Chapter © 2012 Pearson Education, Inc. Publishing as Prentice Hall

IT landscape littered with legacy, packaged & developed applications coupled with unstructured data Uncontrolled silos of data make managing information very difficult and limit its strategic value 16-2

© 2012 Pearson Education, Inc. Publishing as Prentice Hall Increased storage capabilities Layers of “enterprise” solutions Multiple groups managing data Ownership issues Short term workarounds 16-3

© 2012 Pearson Education, Inc. Publishing as Prentice Hall 16-4 Independent process that describes, owns & manages core business data entities Ensures consistency accuracy of these data by providing a single set of guidelines for management, thereby creates common view of key data

© 2012 Pearson Education, Inc. Publishing as Prentice Hall 16-5 Information Delivery Data Warehouse Master Data Store Data Quality Data Integration Metadata Data Management MDM IM Strategy & Principles Enterprise Architecture

© 2012 Pearson Education, Inc. Publishing as Prentice Hall Information Management (IM) covers all forms of info needed & produced by business IM strategy & principles structure, secure & improve information assets provide the context in which MDM is accomplished 16-6

© 2012 Pearson Education, Inc. Publishing as Prentice Hall IM strategy & principles should be important contributors to enterprise architecture Information architecture should be as separate as possible. Establishment of a dialogue & discipline for core corporate data provides the highest value 16-7

© 2012 Pearson Education, Inc. Publishing as Prentice Hall Data management (DM) is the critical work of making decisions about data Information stewards responsible for DM check accuracy, timelines, life cycle & redundancy of data MDM subset of DM that focusing on core data 16-8

© 2012 Pearson Education, Inc. Publishing as Prentice Hall Data quality aims to ensure data correct, complete, current & consistent Possible to have data quality without DM, but not possible to have data DM without data quality MDM efforts focus costs & challenges of data quality on core data 16-9

© 2012 Pearson Education, Inc. Publishing as Prentice Hall Goal of data integration Create data warehouse as credible source of integrated info Data integration serves two purposes: Enables data to be combined & collected in a warehouse Consolidates data not deemed to be core, but which are created & updated by several applications 16-10

© 2012 Pearson Education, Inc. Publishing as Prentice Hall A single source of a company’s data provides: Better information Cost savings thru improved quality Improved business capabilities in better supporting customers Improved technical capabilities by reducing redundancy & data integration 16-11

© 2012 Pearson Education, Inc. Publishing as Prentice Hall Develop an enterprise information policy Delineate principles for corporate data objectives, data ownership & accountability, privacy, security & risk management Business ownership Stakeholders must be involved in MDM or political problems may ensue Governance Establish cross-functional, collaborative IT & business data governance process MDM can’t be sustained without governance Role of IT DM primarily nontechnical problem; however technology & IT staff play important roles: IT staff has skills to develop a data strategy, model data assess applications Technology maintains data models & repositories 16-12

© 2012 Pearson Education, Inc. Publishing as Prentice Hall MDM is wrapper for concepts & issues have been afflicting IT for long time MDM initiative needs thorough planning and incremental approach: Identify some small, quick wins Focus efforts on one type of data Learn with the business how to manage process Develop & continually revisit & info roadmap & strategy 16-13