banner operational system. This should be addressed as a priority through the development of a Student Data Repository populated with currently available student data from Banner and structured to simplify the access for operational reporting and analysis. A BI user presentation layer should remove the technical complexity of accessing student data and support a cross system view targeted at Unit Cost reporting. Efficiency improvements to the reporting process This involves efficiency measures to reduce the manual overhead of data collation, freeing up resources to focus on more value-added analysis activities. This can be achieved through the deployment of a single BI Reporting platform which automates generation and distribution of standard reports and a flexible, user friendly self-service interface for faster turnaround of ad-hoc requests for information. Quick wins can be made through automating external sector reports and exploiting this information for internal management decision support. Increasing Business Intelligence Competence Business Intelligence knowledge and skills need to be developed and shared across a wider community, reducing the dependence on 1-2 people within each IoT. This can be achieved through the development of a Business Intelligence Centre of Competence aimed at achieving greater collaboration across IoT, developing individual IoT with the requisite knowledge and skills and exploiting shared economies of scale. Greater Information Accuracy, Reliability and Control It is essential that greater exploitation of information is supported by improvements in data quality, data standards and process improvements in the management of data. This is a prerequisite to improving the information maturity of the sector and meeting the 6 year vision. Achievement of this will require a shared service for data management incorporating data management as a key pillar of the Common Standard Design. This shared service competence will include defining data stewards and data quality and transformation services. Greater transparency and accountability The drive for more transparency and accountability across Higher Education sectors internationally, e.g. UK and US, has led to the need to develop a capabilities in performance management. Addressing this challenge in the 54

55 context of this strategy will require the definition of common KPIs agreed across major IoT processes, IoT specific KPIs and the support of HEA KPIs. The Sector s Business Intelligence strategy involves a 6 year journey from improved operation reporting on transactional data to more integrated reporting and analytics. The first 2 years should involve addressing the immediate needs outlined while laying the foundations for the eventual 6 year BI Roadmap. The following blueprint outlines which of the overall major functions from the six year blueprint should be enabled in the next 2 year period. Target State 2 Year BI Blueprint Major Business Functions to be Enabled in the BI Roadmap Sector Alignment & Adoption Strategy & Planning Business Insight Change Management BI Governance Self Service Reports Standard Reporting BI Program Management Learning & Competency Development Data Architecture Student Insight Enrollment Admissions Registration Retention Business Operations Unit Costs Staff Examination Data Stewardship Data Sources Data Integration Data Repositories Analytics Access The 2 year blue-print would be achieved using the following architecture, which is a subset of the 6-year architecture described above. 55

56 Wave 1: Data marts and limited cross-system reporting PROD PROD CORE HR ETL HR HR Mart Mart Web Agresso Finance Banner Millennium Syllabus Plus ETL ETL ETL ETL Fin Fin Mart Mart Student Student Mart Mart Millennium Millennium Mart Mart SchedPlus SchedPlus Mart Mart Limited Federation Layer Analytics Query Report OLAP Other Limited Crosssystem Analytics U.Cost Cube Graphics Dashboard Self-Service Reporting Data Sources Data Integration Data Repositories Analytics Access The interim architecture will be a subset of the full architecture to be developed. The emphasis will be to provide improved student reporting followed by improved reporting on the other systems (Agresso, Millennium and Syllabus Plus) there is already a reporting database for Core, though that may need upgrading. The main difference between the two-year architecture and the full architecture is that only a limited degree of cross-functional reporting will be provided in wave 1 to meet the most important needs, such as unit cost reporting. This will be extended in the second two-year wave to provide more comprehensive set of cross-system reporting. A Student reporting database will be implemented, while the current HR repository as part of the CORE BI Suite will be transitioned to the new environment. New analytical reporting databases for Agresso, Millennium and Syllabus Plus will also be implemented, following the student reporting database. A single reporting and analytics tool set will be selected and implemented for use across all data repositories hosted on the new BI data infrastructure. This tool set will allow a wide range of web-based user access. The standard reporting and self-service flexible reporting elements of the tool will be implemented to meet the 2 year BI goals. There will also be emphasis on data quality in the operational systems as already described, though this is not visible in the architecture. 56

57 4.8 Sector Alignment and Adoption The Role of HEA and An Chéim An Chéim s current role as a provider of shared IT services across the sector is based on its legacy as a programme and project management organisation, with activities being directed on a project-by-project basis. In taking forward this BI blueprint, An Chéim should consider the following implications for their role in the sector. A greater role is required in the management of sector s data assets in order to ensure the use and trust of the data. A greater role is required in driving forward the adoption of BI within the business, enabling BI to be incorporated into core decision making processes which cross the sector. This will involve educating IoT on BI and providing the mechanisms for greater collaboration on business intelligence across IoT. This role of education and business process enablement will be vital to realigning the sector s organisational culture with the emerging trends in Higher Education. A closer relationship is required with the HEA to establish common mechanisms for translating strategic information goals for the sector into action, for example, initiatives to agree common data definitions, common reporting processes and implementation of a sector data infrastructure to enable IoT to expand their capabilities in a cost effective way. There will be an extension of the tools and technology standards and reference architecture provided by the Common Standard Design to ensure the right portfolio of tools and technologies are in place to deliver the right capabilities to the IoT. A successful ongoing BI delivery programme will require An Chéim to consider the balance between ongoing coordination over sector information and data standards with a nurturing and supportive role required to encourage the broader use of BI. An Chéim should adapt their current organisation in line with these needs BI Guiding Principles Guiding principles are recommended for BI and Data Governance initiatives as they can help define the roles and responsibilities of all the key stake-holder groups. These principles are particularly useful where there is a peer rather than a hierarchical relationship between these stakeholder groups. In the IoT Sector, such principles will help ensure there is a common view of the functions to be provided and the respective roles of An Chéim, the HEA and the IoT. Please note: The principles below shown as an example, and are suggested for consideration and adoption. They have not yet been discussed with the stakeholders in the sector. Data and BI environment are enterprise assets and will be managed as such Common definitions and terminology will be agreed across the institutes (e.g. definitions of types of students) An Chéim will Develop reports that are required for external bodies 57

