Schneider Electric Business Intelligence

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1 Schneider Electric Business Intelligence March 2011 / White paper Make the most of your energy

2 Summary Executive summary... p 1 Introduction... p 2 Business Intelligence technology at the customer s service... p 4 ETL processes... p 6 Business Intelligence tools... p 7 Method... p 9 Risk factors in the implementation of Business Intelligence technology... p 10 Schneider Electric services... p 11 Schneider Electric success stories... p 12 Conclusion... p 15

3 Executive summary The wide range of processes within the successful business, from planning to strategic implementation, requires accurate and ready information throughout. The cast of personnel involved across the business operation requires widely varying types of information to perform their assignments. In all, the successful business requires a powerful Business Intelligence technology. Discussion covers the constitution and requirements of the effective Corporate Information Factory (CIF) Architecture. The Data Warehouse component of the CIF Architecture must be a fl exible and reliable store of company information that allows a high degree of differentiation in data selection, modeling and analysis. Next, the ETL processes extract, transform and load are responsible for accurately populating the Data Warehouse with information and enabling the use of this data. Again, differentiating methodologies, along with validating performance testing, must be accommodated. Third, Business Intelligence tools for multi-dimensional analysis, budgeting and forecasting, effi cient reporting, and data mining for enhanced insight assure the proper information is accessed for each specifi c business process. Developing and implementing the CIF Architecture involves defi nition of short-, medium-, and long-term objectives for the system as well as defi nition of the elements involved. When a company implements a Business Intelligence technology, it is important that risk factors be identifi ed and evaluated, including the scope and degree of diffi culty of information integration, speed and adaptability, utility and practicality for the employee, and long-term effectiveness. Schneider Electric Business Intelligence services are based on the company s vast experience in helping organizations defi ne their BI policies and develop their BI Architecture. It offers a productive competence center for consulting support, a proven product portfolio that allows effi cient and effective development of specifi c BI solutions, and highly reliable technical assistance for specifi c needs or longer term. Several successful Business Intelligence technology solutions implemented by Schneider Electric are described. Schneider Electric Business Intelligence White paper on Business Intelligence 01

4 Introduction Business Intelligence can be defi ned as a collection of tools and processes that helps users make decisions. Generally, business processes can be divided into three phases: planning, implementation and strategy each requiring unique information. The planning phase allows the user to take into account known details such as budget, the previous planning period and the point at which the previous plan deployed. In creating the new plan, the user wants to present various scenarios supporting improved decisions considering factors related to the past, present and future and simulate deployment of each situation in the proposed scenario. During implementation, users must monitor the project as it progresses and modify processes, make changes and solve problems. To this end, they need information in real time, along with warning systems and specifi c indicators that enable early detection of mistakes. At this stage, the user s questions are focused on the actual situation and on the short term. Information is essential for the selection, design and implementation of a strategy. Based on information, users can defi ne the weak points in the process and capitalize on the strengths resulting in improvements. During this phase, questions can vary widely but are always based on actual implementation and particular information. In addition to these three phases of the business process, it is essential to deal with the R&D&i Management and Marketing, involving different segments of users with different behavior and information requirements. Management users view the key aspects of the company from a global perspective and, consequently, require a high level of data aggregation. Their questions refer to various Key Performance Indicators related to operative, economic and marketing factors. Graphical interpretation allows fast, intuitive understanding of the way in which a situation is developing. R&D&i Marketing users pay special attention to their customer s needs. They study customers, segmentation processes, campaigns, product analysis, market and niche market research, and research and development. Therefore, they need an enormous amount of data addressing their wide range of questions regarding the competition, the customer, the product and the supply chain, in order to adapt to the reality of the market In this paper, we speak to the fl exibility and capabilities necessary in the design and implementation of a Business Intelligence technology that successfully serves all of these corporate needs. White paper on Business Intelligence 02

