Master Data Management (MDM) and Data Quality with SAP Information Steward Kevin Hutto Consultancy by Kingfisher

Transcription

1 Master Data Management (MDM) and Data Quality with SAP Information Steward Kevin Hutto Consultancy by Kingfisher Produced by Wellesley Information Services, LLC, publisher of SAPinsider Wellesley Information Services. All rights reserved.

2 In This Session Learn how Enterprise Information Management helps to empower data stewards with a single environment to discover, define, monitor, and improve the quality of their enterprise data asset Use Information Steward to monitor the quality of data Drive information governance and standardizations for Master Data Management (MDM) enforced by Information Steward tools such as scorecards, dashboards, and data quality cleansing packages Link data quality metrics to drive business data ownership 2

3 What We ll Cover Information Steward and Enterprise Information Management Overview Metadata Management Overview and Demo Data Insight Quality Scorecards Data Cleansing and Data Services Wrap-up 3

4 Data Issues The causes of poor data can be hard to identify and fix Data resides in incompatible sources No consistent, repeatable way to measure data quality No ability to analyze data dependencies across systems No clear strategy for improving data quality 4

5 Data Governance Objectives Get full visibility into the reliability, origins, and lineage of your data Reduce the risk of propagating bad data with comprehensive data profiling Consolidate, integrate, and audit your metadata to create a central glossary Determine the origin of data quality problems and evaluate their net impact Monitor data quality continuously by validating rules against data sources 5

6 Next Generation DI Report usiness and technical people speak different languages 60% Lack of a business-friendly view of data 52% nt tools lack functions for business people to use 41% Collaboration is not an issue for us 17% Information Steward addresses these challenges 0% 15% 30% 45% 60% 75% Source: TDWI Checklist Report // Taking IT to the Next Level: Next-Generation Data Integration 6

7 Enterprise Information Management Before After Data A Quality D B C Data Integration Master Data Management GOVERN Data Modeling Content Management Information Lifecycle Management Source: SAP 7

8 Single Solution, Multiple Benefits Discover Assess Define Monitor Improve Understand Overall Rules and Quality Data quality and catalog data quality ownership continuously and enterprise governance data Empower business and IT users with a single environment to manage the quality of their enterprise data assets Source: SAP 8

9 Data Integration and Quality Opportunities CIOs struggle to empower knowledge workers to make better decisions and improve business operations 46% of organizations cited the need for better access and analysis of real-time data as one of the top data management objectives in next 12 months $8,200,000 is the annual average estimated loss due to data quality issues Source: TDWI Checklist Report // Taking IT to the Next Level: Next-Generation Data Integration 9

10 Why Use Information Steward? Adopt one solution for data stewardship Empower Business Users Bridge gap between business and IT with collaborative solution for driving information management initiatives Govern Enterprise Information Enable effective data governance through combined data profiling, metadata management, and data quality monitoring Improve Information Transparency Give instant visibility into data quality levels and origins with endto-end impact analysis and data lineage 10

11 Information Steward Solution Data Profiling DQ Monitoring Metadata Analysis Cleansing Rules Business Term Taxonomy Source: SAP 11

12 What We ll Cover Information Steward and Enterprise Information Management Overview Metadata Management Overview and Demo Data Insight Quality Scorecards Data Cleansing and Data Services Wrap-up 12

13 Metadata Management Information You Can Trust? How will this change impact my reports? R1 R2 EDW R3 Rn Transformation / Enrichment / Standardization? Where did this number come from? 13

14 Full lineage map from source to report 14

15 Metapedia Overview Promote proactive data governance with a common understanding and agreement on concepts and terms Enable users to understand data used in BI with business user descriptions Central location for defining standard business vocabulary Organize business terms into categories that match with lines of business 15

16 Metapedia Demo 16

17 Information Steward Overview 17

18 What We ll Cover Information Steward and Enterprise Information Management Overview Metadata Management Overview and Demo Data Insight Quality Scorecards Data Cleansing and Data Services Wrap-up 18

19 Data Insight: Scorecard Full Scorecard views of your information 19

20 Categorized view with performance indication against set thresholds 20

21 Validation of data to point to rules not meeting criteria. 21

22 Ability to create and validate rules within the same interface. 22

23 What We ll Cover Information Steward and Enterprise Information Management Overview Metadata Management Overview and Demo Data Insight Quality Scorecards Data Cleansing and Data Services Wrap-up 23

24 Quality Scorecards Central screen for all related workspaces and scorecards 24

25 Quality Scorecards Quality of data over time 25

26 Rules to be added for validation of information 26

27 Validation Rules Standardize the information based on patterns 27

28 Full view of all failed data from rules in place 28

29 What We ll Cover Information Steward and Enterprise Information Management Overview Metadata Management Overview and Demo Data Insight Quality Scorecards Data Cleansing and Data Services Wrap-up 29

30 Create Cleansing Packages Jeff/Jeffrey Smith can be standardized 30

31 Data Standardization McDonalds/McDonald same company and classified as FIRM. 31

32 Cleansing Package Builder Define Standards and variations on free-form text 32

33 Data Services: Cleansing Workflow builds in Data Services to cleanse information as it is loaded. Advanced Workflow 33

34 Data Quality: Transformations Full use of data quality transforms 34

35 Enterprise Information Management Before After Data A Quality D B C Data Integration Master Data Management GOVERN Data Modeling Content Management Information Lifecycle Management Source: SAP 35

36 What We ll Cover Information Steward and Enterprise Information Management Overview Metadata Management Overview and Demo Data Insight Quality Scorecards Data Cleansing and Data Services Wrap-up 36

37 Where to Find More Information Comprehensive Product Tutorials Information from the Enterprise Information Management Community Documentation and deployment documents 37

38 7 Key Points to Take Home Regulations across all industries are driving data governance Enterprise Information Management should be a priority to all organizations Good data is key to any Business Intelligence solution Lineage of data to determine impact analysis on analytics can save valuable time Visibility into the data to decide if corrective action is critical Tools have generally been IT-driven, but now need input from the business Business terminology should be cohesive and something easily understood across the organization 38

39 Your Turn! How to contact me: Kevin Hutto Please remember to complete your session evaluation 39

40 Disclaimer SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. All other product and service names mentioned are the trademarks of their respective companies. Wellesley Information Services is neither owned nor controlled by SAP SE. 40