58 Facilitate development of new reports when requested by [one / many / all] institutes. Mechanisms for this need to be defined and agreed. IoT will Provide a reporting facility that can be used by institutes, along with appropriate training and documentation Work with IoT and the HEA to define a set of common KPIs and create reports that include these Define specific KPIs relevant to that institute Write reports specific to that institute and answer ad-hoc queries Share experience and sample reports that might be useful to other institutes Leverage of BI strategy, skill, knowledge and leading practices is a shared priority across the sector and will be facilitated and continually improved Correction of data errors will be done at the source system The sector will continue to seek for information standardisation, consistency and integration. Common definitions and terminology will be agreed across the IoT (e.g. definitions of types of students) Access to IoT data and information storage must be in accordance with the Sector s Information Security Policy. Limited summary-level information may be shared across IoT, e.g. Information submitted to the HEA for the public domain or where there is a cross-iot agreement to share The central solution will capture data from the sector s strategic applications for the purposes of reporting and analysis. Data from local applications will not be integrated into the central solution but the technical architecture will enable IoT to implement such requirements locally. 4.9 Business Intelligence Centre of Competence (BICC) This involves developing a community of BI resources across the sector. This will be a combination of core BI specialists distributed across the IoT with a central BI core resource and coordination from by An Chéim. The BICC will be responsible for agreeing, developing and maintaining BI Strategy and detailed planning based on this BI blueprint Common guiding principles for business intelligence Sector BI data architecture design Common data definitions and metrics through interfacing with the HEA and IoT A common data model representing the logical relationship of data definitions independent of physical implementations Training, development and promotion of BI across the sector, encouraging broader use of BI across the IoT user community enabling them to become more self-sufficient in generation of standard reports and ad hoc analysis tasks Develop reports that are required for external bodies 58

59 Facilitate development of new reports when requested by [one / many / all] IoT Provide a reporting facility that can be used by IoT, along with appropriate training and documentation IoT will however remain responsible for Defining specific KPIs relevant to that IoT Writing reports specific to that IoT and answering ad-hoc queries Share experience and sample reports that might be useful to other IoT It is envisaged that the BICC will adopt a framework as illustrated below for governing business intelligence. It should be emphasised that the central team, under the An Chéim umbrella is not expected to be more than 1-2 full time roles, including a BI PMO role and Enterprise Information Architect. They will provide the focal point for all the sector s BI needs, and co-ordinate the involvement of third parties in the development and maintenance of the sector s BI assets. Align and Manage: Processes and people that manage the alignment of BI resources to BI strategies. Management of interdependent efforts and initiatives. Data Governance: Management of enterprise data assets to increase the use and trust of the data. Organisational Governance: Processes, people and structure that enable the ongoing management and control of BI initiatives. Data Process Internal and external Align decision making data process with BI strategy Sector wide and IoT data Data warehouses Organisation Align and Manage BI Steering Committee, BI Guiding Principles, Strategy & Roadmap Governance, BI Program Management Technology Structure Tool and Technology Center of Competence Standards (POC) Accountability and Decision making Common reference and solution architecture Process Governance: Business oversight of the decisions to align planning, measurement, and analysis efforts across the sector. Technology Governance: Ensuring that the right portfolio of tools and technologies are in the place to deliver the right BI capabilities to the institutes Data Governance / Data Quality Management Most often in a data warehouse initiative the major issue is data quality and the effects of data quality on their ability to be successful. The old adage garbage in garbage out takes over the project when in fact, with proper planning and focus, the issues are quite manageable. The importance of the data quality issue on the success of the BI programme and the concerns expressed by An Chéim about data quality warrant a particular focus. We are proposing that a Data Quality initiative is initiated to coordinate all data ownership and data quality issues and activities for the BI programme. The 59

60 aim of this project will be to develop data quality strategies that will span the development and future management of the business intelligence solution. The stated objective of this Data Quality project is to ensure the educational decisions being made as a result of the data warehouse are based upon the best information available by establishing data and architectural standards. For example, the project could use a framework such as IBM s Data Quality Assessment Framework as a guideline for specifying what must be managed in a data quality project, including: - Policy, e.g. strategic direction, goals Organisation, e.g. governance, roles and responsibilities Administration, e.g. meta-data, access control Architecture. e.g. data, application, infrastructure Processes, e.g. data quality flagging, remediation Validation, e.g. data quality metrics, scorecard Communication, e.g. stakeholder management Compliance, e.g. framework audit It is envisaged that the data governance disciplines will be established as part of the project and adopted into the Common Standard Design. Data governance will then be embedded into the sector organisation as part of business as usual. It is again expected that An Chéim will need to maintain a small number of central resources to provide a focal point for data governance across the sector, ensuring adherence with sector policy and standards, and providing specialised ervices to the IoT when needed to maintain and safeguard data assets. 60

