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Sap increase your return on information by focusing on data governance - managing data quality - by scott barrett

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  • 1. © 2011 SAP AG. All rights reserved. 1 Scott Barrett Senior Director │ Information Management │ SAP EMEA Return on Information: Managing Data Quality 25th September 2013 Welcome to the ‘Return on Information’ webinar series dedicated to Information Management Solutions by SAP. This is the first of four webinars, the following webinars will be on: 9th Oct: Return on Information: Managing Master Data 23rd Oct: Return on Information: Managing Content 6th Nov: Return on Information: Managing Information Lifecycles Register now › bit.ly/1b26WRY
  • 2. © 2013 SAP AG. All rights reserved. 2 What is Information Governance? Information Governance A discipline that includes people, processes, policies, and metrics for the oversight of enterprise information to improve the business value High Value Information: Optimized Business Processes Smarter Business Analytics Timely Mergers and Acquisitions Compliance with Laws and Regulations ProcessPeople Policies & Standards Metrics
  • 3. © 2013 SAP AG. All rights reserved. 3 Why Should You Care About Information Governance? SAP Solutions are uniquely integrated with SAP Business Suite and SAP BusinessObjects BI solutions Utilize entire EIM stack by providing a complete solution that can address needs at any stage of their information governance program Talk to the business by discussing business priorities and how high-quality information can help them Elevate conversation above technology discussion by showing how SAP can support information governance 1 2 3 4
  • 4. © 2013 SAP AG. All rights reserved. 4 What Aspects of Information Can Be Governed? People  Information ownership and accountability  Information access (who, when, where, what) Process  Data handling (creation, updating, deleting)  Information storing (archiving, security) Metrics  Data quality levels  Reporting (who, when, what) Policies and Standards  Data definitions  Allowable values  Information architecture Begin with the business priority that is enabled most by quality information.
  • 5. © 2013 SAP AG. All rights reserved. 5 Information Governance is a Key Pillar of the Global Transformation Program Business Model For Vodafone
  • 6. © 2013 SAP AG. All rights reserved. 6 Information Governance and MDM Transforming the Data Organization at Vodafone  Single governance process for management of SCM, Finance and HR master data  Creation of a data definition for each master data object  Enforcement of data policies and standards  Centralization of data management to Global Shared Services Organization  Establishment of Data Review Board  Transformation from free text purchasing to catalogues
  • 7. © 2013 SAP AG. All rights reserved. 7 Information Governance is a Best Practice Discipline Required for Enterprise Information Management Data Migration Data Integration for Data Warehousing Master Data Management Data Quality Management A B Before After A B C D Mergers & Acquisitions Business Analytics Business Process Efficiency Compliance EIM Initiatives (Examples)Business Goals InformationGovernance
  • 8. © 2013 SAP AG. All rights reserved. 8 SAP Information Governance Solutions Maximize the value of your enterprise information Easier information stewardship Governance analytics Business and IT collaboration Embedded in the business process Governance workflow Enrich business process with enterprise content Optimized data quality Continuous monitoring Unlock insights from unstructured data Empower the Business Govern In Process Trust Your Information
  • 9. © 2013 SAP AG. All rights reserved. 9 Govern Information in Process from the Point of Information Creation to Consumption information information Systems of Record 3rd Party Systems SAP Business Suite information information Data Warehouse Systems of Analysis Employees Other Systems Customers Other Systems Is it correct and valid? Does it meet standards? Is it a new record? Active Information Governance Is it correct and valid? Does it meet standards? Is it a new record? Passive Information Governance information information Information Creation Information Consumption
  • 10. © 2013 SAP AG. All rights reserved. 10 information information Govern Information in Process with SAP Solutions for Enterprise Information Management Systems of Record Non SAP Systems SAP Business Suite information informationx information information Data Warehouse Systems of Analysis Employees Clean data at point of user entry with SBOP DQM for SAP ERP/CRM Other Systems Clean and check for duplicates with SBOP DS Create global master data with SAP MDG Customers Other Systems SBOP DQM for Oracle or Informatica SAP NW MDM SBOP DS Measure and monitor data quality with SBOP Information Steward Clean, match, and integrate data with SBOP DS Create global master data with SAP NW MDM Legend: SBOP = SAP BusinessObjects DS = Data Services DQM = Data Quality Management NW = NetWeaver MDG = Master Data Governance SAP Solutions Information Creation Information Consumption
  • 11. © 2013 SAP AG. All rights reserved. 11 Manage Cleanse Create Monitor Integrate Optimize Quality and Consistency of Information with Marketing Leading Solutions for Data Quality and MDM Information Governance Rated as a MARKET LEADER IN DATA QUALITY AND DATA INTEGRATION by Gartner, Forrester, and TDWI SAP customers KRAFT FOODS INC., Gartner MDM Excellence Award 2009, Lexmark International 2011