5 Business Intelligence can be defined as a collection of tools and processes that helps users make decisions

6 Business Intelligence technology at the customer s service CIF (Corporate Information Factory) architecture With the aim of giving an answer to the needs of the customer through Business Intelligence Technology it is necessary to understand what the CIF (Corporate Information Factory) Architecture consists of: the Data Warehouse, the ETL processes and the Business Intelligence tools. Data warehouse This is a huge warehouse where all the company information which is susceptible to analysis can be integrated. A rigorous and exhaustive process is required in order to transform the enormous volume of data from different activities into information, this transformation being one of the key points in the success of the Data Warehouse. The design and modelling of the database is the main factor that determines the smooth running of the analytical environment. Different architectures exist to defi ne the model that should be selected and function of the foreseen criteria of usage for the information warehouse. The key aspects that characterizes the Data Warehouse are: Uniqueness of information, given that at corporate level it must be the sole point of access in the search for the same data A logical and natural view of the information directed towards the end user, creating an environment in which one can manage with ease and fl exibility The capacity to respond to any type of question: What?, When?, How?, What if...? Flexibility when faced with the changing needs of the business, given that although the fi led information does not change, the needs of the user exploiting it does Furthermore, it has a number of differentiating methodological aspects: Star or Snowfl ake models, according to the exploitation needs Special attention to the treatment of the Time dimension Denormalization of the relational model Neutral Modelling, independent from the selected Data Base Manager White paper on Business Intelligence 04

7 Data warehouse Normalization of concepts, abbreviations, formats, etc. Atomization of analytical entities Dimensional Structuring around the business processes Processes of checking and continuous auditing which guarantee the lifetime of the model The following are key areas of the Data Warehouse: Identifi cation of analysis stars Model design: Identifi cation of fact tables: Granularity, historicity, aggregation levels, volumetry Indicators and calculating formulae Identifi cation of dimension tables: Special attention to the Time dimension Identifi cation of hierarchies Indexing policies Partitioning policies White paper on Business Intelligence 05

8 ETL processes These are all the processes necessary in order to populate the Data Warehouse with information, and one of the main factors in its optimum exploitation. Through these processes the information is consolidated in various operating environments (OLTP) in a unifi ed and integrated way. These characteristics mean that a great effort is required with respect to design and implementation and a maintenance policy, given that the ETL processes can be defi ned by a certain temporary nature. The processes of duplication and information cleaning, and integrating elements of information from various origins, help the company to detect errors or incongruence in the data from operational environments. Among the differentiating methodological aspects within the ETL processes the following are found: Defi nition of the ETL policies. That is to say, criteria for process re utilization, best practices, etc. Defi nition of quality criteria: taking action against invalid values, duplications, loss of integrity references, incomplete data, etc. Identifi cation of data sources and the determination of relationships between information from different sources Analysis and identifi cation of patterns and assurance of the uniqueness of transformation criteria, including typologies, formats, etc. Mapping of origins and destinations: defi nition of transformation fl ows. Performance tests and procedure tuning Defi nition of the ETL processes via transformation workfl ows, which allow the clear identifi cation of the origin of the fi nal data and the distinct processes passed through in order to obtain it Traceability of the transformation steps. Through rewind and pause it is possible to analyze, follow-up and audit each of the critical points in the process Management of planning calendars which permits a correct execution of the different loading sequences and eliminates process obsolescence Filing history of process execution which allows the optimization of the batch execution window. White paper on Business Intelligence 06

9 Business Intelligence tools In view of the different tasks performed by users, their informational needs vary substantially. The Business Intelligence tools must allow fast, simple and comfortable access to the information contained in the Data Warehouse or in the Data Marts, while never straying from the user s point of view. Needs vary with the type of user, and it is therefore necessary to identify the user typology before deciding what technology will be selected in order to cover their needs. 1. OLAP (Online analytical processing) : multidimensional information analysis systems via non-predefi ned questions. It is directed at users with a wide knowledge of the business as it responds to all types of question (what, when, why, how). It allows the creation of reports for the operating user and also the possibility to make forecasts, simulations, and generate scenarios. It assumes that the user can act independently of the IT department. 2. Operative Reporting: systems of information use via predefi ned, formatted and fl exible questions, characterized by a highly operative and functional content; low volatility; application of parameters according to criteria such as time, organization and geography; low level of detail, execution subject to planning; distribution to users without the need to connect to the Business Intelligence platform, and the possibility of integration within nonanalytical platforms. 3. Balanced Scorecard: a system of information use which makes it possible for a fast, visual analysis of the business circumstances through the defi nition of Key Performance Indicators (KPI) based on a series of criteria of analysis (time, geography, organization, etc.) It is characterized by elements of graphical analysis (such as traffi c lights, maps and graphs); maps to help with navigation through the information, from the highest aggregation level to the lowest level of detail; agility and versatility in the use of the navigational elements (drop-down lists, radio buttons and multiselectors) and the possibility that the user can incorporate their own information (notes and comments). White paper on Business Intelligence 07