61 5 BI Roadmap and Implementation Planning 5.1 Introduction In this section we lay out the roadmap for implementation of the BI Strategy. Implementation of the Strategy is set in the context of the evolution of reporting / BI in the sector and integrates with other initiatives progressing in the area. As described in Section 3, implementing the overall BI Strategy will move the sector from an Information Maturity level 1 to level 3. However it is not advisable to aim to move two levels in a single project. To attempt to build a major BI solution without resolving some of the underlying data issues first would be likely to be expensive and would have a high risk of failure. As a result, IBM recommends that the BI Strategy be implemented in a series of waves. Each wave can be considered as a programme of projects that move the sector forward. The section contains an outline for all three waves, and a detailed description of each project in wave Integrated BI Roadmap Implementation of the BI Strategy should be seen as a continuation in the evolution of reporting / BI in the IoT Sector. This evolution is characterised by a number of distinct stages of development: 1 st Generation BI - the predefined reports that were bundled with Application Packages. These reports are written in a programming language resulting in the need for specific programming skills to amend existing or create new reports whilst minimising the effect on performance of production systems. 2 nd Generation the provision of tools that allow users to have direct access to data, to generate ad-hoc queries and to produce limited graphical and other analytical reports. Queries and reports can be written by users, thereby alleviating some of the impact on IT Departments, but introducing a potential risk to production system performance. Data Warehouses/Marts can be used in 2nd Generation BI Implementations to eliminate the performance impact of these reporting tools on the production systems and to store data in a manner where it could be accessed more easily. Recent successes in pilot projects using Oracle Discoverer 10g for the Banner and Core applications have demonstrated that 2 nd Generation BI implementations can be run from An Chéim s centrally hosted environment for ad hoc reporting 7. Implementation of this BI Strategy will consolidate progress to date and will move the IoT sector towards the 3 rd and 4 th Generation BI capabilities required for Maturity Level 3 on IBM s Information Maturity Model. It should be noted however, that this progression will not happen without appropriate funding being in place and sustained throughout the full six years of the implementation programme. 7 One of the early projects in the Strategy will be the roll-out of this 2 nd Generation BI functionality to the IoT using Oracle Discoverer 10g (Project 2d - p.71). 61

62 The chart 8 below illustrates the integrated approach being adopted at sector level to the implementation of BI across the IoT: The first three years will see significant progress in the overall implementation of the BI Strategy with numerous projects aimed at laying the foundation for effective BI in the sector. The intention is to incrementally roll-out enhanced BI functionality to IoT during this period as it is developed rather than waiting to the end of the period to do so. BI projects will be delivered as part of three waves : Wave 1 Lay BI Foundations Wave 2 Develop BI Capabilities Wave 3 Embed BI into Business Processes Each wave will be made up of a number of projects and work-streams, and there will be some work-streams which will last for the duration of the programme. These waves can be mapped against the eight capabilities described at the beginning of Section 4 to demonstrate the timing of programme deliverables, particularly those relating to Wave 1: 1. Improved reporting on student Improved reporting on the information in Banner will be a major deliverable of Wave 1. Wave 1 will also provide enhancements to the other reporting systems, to fit into the overall architecture. 2. Improved cross-system reporting An initial cross-system reporting solution will be implemented towards the end of Wave 1, to meet current needs such as Unit Cost reporting. A 8 Please note that a large format version of this chart is available in the appendix of this report. 62

63 more complete implementation of cross-system reporting will be the primary deliverable of Wave Widespread access to reporting suited to users Appropriate reporting will be delivered throughout the programme as part of each project s deliverables. The reporting infrastructure will provide widespread access to standard reports through a web interface, with a single sign-on where this is supported by the overall infrastructure. 4. Improved data standards and quality An early deliverable from Wave 1 will be a review of the data standards in the source systems, leading to projects to improve the consistency and quality of data within these systems. This improvement will be a key building block to enable cross-system reporting in Wave Agreed Key Performance Indicators As described in section 4, gaining agreement on the main Key Performance Indicators and measures before implementation of new systems will reduce the effort required to produce them later. These indicators may be included in reports or on a management dashboard. During Wave 1 senior management and other key stakeholders from the IoT as required will be consulted in agreeing consistent indicators and measures. Note that IoT will have the opportunity later to tailor their KPIs or use additional KPIs to meet their own specific requirements, as we recognise that the IoT will have different objectives and priorities to each other at different points in time. 6. Simplified external reporting requirements A primary objective of the cross-system reporting created will be to support external reporting and reduce the effort involved in supplying it. In addition to KPIs for the IoT, reporting requirements from external sources will be considered during Wave 1, and appropriately integrated into the BI Strategy. Opportunities will also be investigated for the rationalisation of external reporting, to see whether the content and timing of reporting to different external stakeholders can be coordinated. 7. Flexible architecture and technology The BI solution architecture and tool set will be defined early in Wave 1, and this will be implemented over all three waves. As discussed earlier, the architecture at the end of Wave 1 will support reporting from each source individually with limited cross-system reporting, and at the end of Wave 2 this will be extended to support a more comprehensive set of cross-system reporting. 8. Improved support model Wave 1 will set up an appropriate organisation arrangements that will own the solution and provide an appropriate level of support to the IoT. This 63

