IPL Service Definition - Master Data Management Service

Transcription

1 IPL Proposal IPL Service Definition - Master Data Management Service Project: Date: 16th Dec 2014 Issue Number: Issue 1 Customer: Crown Commercial Service Page 1 of 7

2 IPL Information Processing Limited 2014 Contents 1. Master Data Management 3 2. Detailed Service Definition Master Data Environment 4 Figure 1 IPL's Enterprise Architecture IPL Master Data Method 5 3. Features and Benefits of the Master Data Management service 6 4. Keywords 6 Page 2 of 7

3 1. Master Data Management IPL offers its Master Data Management (MDM) methodology: a carefully designed module-based framework necessary for successful MDM initiatives. This method defines the foundation for managing master data entities, including the design of the enterprise data model, required data governance structures for efficient stewardship, and the evaluation, implementation and deployment of solution capabilities. MDM is one of the core IPL data management principles necessary for the effective management of a single subject area, such as customer, product, location, account or organisation. Our consultants have been providing Business Intelligence, Process consulting and Master Data Management (MDM) for over 15 years to a wide variety of both Public and Private Sector clients. Our Consultants are data professionals with significant information management expertise and possess certifications from renowned data organisations such as DAMA International. We work closely with customers to assist in the selection of the perfect fit for MDM. IPL can assist with the implementation of any aspect of the Master Data Method, preparation of MDM solution RFI/RFPs through to final vendor selection. Page 3 of 7

4 2. Detailed Service Definition 2.1. Master Data Environment MDM is the authoritative, reliable foundation for data used across many applications and constituencies within an organisation, with the goal to provide a single view of the truth no matter where it resides. MDM processes must be based on ISO standards to allow the management of dynamic business driven data structures. Associated reference data and metadata must be centrally managed via MDM tools and appropriate governance mechanisms to offer consistency across all organisational product and service lines. The following diagram Figure 1 is a schematic showing the various components that make up the Master Data Environment. Source: IPL Figure 1 IPL's Enterprise Architecture The Data Sourcing element is used to source master data from the Systems of Entry and existing Systems of Record to perform data integration, harmonisation and cleansing. The Data Sourcing Element makes use of the Data Quality element in order to standardise, de-duplicate and enrich the master data. A repository (or virtual repository) element serves as the System of Record. In other words, the repository (or virtual repository) contains the organisation s best information about each critical business entity. The User Interface element enables Data Stewards to monitor the quality of master data, and to resolve conflicts where necessary. Tools that are adopted to fulfil the Data Sourcing, System of Record and User Interface functions may vary from one Master Data Subject Area to another. The Meta-data Catalogue contains the published catalogue of meta-data associated with the contents of the repository (plus meta-data associated with other systems). The Data Distribution element is used to distribute master data to consuming systems. It is also used to distribute meta-data to consuming systems. The Metadata Catalogue and the Data Distribution element are likely to be common across all Master Data Subject Areas. Page 4 of 7

5 2.2. IPL Master Data Method As part of the IPL method, a Data Asset Plan (DAP) will be produced for each identified Subject Area. This will identify the data subject area, data owner and outline business information requirements. Further specific deliverables resulting from the production of a DAP will form the input required for execution of the Master Data Method. The Master Data Method is split into a number of modules; each module into a number of activities, and each activity, optionally, consists of a number of steps. An increasing level of detail is associated with each breakdown. A summary of the content of individual modules is presented in the table below: Module No. N/A Module Name Data Asset Plan Summary Defines the scope of the Master Data Subject Area, and defines the broad scope of the project to bring that subject area under Master Data Management, in terms of systems, business processes etc. 1 Governance Develop and deliver the service level agreement, operational guidelines and organisational structure for the subject area, and all associated training. This module runs in parallel with all other modules. 2 Business Requirements Conduct workshop(s) with key process contacts to identify concepts and associated definitions in the subject area. Use the results to develop and extend the outline business requirements from the Data Asset Plan. 3 Source Systems Analysis Refine inventory of Systems of Entry and consuming systems and collect additional required system details. Collect and catalogue relevant meta-data associated with the identified systems and conduct data quality assessments against all potential Systems of Entry. 4 Data Modelling Conduct workshop(s) with system contacts to agree attributes, rules, definitions and prioritisation for use in the logical data model. Review the conceptual data model in the Data Asset Plan and either develop a bespoke logical model or extend the identified industry standard model identified in the scoping document. 5 Select MDM Architecture and Toolset Define the MDM Architecture to be used, and select tools to provide Data Integration, Data Quality and Master Data Management capabilities 6 System Requirements Specify the data mappings and rules between attributes in the Systems of Entry and attributes in the System of Record. Specify the data mappings and rules between attributes in System of Record and attributes in the consuming systems. Analyse and record details of the impact on and necessary changes to all identified systems. 7 Build Data Sourcing Components Develop the physical data model and plan, load and test the integration Master Data Environment. Plan and perform the initial population of the System of Record. 8 Build Data Distribution Components Develop the components that make data available to consuming systems. This can include components to update the Systems of Entry with enriched data. 9 Build Data Quality Components Develop components to monitor Data Quality, and develop Data Enrichment web services to be used by Systems of Entry in order to help end-users to improve data quality at the point of data entry. Page 5 of 7

6 Module No. Module Name Summary 10 Build User Interfaces and Workflows Develop user interfaces and associated workflows for Data Stewards. 11 End-to-end Testing Test the behaviour of the Master Data Environment with respect to the subject area, testing each data concept throughout its lifecycle from creation through to retirement. 12 Acceptance Testing and Deployment Define and execute the deployment plan for the Master Data Subject Area, and perform acceptance testing. 13 Project Review Review all aspects of the project, update the Master Data Method and archive all documentation for future reference. These modules may not be strictly executed sequentially as per the flow indicated by the method overview, however will be tailored to suit both the requirements and the data subject area. Some degree of iteration and parallelism is expected to take place between modules. The method provides a series of templates and recommended deliverables. It builds upon IPL s substantial body of knowledge and proven success in delivering Master Data solutions. 3. Features and Benefits of the Master Data Management service Improve data quality and integration across data sources, applications and technologies. [DAMA] > Provide a consolidated, 360-degree view of information about important business parties, roles and products, particularly for more effective reporting and analytics. [DAMA] Improve data quality > Ensure compliance with data quality standards (completeness, validity, consistency, timeliness and accuracy) Improve the integration of data from different sources > Allowing data to be combined for reporting and analysis ( Analytic MDM ) > Reducing data inconsistencies within systems and between different systems ( Operational MDM ) 4. Keywords Master data management, MDM, system of record, Master data method, data asset plan, master data environment, data sourcing, data quality, enterprise data modelling, single view, user interface, system of entry, data governance, data steward, meta-data, data distribution, subject area. Page 6 of 7

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