Data Governance: Ensuring Trusted Data for Business Success

1. May 29, 2008. Call in at 12:55 p.m. Eastern time

2. Definition • Data used by business stakeholders to support their processes or decisions with no reservations as to its relevance, freshness, accuracy, integrity, and other previously agreed upon definitions of quality

3. Data governance is on the critical path to trusted data In order to deliver trusted data through a data quality or MDM effort: • Traditional functional and IT silos must be broken to share data across the enterprise. • The introduction and successful adoption of new processes, organizational responsibilities, and supporting technologies is a must.

4. So what exactly is data governance? • Data governance is the process by which an organization formalizes the “fiduciary” duty for the management of data assets critical to its success.

5. Agenda • Themes for defining and measuring data quality using data governance best practices • Data quality technology features and functions, and how it fits into your information architecture • Data quality market overview and segmentation • Recommendations

6. Agenda • Themes for defining and measuring data quality using data governance best practices • Data quality technology features and functions, and how it fits into your information architecture • Data quality market overview and segmentation • Recommendations

7. Trusted data initiatives like data quality and MDM offer compelling drivers . . .

8. Drivers aside, a common theme emerges Ignoring the need for trusted data is common . . . until the lack of it impacts your business.

9. Forrester data quality inquiry highlights • Insurance: “What is the history of data quality, where can data quality provide a good ROI, and what tools should be considered in that space?” • Financial services: “We are interested in your perspectives concerning (DQ Vendor) capabilities, directions, and the competitive landscape for ‘address validation’ of the kind that (DQ Vendor) provides. Are there comparable (better?) products available from competitors?” • Manufacturing: “We need to have a position on data quality frameworks to address data quality issues for large/complex application implementations, as well as ongoing data quality improvement.” • CPG: “Which tool is the best for name/address cleansing and standardization? Should we consider another tool if we need a ‘profiling’ function built in the tool to detect data fields that need cleansing in addition to name and address?”

10. MDM maturity curve: Data quality is your foundation Source: May 16, 2008, “Trends 2008: Master Data Management”

11. Accuracy. Data must be consistent with the intended goal. Completeness. Having missing or invalid data leads to problems. Integrity. Not having the expected relationships between multiple data sets intact presents data integrity issues. Hierarchal relationship accuracy. Parent-child relationships can be overlooked, leading to data quality issues. Timeliness. While more of an operational quality metric, timeliness addresses whether the delivery of data from one environment to another meets user expectations. ? Data quality: What is being measured?

12. Consistency and standardization. Delivering data that doesn’t conform to defined formats and standards can lead to chaos. Uniqueness. While data will be scattered throughout the enterprise, not all of it should be considered unique. Freshness. A different metric than timeliness, freshness focuses on the age of the data, which may have varying levels of usefulness depending on its type. Third-party enrichment. Not all data exists inside the enterprise and often must be appended with third-party information. Data quality: What is being measured?

13. Data governance enables innovation Without data governance, expect strategic data management initiatives to perform below expectations.

14. Agenda • Themes for defining and measuring data quality using data governance best practices • Data quality technology features and functions, and how it fits into your information architecture • Data quality market overview and segmentation • Recommendations

15. The role of technology in data governance Data profiling and data quality software supports business and IT stewards in: • Profiling and analyzing source data. • Defining and capturing standard definitions. • Standardizing lists of values. • Defining and implementing cleansing, standardization, validation, enrichment, and matching; and merging business rules for automatic data quality validation and remediation. • Defining and implementing exception rule parameters where manual intervention is required. Data governance is not an IT project: It is a business strategy that can be optimized with the appropriate use of enabling technologies.

16. Data quality software supports trusted data • Data quality software (DQS) provides the technology enabler for implementing many of the data quality rules and processes defined through your data governance efforts.

17. Common DQS capabilities

18. Agenda • Themes for defining and measuring data quality using data governance best practices • Data quality technology features and functions, and how it fits into your information architecture • Data quality market overview and segmentation • Recommendations

19. The data quality software market has been active.

20. Data quality market consolidation: 2000 to present

21. DQS market segmentation

22. Master data management should begin where data quality software leaves off.

23. Master data management defined Forrester defines MDM as a business capability enabling an organization to: • Identify trusted master data. MDM defines and/or derives the most trusted and unique “version” of important enterprise data (e.g., vendor, customer, product, employee, asset, material, location, etc.). • Leverage master data to improve business processes and decisions. MDM incorporates this master version of the data within functional business processes (sales, marketing, finance, support, etc.) that will provide direct benefit to employees, customers, partners, or other relevant stakeholders within an organization. • Master data alone provides little value. Hence, anticipation of how the data will be consumed by other applications or systems within the context of a business process provides the most value. MDM is not a technology space; it is a business capability enabled through the integration of multiple technologies and business processes.

24. Your MDM ecosystem is complex Source: April 28, 2008, “Making MDM And SOA Better Together”

25. Software vendors approach MDM from varying heritages Intelligent consumption BI/action frameworks Trusted data sources * InfoUSA * Lexis-Nexis * Cognos (IBM) * Acxiom * Stratature (Microsoft) * Austin-Tetra * D&B -Purisma * Oracle -Hyperion * Kalido • Business Objects (SAP) * GoldenSource Master datahub Infrastructure players End user-focused solutions * Pitney Bowes Group 1 * Oracle – Siebel * VisionWare * Initiate * DataFlux (SAS) * IBM * Siperian * Trillium * Informatica * Oracle * Orchestra *Teradata * Amalto * SAP * Silver Creek Systems * Tibco * FullTilt Enterprise apps * i2 * Sun (SeeBeyond) * GXS Data management Transactional maintenance

26. Have you considered your requirements around: Confusion reigns supreme in the MDM marketplace

27. Agenda • Themes for defining and measuring data quality using data governance best practices • Data quality technology features and functions, and how it fits into your information architecture • Data quality market overview and segmentation • Recommendations

28. Recommendations Consider data quality strategies that support enterprise demands: • Prioritize your data quality objectives by focusing on data elements supporting your most business-critical processes. • Get started with project-based data quality. • Ride the coattails of cross-enterprise data management initiatives. • Adopt data governance to allow you to evolve from project-based DQ to enterprise-class MDM.

29. Questions?

30. Thank you Rob Karel +1 650.581.3821 rkarel@forrester.com www.forrester.com