64 support model will evolve as appropriate during Waves 2 and 3 of the BI strategy implementation. 5.3 Wave 1 (Years 0-3) Lay BI Foundations Programme Detail The first three years (Wave 1) is targeted at laying the foundations for the BI 6 year blueprint and addressing the immediate needs for all source systems, with a particular initial focus on student reporting and unit costing. Wave 1 is comprised of a series of individual projects organised into a number of discrete work streams. The portfolio of recommended work streams and projects is illustrated in the chart 9 below: Work Stream Descriptions Wave 1 is described in five work streams: Stream 1: Data Infrastructure This work stream focuses on improving the data quality of the source systems and increasing consistency of data standards, initially focussed on student information but then extending to other sources. It will form a key input into data quality projects that will be initiated to clean-up Banner and other source systems. Stream 2: Reporting Delivery This is the main delivery stream of the programme. In this stream, the Banner reporting solution will be evaluated to determine whether it is suitable for the sector. Following that, a tool-set for the sector will be determined together with the overall solution architecture. The tool 9 Please note that a large format version of this chart is available in the appendix of this report. 64

65 selection follows the Banner evaluation as the result of the evaluation will have a strong influence on what tools are most suitable. Following evaluation and tool selection, the student reporting solution will be implemented, with a pilot followed by a full roll-out. The reporting tools for the other systems will be evaluated and rolled out in a similar fashion. In parallel a project will be initiated to roll-out Oracle 10g to the sector. In addition an initial set of cross-system reporting will be set up to provide improvement to some of the most important cross-system reports. Stream 3: Performance Management As described in this report, there is an opportunity to coordinate the information requirements of the IoT, HEA and other external stakeholders such as the CSO. The purpose of this stream is to agree a common set of performance indicators and measures that will meet the needs of the IoT and the HEA, and where possible to add these measures to the existing system reports as an early win. Where immediate reporting improvement is not possible, the agreed measures will be added to the requirements of the other BI delivery projects. Stream 4: Alignment and Adoption The focus of this stream is to agree and set up appropriate organisation arrangements to drive, manage and support BI for the IoT, and put in place a data governance structure. For the duration of the programme, these roles will be set up as part of the project. Some of these roles are likely to be necessary beyond the completion of programme, so at the end of the programme decisions will be needed on how to sustain them. Stream 5: Programme Management The stream is responsible for the successful delivery of the overall programme. It will involve the establishment of programme governance and support structures and will include stakeholder and risk management. 65

66 Mobilisation Phase As can be seen from the above chart the Wave 1 implementation programme involves a large portfolio of projects, some of which will be highly complex in nature. IBM recommends that implementation begin with a detailed mobilisation phase. Key activities during this phase would include: Definition of overall programme governance, including executive sponsorship Development of appropriate involvement and consultation mechanisms for key IoT and other stakeholders Agreement on the level and nature of required IoT and other stakeholder support for the BI implementation programme Agreement on baseline performance information, benefits realisation framework, financial protocols, and required business case methodologies Establishment of a Programme Management Office Identify interrelationships and dependencies between projects Detailed planning and scheduling of projects, using the project descriptions set out later in this report Conduct of any procurement / contractual activities required in support of the BI implementation We estimate that this mobilisation phase should last for 4-6 months and recommend that it be largely completed before examinations begin in the early summer of In order to progress the overall BI Implementation programme IBM recommend that a number of specific projects are carried out (or at least initiated) during this period. For example, the evaluation of student reporting (Project 2a), is a critical path project and should be completed during the mobilisation phase. Consideration should also be given to initiating data assessment and quality projects within the Data Infrastructure work stream. It is also likely that the roll-out of Oracle Discoverer 10g (Project 2d) would begin during the mobilisation phase. Other suitable projects to begin during the Mobilisation Phase would be agreed with key stakeholders at an early stage. In the sections that follow we set out the nature of the proposed implementation work streams and outline the projects contained within each The projects outlined have been identified as those required to deliver Wave 1 of the BI Strategy. During the Mobilisation Phase, the precise project programme, timelines, and interrelationships between individual projects will be further detailed. This detailed planning may involve projects being added, split, merged or de-scoped. 66

67 5.3.1 Stream 1: Data Infrastructure Project 1a: Data profile of student system This project will use a tool-based approach to review the quality of Banner student data across the sector. This will include identifying possible gaps, duplications and differences in the way items are coded. This will require use of a data profiling tool. Purpose Output Duration Resources Required Involvement from IoT Dependencies A technical review of the data within the different Banner implementations to provide a baseline for data definitions review Report on data profile and recommendations for improvement and next steps 1-2 months Technical resource to run data profiling tools, investigate discrepancies and prepare findings Data profiling tool Provision of sample set of data Respond to specific questions on definitions once initial profile completed Review overall findings and provide feedback Obtaining a data profiling tool Project 1b: Review Student Data Definitions The future BI solution for the sector requires a great degree of coordination. In order to successfully manage multiple BI implementations across the IoT, there needs to be well defined set of data definitions and supporting data model. This project will start with the student domain, reviewing and defining data definitions for incorporation into the Common Standard Design. This will maximise benefits and reduce duplicate effort on future BI implementation. The project will develop a glossary of student data definitions and an accompanying data model through the review of existing definitions governed by the CSD and HEA, and IoT. A structure and process will be used to consolidate existing and agree new definitions. The data standards should ideally cover the full student life-cycle from recruitment to alumni and life-long learning. Note that this is not intended to force uniformity as there will be clear areas where variation is desirable for instance the difference between numerical and alphabetical exam results. However where similar items have different codes, or the same codes are used with different meanings 67