10 Business Intelligence tools 4. Data Mining: through complex processes it is possible to identify keys to business that are hidden on fi rst view and that can come to represent major profi t for the company. This tool requires a profound knowledge and analysis of information, together with very high specialization in the use of statistical techniques and logarithmic and segment information analysis. 5. Budgeting / Forecasting: they are control tools in order to know what the situation of the organization should be and they are characterized by being totally involved in order to make that better informed decision s; to substitute the rigid annual budget with a continuous plan which offers a greater capacity to provide answers; to allow operations and fi nance to interconnect and aid the generation of budgets from the bottom up, while adjustments are made from the top down. White paper on Business Intelligence 08

11 Method The methodology for the implementation of CIF architecture must follow certain guidelines 1. Defi nition of the strategy: The defi nition of the strategic framework in which the short-, medium- and long-term objectives for the proposed system are included Identifi cation of key users and the areas involved Functional, non-functional and analytical requirements Capacities analysis (volumetries, concurrencies, etc.) 2. Defi nition and development of the process: System modelling Areas of analysis Identifi cation of facts and dimensions Granularity Denormalization Study of the originating system Transformation workfl ows Design of ETL processes Study of the analytical needs Solutions design Reporting Analysis universes Balanced scorecards The management of metadata is defi ned on two levels: 1. User metadata Business dictionary and defi nition of business entities Description of business objects (metrics, hierarchies) Description of reports The capacities of the Business Intelligence tool are used 2. IT metadata (administration) Defi nition at data model level, within its own database Defi nition of the ETL processes: defi ned at the process level (in the case of ETL) and in the database dictionary tables (in the case of the model entities) White paper on Business Intelligence 09

12 Risk factors in the implementation of Business Intelligence technology The following factors which could imply a risk in the implementation of Business Intelligence technology must be kept in mind. 1. Projects which are too ambitious. It is advised to face projects in phases and provide results in each of them. 2. Difficulties with information integration. The processes of information clean-up are expensive and the results obtained never match expectations, so it is very diffi cult to explain to the user why the systems are not as good as they believed. 3. Long-term sustainability. Changes often cause a loss of homogeneity in the system. 4. Adaptation. Given that a business is a living organism that needs to change in order to survive, the decisions related to changes are taken through the Business Intelligence systems which must be constantly adapting to change. 5. Speed of implementation. A solution whose implementation takes more than three months is ineffective to the user. For this reason it is recommended to shorten the times of the fi rst implementations and defi ne a gradual development in the execution of the remaining objectives. 6. Loss of perspective regarding the needs of the users. The user must be provided with the tool most compatible with their job, instead of the tool most compatible with the IT department. 7. Dependency. Dependency on the IT department is to be avoided by providing compatible tools, a measure which will also avoid disproportionate costs. White paper on Business Intelligence 10

13 Schneider Electric services Schneider Electric Business Intelligence services include: Business Intelligence competence centre. A highly productive environment for the development of Business Intelligence which provides technology and methodology skills to help organizations and the customers with the defi nition of BI policies. Responsible Projects. Contracting a closed project with a reach perfectly defi ned by deliverables, costs and fi xed and defi ned terms laid out in a project plan. Remote assistance. Technical assistance from the offi ces of Schneider Electric. A direct connection with the customer s server or development in the server itself. Product Portfolio. Market solutions which can be adjusted based on the idiosyncrasies of customer, oriented to the product and easy to implement. Technical assistance. The provision of our own people to work with the customer in his offi ce in order to provide specialized capacities, to address a specifi c need or for longer periods of time. White paper on Business Intelligence 11