  • 12. © 2013 SAP AG. All rights reserved. 12 Suppliers End ConsumerRetailerDistributorWarehousePlant Social media data for segmentation, sentiment, behavior information Geo spatial data identifies best routes/ number of trucks/timing Warehouses use bar code data to speed shipping Plant operators use carbon input data to identify compliance Manage suppliers relationships to optimize purchasing Decrease COGS Decrease days sales outstanding Increase promotion effectiveness Decrease compliance issues Decrease time to delivery Benefits of End-to-End Information Governance throughout the Business Process
  • 13. © 2013 SAP AG. All rights reserved. 13 Considering what the Experts Say Lower Profits Poor Customer Relations Low Productivity Average organization loses $8.2 million annually through poor data quality. Gartner “ “ 55% of all CRM projects failed to meet software customers' expectations. Poor customer data is one of the biggest factors. Gartner “ “ 50% to 70% of ERP implementations are reported as “challenged” in part due to data integrity and/or data accuracy problems. =Adaptive Growth, Inc. “ “
  • 14. © 2013 SAP AG. All rights reserved. 14 How does Poor Data Quality Impact Business Process? Problems experienced when data quality practices are not followed  Difficult to determine the right recipients for marketing campaigns  Inaccurate order information causes delayed or lost shipments and lower customer satisfaction  Sales representatives are not able to identify relevant accounts  Costs are high due to account duplications, while response rates are low  Potential customers are annoyed by redundant mails, e-mails and phone calls  Reporting uses wrong data and this leads to wrong conclusions/decisions
  • 15. © 2013 SAP AG. All rights reserved. 15 What Kind of Data Are We Dealing with? Materials/ Products Customers Financial Business Partners Suppliers Distributors Retailers “What kind of data is most susceptible to data quality problems?” Enterprise Information
  • 16. © 2013 SAP AG. All rights reserved. 16 What Are the Sources of Bad Data Problems? Enterprise Information Employee Data Entry Customer Self- Service Data Migration Projects IT Application Updates Purchased or Rented External Data
  • 17. © 2013 SAP AG. All rights reserved. 17 People & Process Maturity ValueBuilding a Roadmap for Data Quality is Key for Success 1. Data READINESS 4. Data GOVERNANCE Understand what data assets you have and how they are being used Deliver trusted information repeatable and reliably at the right form, to the right place at the right time 2. Data INTEGRATION & CLEANSING 3. Data CONSOLIDATION Understand Govern Consolidate Understand Consolidate Understand Understand Consolidate diverse master data landscapes and increase trust and reliability in information Technology enabling people to implement a repeatable process to manage the use, quality and lifecycle of information Cleanse Cleanse Cleanse
  • 18. © 2013 SAP AG. All rights reserved. 18 Data Quality Provides Value Throughout Portfolio maps to the People and Process Maturity END-TO-END Data Management Full Enterprise COVERAGE 1. Data READINESS 4. Data GOVERNANCE 2. Data INTEGRATION & CLEANSING 3. Data CONSOLIDATION Information Steward Enterprise MDM MDM Information Steward MDM Information Steward Information Steward Data Quality Data Quality Data Quality People & Process Maturity Value
  • 19. © 2013 SAP AG. All rights reserved. 19 The Data Quality Framework Continuous Monitoring CONTINUOUS MONITORING MEASURE ANALYZE PARSE STANDARDIZE CORRECT ENHANCE MATCH CONSOLIDATE Data Assessment Enhance Data Cleansing Match & Consolidate YOUR DATA
  • 20. © 2013 SAP AG. All rights reserved. 20 Gartner Magic Quadrant for Data Quality Tools www.sap.com/campaign/na/usa/Gartner_Data_Quality/index.html SAP ranked as a LEADER in the “Gartner Magic Quadrant for Data Quality Tools” for the 7th consecutive year.
  • 21. © 2013 SAP AG. All rights reserved. 21 Goal: Achieving Pristine Data Quality with SAP Bob oldstead 175 Riviington Ave suite 2 Manhatten, new yourk 10002 INPUT PARSE1 First Name: Bob Last Name: Oldstead AddressL1: 175 Rivington Ave AddressL2: Suite 2 City: Manhattan State: New York Zip Code: 10002 CORRECT3 First Name: Robert MiddleName: E Last Name: Oldstead AddressL1: 175 Rivington Ave AddressL2: Suite 2 City: Manhattan State: New York Zip Code: 10002 Phone: (847) 442-5555 Email: robo@tcabuilders.com ENHANCE First Name: Robert MiddleName: E Last Name: Oldstead AddressL1: 175 Rivington Ave AddressL2: Suite 2 City: Manhattan State: New York Zip Code: 10002-2517 Longitude: 40.7325525 Latitude: -74.004970 Phone: (847) 442-5555 Email: robo@tcabuilders.com 6 MATCH Robert E. Oldstead Manhatten, NY 10002 robo@tcabuilders.co m 847 442-5555 4 CONSOLIDATE STANDARSIZE2 5
  • 22. © 2013 SAP AG. All rights reserved. 22 Technologies Required for Effective Information Governance Technologies EIM Solutions Extract transform load (ETL) SAP BusinessObjects Data Services Data quality SAP BusinessObjects Data Services Data profiling SAP BusinessObjects Information Steward Metadata Management SAP BusinessObjects Information Steward Workflow management SAP MDG Business rules engine SAP MDG Master data management SAP MDG BI dashboards and scorecards SAP BusinessObjects Information Steward or SAP BusinessObjects Dashboards