68 Purpose Output Duration Resources Required Involvement from IoT Dependencies Review data standards for student and develop an agreed set of standard values Clear definitions of coding for values around student 3 months Data analyst and IoT input Participation in workshops to review definitions and derive a more standard set Output from data profile Requirements from any future known system changes e.g. modularisation Project 1c: Implement improvements to data quality in core systems This project is to facilitate improvement of data quality in the source systems. The majority of this work will happen outside the scope of the programme, for instance as part of an enhancement or upgrade to a source system, but the BI programme should act as a stakeholder in these projects in order to ensure that the improvements are made. Some data quality work will be performed by the IoT themselves, for instance to standardise on codes or to ensure complete set of data is entered. The data quality improvement work will address the gaps identified in the data profiling done for each source system and implement changes as required for both current and legacy data. These resolutions could range from setting null values to a dummy default value, updating codes used, to introducing new business and / or technical processes to ensure data quality of current data. Purpose Output Duration Resources Required Involvement from IoT Dependencies Promote and coordinate improvements to data quality in the source systems Improved and more standardised data Dependant on improvement schedule for source systems Source system project team Commitment to improve the data quality in the operational systems Improvements will be made by projects responsible for the source systems Ensuring an appropriate balance between central and individual IoT investment for funding and resources. Availability of technical resources skilled in the source systems Need to amend business processes and practices to maximise use of existing and available but unused modules. 68

69 Project 1d: Data profile of other systems Following the data profile of the student system, similar projects will be carried out for the other major source systems. The order is to be determined, and will be influenced by the improvement or upgrade programmes for the other systems. Where appropriate, data standards will be defined as for these systems as in section 1b, but this is not shown as a separate project as the scale is likely to be smaller than for the student definitions. There is likely to be a series of similar recurring projects covering each source system in turn. Purpose Output Duration Resources Required Involvement from IoT Dependencies A technical review of the data within the remaining source system implementations Agresso, Core, Millennium and Syllabus Plus. Report on data profile and recommendations for improvement 2 months per source system Technical resource to run data profiling tools, investigate discrepancies and prepare findings Findings from Banner Data profiling project Provide sample data sets Respond to specific questions on definitions once initial profile completed Data profile tool Stream 2: Reporting Delivery Project 2a: Evaluate student reporting tools This project will evaluate the Banner reporting system repository to determine whether it is an appropriate reporting platform for the IoT sector, taking into account the tailoring that has been made to the operational reporting system. Evaluating the fitness of the data model structure and pre-packaged reports against the student logical data model, data definitions, and reporting requirements of the sector. Including future major changes to business practices i.e. modularisation Evaluating the effort of populating the repository from Banner, taking into account the impact of Banner customisation Evaluating the effort of populating the reporting system from other sources, e.g. finance data for unit costing Evaluating the effort of generating federated reports which combines ODS data with other sources through the BI reporting layer, e.g. finance data source for unit costing Ability to support provide reports required by the sector Architectural flexibility Report generation capability (standard and ad-hoc reports) References and market penetration 69

70 This evaluation will probably include installation of the Banner reporting tool and involvement of one or more institutes. However this evaluation should not be considered at this stage a pilot, in the sense usually used in the sector i.e. a Rollout to an individual institute. Should the evaluation be successful, a normal pilot would be carried out later. Should the evaluation not be successful, alternatives may need to be considered, including developing a semi-bespoke solution with assistance from experience Banner consultants. Purpose Output Duration Resources Required Involvement from IoT Dependencies Evaluate the Banner ODS reporting product to determine whether it is appropriate for the sector prior to pilot A report on the suitability of Banner ODS 2-3 months Evaluation team Specify specific reporting requirements to be considered For an involved evaluation site: Explain and data discrepancies to allow evaluation system to be installed Test the system in a controlled environment, such as on a copy of the operational Banner system Report from BT Banner reporting pilot (based on Discoverer) Project 2b: Select BI Tools ETL & End User The purpose is to evaluate, select and procure the BI tool from the tool vendors suggested in the BI Strategy on behalf of An Chéim. These tools include reporting, OLAP and dashboard functionality, as well as ETL and meta-data capability IBM consider that the most appropriate approach will be to consider suites of tools from major vendors, as they will have an interest in ensuring integration and commonality between the tools. Following recent consolidation almost all the mainstream reporting tools are owned by major software companies. Major candidates will be Oracle, Cognos (under offer of purchase from IBM) and Business Objects (under offer from SAP) This is not intended to be a detailed analysis of the functionality provided by each tool, as the tools suites can all meet most general functional requirements. The selection criteria will therefore be based on Critical or unusual functional and non-functional requirements Cost Local implementation and post-implementation support Existing base and skills, including use of Discoverer, Access, Business Objects etc Other commercial terms 70

71 An assessment team will be brought together to represent the interests of the key stakeholders. The vendors will be asked to respond to a short questionnaire, and present to the assessment team The selection is scheduled after the evaluation of the Banner reporting tool, as that product has linkages with particular vendors including Cognos. Therefore the outcome of that evaluation is likely to influence the tool selection. Purpose Output Duration Resources Required Involvement from IoT Dependencies Determine which suite of tools should be used in the sector Recommendation on tool suite 1-2 months Evaluation team, vendor participation Input and participation in workshops from representative key information providers with tool experience Completion of Banner student reporting evaluation Project 2c: Technical Architecture The technical architecture will determine the overall solution architecture for the BI reporting system. It will confirm the technical approach, and identify any gaps. A key subject for investigation will be to confirm the technology for data federation that will be used for cross-system reporting, and which parts of the vendors reporting systems are appropriate for that. Purpose Output Duration Resources Required Involvement from IoT Dependencies Define overall BI solution architecture Confirmed products and approach for the overall solution 4 months BI Architect Limited technical input from interested parties Approval of overall architecture when complete Evaluation of Banner student reporting evaluation (this can start before evaluation fully complete). Project 2d: Roll-out of Oracle Discoverer 10g An integral part of the BI strategy in will be the roll-out of Oracle Discoverer 10g for Banner and Core to all IoT. This will involve the provision of a web-based solution to provide an interim ad hoc reporting infrastructure to meet the following needs: Ad-hoc querying and reporting Direct access to data Graphical format reporting Export to Excel spreadsheets Ability to write own reports 71