14 Schneider Electric success stories Described below are some of the Schneider Electric success stories in the implementation of its Business Intelligence technology: 1. Project development with Powercenter and Business Objects for Isban including: Means of payment Data Mart Mortgage holder Data Mart Account Balance Test Model for Abbey Bank PMO Data Mart (Project Management Offi ce) MIS NEWCO Project, migration and integration of data with Powercenter Financial consolidation Digital signature integration Remote collaboration in projects via models of software industrialization Administration of environments and process monitoring Defi nition of methodologies Training in Powercenter 2. Implementation of the BI platform for Parques Reunidos: Implementation of Data Warehouse (Illuminate) Training X Cognos version 8 projects Projects for migration from Analysis Service Training of users in the Cognos and Illuminate tools Implementation of the Cognos 8 corporative reporting platform Implementation of the Planning and Forecasting module 3. Iberia migration project: Administration, development and maintenance of metadata Development of new analyses and reports User support and incidents Maintenance of the loading process (Oracle warehouse builder) New balanced scorecard developments Balanced scorecard development with OBI 4. Audit of the implentation and performance of the Data Warehouse for the Hospital L Horta Manises: Audit of data models Audit of the metadata level of the model Development with Cognos 8 Defi nition of Best practices 5. Definition of reports and balanced scorecards for J. García Carrión: Defi nition of logistics management commercial indicators Defi nition of sales management commercial indicators Elaboration of the Sales Dossier for general management 6. Analysis of the implantation and performance of the Powercenter tool for Caser Seguros: Audit and tuning of Powercenter processes Audit of methodology and administration of Powercenter White paper on Business Intelligence 12

15 Schneider Electric success stories 7. Analysis and exploitation of DWH financial services for Carrefour: 11. Development of Balanced Scorecard for B:SM (Barcelona de Serveis Municipals): Development of indicators Maintenance and evolution of the Data Warehouse Model optimization 8. Analysis and development of the ETL processes for Arias: Development of the ETL processes from the ERP Defi nition and development of indicators Modelling 9. Analysis and development of the ETL processes (Powercenter) for Sage: Balanced scorecard for payroll and human resources departments Development of reports with DC-Reporting Design and development of Data Mart with SQL Server 12. Balanced scorecard for Financial Management with Apesoft for Grupo Abades: Data Warehouse analysis Database concepts and calculation criteria check Training for developers and users ETL processes for medium-sized businesses (KRONOS) ETL processes for sales system (ASTEC) Sales team information exchange fi les (MDB) 10. Cognos Migration Technology for Bristol- Myers Squibb Company: 13. Definition of strategic maps for Ono Companies: Development of balanced scorecard for the departments of Ono Companies Defi nition of the architecture ETL process Requirements, planning, unit tests and user guides Intermediate and advanced user training Confi guration of a development environment 14. Design and development of a Data Warehouse for Ericsson: Design and development of the Data Warehouse Implementation of indicators ETL Processes Creation of reports, consultancies and alarms White paper on Business Intelligence 13

16 Schneider Electric success stories 15. Design and development of Data Warehouse for the Management Control department for La Razón: ETL processes Modelling of data structures Development of Reporting 16. Design and development of Data Mart Commercial Network with Apesoft for González Byass: ETL processes Analytical structure modelling Platform management 17. Implementation of a reporting system for Zena catering group: Distribution, operative and accounting information reporting 18. Implantation of Integrated Balanced Scorecard for Movie World: ETL processes Analytical structure modelling Design and development of the Balanced Scorecard White paper on Business Intelligence 14

17 Conclusion Business Intelligence in a nutshell: The strength of a company s Business Intelligence technology in meeting the informational needs of all business units of the organization is highly dependent on proper planning during defi nition and design, to assure adequate fl exibility and longevity of the architecture. Other factors bearing on the success of a Business Intelligence technology include the robustness of data analysis collection and the processing elements that act on the data. The successful Business Intelligence solution must make the tools available to employees that empower them to query, process and report the information needed. Business Intelligence technology should serve the organization, and not the other way around. A Business Intelligence competence centre provides technology and methodology skills to organizations and customers White paper on Business Intelligence 15

18 Schneider Electric C/ Valgrande, 6, Alcobendas (Madrid) (Spain) Telephone: Fax: July Schneider Electric. All rights reserved.

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