  • 23. © 2013 SAP AG. All rights reserved. 23 SAP Business Objects Data Services Data integration, data quality, stewardship, and text analytics Move Improve Govern Unlock One Runtime Architecture & Services Business UI (Information Steward) Unified Metadata Technical UI (Data Services) SAP BusinessObjects Data Services 4.0 ETL Data Quality Profiling Text Analytics One Administration Environment (Scheduling, Security, User Management) One Set of Source/Target Connectors
  • 24. © 2013 SAP AG. All rights reserved. 24 Monitor Quality continuously Improve Data quality and governance SAP BusinessObjects Information Steward Collaborative environment for your IT and business users Empower business and IT users with a single environment to manage the quality of their enterprise data assets Discover Understand and catalog enterprise data Assess Overall data quality Define Rules and ownership
  • 25. © 2013 SAP AG. All rights reserved. 25 Validation Rule for Business Process Centric Data Profiling, Advanced Data Profiling System landscape and architecture metadata cataloging (Dependency analysis, lineage etc.) DQM Functionality for party and non-party data (Create/MaintainCle ansing Packages) Comprehensive Business Taxonomy (Search, tagging, Ownership etc.) Data Profiling DQ Monitoring Metadata Analysis Cleansing Rules Business Term Taxonomy Single Solution with Integrated Data Stewardship Capabilities
  • 26. © 2013 SAP AG. All rights reserved. 26 Master Data is the “DNA” of the Enterprise Enterprise MDM  Master data domains across Business Applications and Business Analytics  Line of Business specific and cross Line of Business processes  Cross Value Chain in Business Network Enterprise Master Data Procurement Supply Chain CRM PLM Finance Manufacturing
  • 27. © 2013 SAP AG. All rights reserved. 27 Lack of Single View Hinders Business Decisions, Business Processes, and Business Transformation Lack of single view and data quality New systems  Complexity to consolidate existing and new systems Reporting/analytics  Lack of trusted decisions Business partners  Lack of business insight and collaboration Lines of business  Inefficient business processes Cloud  Complexity to bridge cloud and corporate master data Business Impact  Diminished revenue and service  Uncontrolled costs  Lack of compliance
  • 28. © 2013 SAP AG. All rights reserved. 28 Enterprise MDM Capabilities Centralized ownership LoB Customer LoB Procurement other LoB Decentralized ownership New systems Reporting/analytics Business partners Cloud LoB Finance LoB Manufacturing Enterprise Master Data Consolidation and high velocity integration Central creation Data quality stewardship Data quality continuous monitoring
  • 29. © 2013 SAP AG. All rights reserved. 29 Seamlessly Support Key Data Quality Best Practices Data Quality Management, version for SAP Solutions (DQM for SAP) Improve the quality of customer, partner, and supplier data from within SAP ERP, SAP CRM, and SAP MDG applications by utilizing SAP DQM’s address cleansing, matching and de-duplication capabilities EnforceVerify Real-time data validation (address reference data available for 230+ countries) Batch data cleansing (ensure ongoing data accuracy and completeness) Pre-built best practice data quality templates (tuned specifically for SAP ERP, SAP CRM, and SAP MDG apps) Detect pre-existing records during data loads (Prospect lists, trade show leads, webinars, etc.) Improve Duplicate record detection (Potential duplicates are identified and tracked for best record resolution) Clearer, more efficient customer segmentation (with the ability to match on marketing attributes for SAP CRM)
  • 30. © 2013 SAP AG. All rights reserved. 30 Automatically Completes and Corrects Entries, then Presents to User for Approval In-complete Address entered. Automatic Accurate address returned in each address component
  • 31. © 2013 SAP AG. All rights reserved. 31 Previously Unknown Entries are Identified, Before Another Duplicate can be Created List of pre-existing entries are presented
  • 32. © 2013 SAP AG. All rights reserved. 32 9th Oct: Return on Information: Managing Master Data A single version of the truth is critical when governing the data in your operational systems – whether ERP, CRM or HCM. Learn how to improve efficiency and reduce costs across six key data domains: Customer, Supplier, Finance, Material, Vendor, and Employee. Register now › bit.ly/1b26WRY 23rd Oct: Return on Information: Managing Content Seventy per cent of all data in your organization resides outside of databases. By governing unstructured content such as documents, invoices, notes and images from directly within your Operational System, you can reduce costs while boosting productivity and user adoption. Register now › bit.ly/18nbeRF 6th Nov: Return on Information: Managing Information Lifecycles Information Governance is a cradle-to-the-grave exercise. Learn how to understand the data in legacy systems, migrate it into your Operational System, and decommission legacy systems. Also discover how to effectively meet regulatory and statutory requirements around data retention and removal, by managing the entire information lifecycle. Register now › bit.ly/1dGeXgw Stay connected Exchange with experts on communities and social networks. SAP Community › IM Channel › @SAPEIM › SAP › SAP on LinkedIn ›