72 Purpose Output Duration Resources Required Involvement from IoT Dependencies Roll-out Oracle Discoverer 10g to IoT Implemented solution months, dependant on ability to roll-out to multiple IoT at one time Roll-out & Training Team Active involvement including implementation and training Successful completion of Pilot Project 2e: Pilot Student reporting system Delivery will commence with a pilot of the student reporting system. This delivery will be the first one to make use of a Business Intelligence methodology which can be adapted to suit the needs of the sector. A BI method provides: A tried and tested approach for implementing BI solutions to be agreed and adopted by the sector An accurate schedule to be estimated for the follow-on implementation of the student and cross-system reporting platform. Prioritisation and documentation of most urgent reporting requirements for student and cross-system information and creation of an initial common data model with supporting glossary. This will Fast track of the requirements and design phases for the first release As input to the pilot, reporting requirements will be gathered through a series of workshops with the IoT, together with detailed analysis of requirements at a small number of representative institutes. Where possible, existing expertise and knowledge will be incorporated into the solution The pilot is currently shown to cover the registration period, to check whether the solution can support this important period in the academic year. It may be appropriate for the pilot to be split into sections, for instance piloting one part during registration, another during examinations etc. This will be determined after the evaluation by the programme management function. Purpose Output Duration Resources Required Involvement from IoT Dependencies Develop and pilot new student reporting in one IoT Full definition of common BI requirements for Banner for all IoT Confirmation of readiness of the solution for a wider roll-out 4-6 months, ideally incorporating registration period (confirm whether to incorporate or avoid this period) Business Analysis and development Active involvement of pilot IoT Completion of tool selection and student data definition review 72

73 Project 2f: Roll-out student reporting Following the successful pilot, the student reporting system should be rolled out to the remaining IoT Purpose Output Duration Resources Required Involvement from IoT Dependencies Roll-out student reporting to remaining IoT Delivery of improved student reporting months, dependant on ability to roll-out to multiple IoT at one time Roll-out and training team Active involvement including implementation and training Successful completion of pilot Project 2g: Develop and pilot cross-system reporting Following the pilot of the student reporting system and any necessary improvements to the other reporting systems, a limited cross-system reporting capability will be built. The purpose of this cross-system reporting is firstly to simplify some of the most complex reporting for the IoT, and secondly to test the approach to cross-system reporting in the IoT sector. Wave 2 will implement a more complete set of crosssystem reporting. Purpose Output Duration Resources Required Involvement from IoT Dependencies Create system for a limited amount of cross-system reporting Pilot study 4 months Development resource Active involvement of pilot and roll-out Successful completion of student reporting system and possibly improvements to other reporting systems Project 2h: Roll-out cross-system reporting Following a successful pilot of cross-system reporting, it will be rolled out to the remaining IoT. Purpose Output Duration Resources Required Involvement from IoT Dependencies Roll-out student reporting to remaining IoT Delivery of improved cross-system reporting 3-6 months, dependant on ability to roll-out to multiple IoT at one time Roll-out and training team Active involvement including implementation and training Successful completion of pilot 73

74 Project 2i: Pilot other reporting systems Following the development of improved student reporting, which is a priority for the IoT, similar systems may be implemented for the remaining systems (Agresso, Millennium and Syllabus Plus). A reporting database for Core already exists. These improvements can be implemented in parallel with the roll-out of student reporting, though the timing may be based on the improvement schedule for the source systems, and the necessity for upgrade to support the cross-system reporting. This will be determined during the architecture phase. Purpose Output Duration Resources Required Involvement from IoT Dependencies Develop and pilot improved reporting for the remaining systems (a series of similar projects) Delivery of improved reporting for each system in turn 3-4 months per system Timing to be determined following the technical architecture study Development resource Source system expertise (internal or vendor) Active involvement of the pilot IoT Data profiling exercise for that system Project 2j: Roll-out other reporting systems Following successful pilot of each reporting system, it will be rolled out to the remaining IoT Purpose Output Duration Resources Required Involvement from IoT Dependencies Roll-out student reporting to remaining IoT Delivery of improved student reporting months, dependant on ability to roll-out to multiple IoT at one time Roll-out and training team Source system expertise (internal or vendor) Active involvement including implementation and training Successful completion of pilot 74

75 5.3.3 Stream 3: Performance Management Project 3a: Define standardised common metrics / KPIs This project will be to determine a set of common key performance indicators that can be centrally supported and produced. This project will focus on the shared and non-contentious indicators. It is likely that the KPIs defined will be a subset of all the possible KPIs that individual IoT may want or need, and that some IoT may want additional indicators to monitor their particular business strategy. This activity is also likely to clarify some of the difference in interpretation that exists today in KPIs, for instance how preparation time is used in unit cost calculations. Note that this will not enforce a standard set on any IoT, but will allow the differences to be clearly identified and supported. At present, there is an opportunity to coordinate KPIs with external bodies such as the HEA. Definition of common performance indicators will involve a series of workshops, with participation of the HEA so that there will be an opportunity to create alignment between the measures required by the IoT for their own use and those required by the HEA Purpose Output Duration Resources Required Involvement from IoT Dependencies Agree a set of KPIs across the sector and with the HEA Agreed set of KPIs and measures that can be implemented by later projects 3-5 months This activity may stretch out over a long elapsed period due to availability of senior staff members. Timing could also be determined by external factors, such as the HEA schedule. IoT senior management and HEA input IoT senior management and HEA input Informed by data profiling activity Project 3b: Build initial key performance reports While some KPIs may require the new reporting functions to be in place before they can be delivered, it is likely hat other KPIs can be implemented on the existing reporting functions. Where possible, reports will be developed using the existing reporting functions to support any new KPIs and measures that are agreed. 75

76 Purpose Output Duration Resources Required Involvement from IoT Dependencies Agree a set of KPIs across the sector and with the HEA Agreed set of KPIs and measures that can be implemented by later projects 3-5 months This activity may stretch out over a long elapsed period due to availability of senior staff members. Timing could also be determined by external factors, such as the HEA schedule. IoT senior management and HEA input IoT senior management and HEA input Informed by data profiling activity Stream 4: Alignment and Adoption Project 4a: Agree and set up BI support organisation The purpose of this project is to set up a BI support organisation for the sector, identifying the resources, skills and sponsorship that will be used to support sector information needs across the IoT, support current and future implementations, establish standards controlling its usage, maintenance and upgrading technical and business process environments. The organisation will form the heart of a BI Centre of Competence (BICC) that will provide support to the users and the overall programme, and help ensure that appropriate change management and training activities are included in each implementation. The BICC will be a combination of small number of key skills managed centrally, together with the network of main users around the IoT. Setting up the support organisation will involve: - Determining the scope of BICC s efforts Use of BI methodology to determine the roles and commitments required, and guidelines for the BICC s work Identifying people with the right skills, and making best use of the available pool of skills across the sector operations and projects Identification of an executive sponsor to guide and promote the BICC s efforts Definition of BI Guiding Principles Purpose Output Duration Resources Required Involvement from IoT Dependencies Set up the organisation to support the overall programme and the IoT users An organisation structure set up under the programme 3 months Senior An Chéim and IoT input and agreement Senior management agreement Investment of resources for duration of Programme 76

77 Project 4b: Data Governance An important goal will be to set up the data governance framework to allow the sector to manage its own data as an enterprise asset. This will include an adapted method to enable the sector to repeat the data quality assessment tasks at a later point for other core systems. This initiative will have responsibility for: Assessing current data quality using a data profiling tool, and developing a procedure for new data prior to entering data warehouse Gaining support of senior-level sector management through highlighting the importance of data quality, and securing the authority to define and enforce a data ownership policy Establishing and coordinating a Data Quality Steering Committee of both An Chéim and IoT personnel Determining data quality standards, data ownership responsibilities and policies Data quality management tasks include Standardisation of common data definitions based on basic set of data elements, Authorisation of access and validation of security Support of the user community Management of business rules and metadata Standards management which allows for a balance between common definitions and flexible IoT definitions Data Ownership Policy tasks include: Senior-level management support of enforcement of the policy, including signatures. Identify stakeholders, producers and consumers Catalogue all data sets covered under the policy Determine ownership model for each data set Determine roles associated with data ownership Assign responsibilities of each role Create a policy for acceptable data Make any necessary recommendations to ensure better quality and establish a timeline and schedule for cleanup. Create documented guidelines and procedures 77

78 Purpose Output Duration Resources Required Involvement from IoT Dependencies Provide a mechanism for ownership of the data and its definitions A mechanism including people and technology to better manage the data On-going Data stewards who will manage the definitions on behalf of the sector The mechanism and organisation for this will need to be determined Representatives to major reviews and board to support data ownership and stewardship functions Output from assessments Creation of the overall programme organisation Stream 5: Programme Management Delivering a complex Business Intelligence programme requires strong management input to provide overall direction and ensure the programme receives sustained focus, To deliver this programme successfully, it will be essential to have a strong programme management function with overall responsibility for delivery of the programme. Responsibilities will include: Maintaining sponsorship and coordination with stakeholders Ensuring suitable involvement of the sector Taking major decisions as required throughout the programme and acting as the arbiter of any conflicting issues Defining project charters and providing oversight of projects Managing overall programme risks Ensuring appropriate change management activity takes place Acting as an overall design authority Purpose Output Duration Resources Required Involvement from IoT Dependencies Overall programme management to deliver the BI Strategy A successful programme that meets the needs of the sector 2 years Steering board containing senior management from An Chéim, Business Intelligence technical and programme expertise. Representatives from senior management None 78

79 5.4 Wave 2 (Years 4-5) Develop BI Capabilities Programme Overview At this early stage it is not possible to fully plan Wave 2 in full detail, but it is clear that there will be two main priorities: Providing more complete set of cross-system reports, built on the capabilities delivered in wave 1 Exploiting the information already delivered, through more reports analysis and delivery to a wider audience At the end of Wave 1, the sector will have implemented major improvements to the reporting available to IoT, and will be in a position to implement a more complete set of cross system reporting. The cross-system reporting will build on the enhancements in Wave 1 including the improved data quality and standards, the reporting capabilities aligned by individual sources, and the flexible architecture built. The cross-system reporting will be usable both by the advanced users, to produce more complex analysis, and by managers and educators to have easy on-line access to information to that currently can take weeks to produce. The implementation of cross-system reporting is likely to be implemented out by information subject area. This means that the technology will be developed and rolled out for one specific set of information, and then further sets of information added in later projects. The timing and sequence of this will be determined closer to the time. Additional reports and forms of analysis will be delivered in this stage, including additional dashboard functionality to help the IoT exploit the information that is available. The support organisation defined in wave 1 will also focus on helping the IoT make better use of the technology that has been implemented. During Wave 2 the reporting capabilities will be provided to a wider audience, including for instance educators and potentially students. These people are likely to be infrequent users who will put a premium on easy access to data they can trust, and are less likely to spend time understanding complexities around the meaning of data than more frequent users. The work on data quality and standards earlier in the programme will be key to ensuring that these users can make appropriate use of the data provided. 5.5 Wave 3 (Year 6) Embed BI into Business Processes Programme Overview Business value from the significant investments in sector s information assets can only be fully realised through a shift in the organisational culture where information is used as a matter of course to inform all decision making processes. Wave 3 will involve embedding information assets into business processes, e.g. student enrolment, examination and course planning, through the sector s portal and web services platform. A fully integrated executive dashboard is envisaged to support senior management and the HEA. This will provide the institution's Director, Heads of School, Student Services, Registrar and administrators, the ability to access mission critical metrics and key performance indicators and act 79

80 on the information immediately. This unique monitoring, reporting, and analysis tool provides insight into real-time performance, root causes, and potential outcomes, allowing institutions to optimise day-to-day performance. Wave 3 projects will evolve over time, as the Institutes capabilities increase. At present, the full range of projects for Wave 3 cannot yet be defined as these will be dependant on the outcome of Waves 1 and Implementing the BI Strategy Implementation of the BI Strategy for the sector is a not insignificant task. It will require proper governance and programme management arrangements to be in place, coordination of effort and involvement from across the sector and appropriate use of external expertise. An Chéim should have overall responsibility for implementing the BI strategy in the IoT sector. The nature of the initiatives involved will require input and cooperation from the sector. This is most likely to be achieved by: the establishment of a representative steering committee from the IoT Sector (based on the existing SIF steering Committee and drawing in additional expertise as required); the selection of representative pilot and validation sites for initial implementation of the recommended solution; on-going involvement of key personnel from the sector in specific BI programme initiatives An Chéim however, has insufficient scale and expertise to deliver the BI strategy out of its own resources. The implementation programme is complex involving numerous interdependent and overlapping projects and initiatives. In addition, An Chéim should not waste time and resource in carrying out multiple fragmented procurements for each individual element of the BI programme. An Chéim should leverage its recently completed Framework Agreement in selecting BI implementation partners with the skills required to effectively deliver the BI Strategy. The company selected should have: Knowledge of and track record in the Higher Education sector Proven programme and project management experience including the implementation of a complex, multi-dimensional set of in-flight projects World class BI credentials and local expertise A proven Business Intelligence methodology and architecture that can be tailored to the sectors needs and integrated into An Chéim s own techniques. The scale to provision the full range of skills and resources required 80

81 Glossary & Large Format Figures Glossary Term Agresso Banner BI Core CSD CSO DW ETL HEA HE2012 IoT KPI Millennium ODS Source systems SunGard Syllabus Plus Definition The finance system in place in the IoT. The student management system in place in the IoT. SunGard is the vendor of Banner. Business Intelligence The HR system in place in the IoT. Common Standard Design implemented by An Chéim today Central Statistical Office Data Warehouse Extract, Transform and Load A technique for moving data from operational systems to reporting systems Higher Education Authority IBM white paper Higher Education 2012: Executing to lead in the learning economy Institute(s) of Technology Key performance Indicator The library system in place in the IoT Operational Data Store In this context, this term has two meanings 1. A concept in Business Intelligence for a data store for operational reporting 2. It is the name of a reporting database for Banner produced by SunGard (in this case it is referred to as Banner ODS) Used to indicate the primary source systems used across the IoT: Banner, Agresso, Core, Millennium and Syllabus Plus The vendor of Banner The time-tabling system in place in many of the IoT. Scientia is the vendor of Syllabus Plus. 81

82 An Business Chéim Business Intelligence Intelligence Strategy Strategy and Roadmap and Roadmap for the Institutes of Higher Education IBM Information Maturity Model 82

83 BI Blueprint Sector Alignment & Adoption BI is a part of the organisational culture and is sustained by sector and institute commitment, engagement and adoption of BI capabilities. BI is championed by senior management and adopted at every level to ensure organisational alignment. Business Insight An environment in which users can apply a variety of analytical techniques against high quality integrated information to develop insights that can be leveraged throughout the sector and institutes to make informed strategic and tactical business decisions. KPIs, metrics, dashboards and reports are used to measure institute, operational and employee performance and drive accountability for results. Each level of the organisation understands how it impacts institute and sector performance and results. Established organisational structures and processes to ensure a valuebased, business-driven build out of BI capabilities. Data Architecture Student Insight Using BI to understand to develop deeper insights into the current and potential students values and needs and to develop services to satisfy them Business Operations Using BI for to improve the management of business operations, e.g. to identify time and cost savings through eliminating courses with declining interest, developing new courses and aligning staff skills and knowledge. A set of enabling technologies that collects disparate data, integrates that data into usable structures, and then presents a more useful representation of the data within views that are intelligent and fact based. 83

84 An Business Chéim Business Intelligence Intelligence Strategy Strategy and Roadmap and Roadmap for the Institutes of Higher Education Integrated BI Roadmap 84

85 Wave 1 Project Portfolio 85

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