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DATA GOVERNANCE & DATA QUALITY PROGRAMS BETTER OUTCOMES, WORTHWHILE CHANGE, FOR ANY ORGANIZATION 10/16/2013 + by Deepak Bhaskar.

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Presentation on theme: "DATA GOVERNANCE & DATA QUALITY PROGRAMS BETTER OUTCOMES, WORTHWHILE CHANGE, FOR ANY ORGANIZATION 10/16/2013 + by Deepak Bhaskar."— Presentation transcript:

1 DATA GOVERNANCE & DATA QUALITY PROGRAMS BETTER OUTCOMES, WORTHWHILE CHANGE, FOR ANY ORGANIZATION 10/16/2013 + by Deepak Bhaskar

2 AGENDA

3  Introduction  Speaker Bio  Company introduction  Data issues for our Business:  Challenge 1  Batch mode Data cleansing: Centralizing commerce data in an ERP  DQP in ERP Implementation (Data Discover Profiling & DQ Tool)  Challenge 2  Real Time Data cleansing: Cloud Commerce Billing/Shipping Address Errors  DQP in Real Time Address Validation & Cleansing (DQ Tool & Postal dir.)  Further Recommendations  Conclusion: Digital River Data Governance best practices 3

4 SPEAKER BIO: 4 IntroductionBusiness Challenge 1Business Challenge 2RecommendationsConclusion  At Digital River – 10+ years  Other roles held:  Manager, Enterprise Data Quality, (2008-12)  Sr. Strategic Database Analyst, Strategic Marketing (2005-08)  Sr. Software Test Engineer, Quality Assurance (2003-05)  Roles held in prior to Digital River include:  Lead Test Consultant, (Gelco Info. Network, now Concur Technologies)  DBA, (Eschelon Telecom, now Integra Telecom)  DBA, Software Developer, Sr. Test Engineer (techies.com)  Education & Training:  ACE Leadership Series; Minnesota High Tech Association  Business Strategy: Competitive Advantage; Johnson School of Management, Cornell University  MBA, International Business; Keller School of Management, DeVry University  BSEE, Electrical Engineering: Microelectronics & Telecoms; Minnesota State University DEEPAK BHASKAR Sr. Manager, Data Governance, Trillium Product. Governance and Compliance.

5 COMPANY OVERVIEW DIGITAL RIVER

6 6 Generating Revenue in Virtually Every Country on the Planet 38 Patents Issued in Commerce, Marketing and Payments Technology Pioneer, Founded in 1994 2012 FINANCIAL HIGHLIGHTS Revenue $386 MILLION R&D Investment $64 MILLION Strong Financial Balance Sheet NASDAQ: DRIV Invest 3 Million Hours Per Year Focused on Growing Our Clients Revenue Who We AreOur FocusOur PassionExperience Managing Over $22 Billion in Annual Online Transactions Innovation

7 SIMPILFY THE COMPLEX Shopping Cart Export Compliance Global Capabilities Payments, Multi-lingual Advanced Business Models Subs, Rentals, Points, etc. Tax & Fraud Management Compliance (PCI, SOX, SAS, Export) Marketing and Demand Gen Store Front API’s & Integrations We manage the complexity and risk on a global scale to enable a great user experience Who We AreOur FocusOur PassionExperienceInnovation 7

8 UNMATCHED GLOBAL EXPERIENCE AND REACH 8 40 30 31 15 localized payment methods transaction currencies site display languages offices across the globe languages in customer service Minneapolis Aliso Viejo Pittsburgh Portland Provo San Diego Seattle Cologne London Luxembourg São Paulo Shanghai Shannon Stockholm Taipei Tokyo Vienna Who We AreOur FocusOur PassionExperienceInnovation

9 DIGITAL RIVER PROMISE 9 Unmatched speed to market 19 years of experience Why world class companies put their trust in Digital River 1,400+ e-commerce experts worldwide 3 million hours a year invested in our client success Deep understanding of consumer psychology and online behaviors Manage more than $22 billion in online transactions Global Demand marketing experts Over 100 third party relationships Most complete fraud detection tools in the industry Who We AreOur FocusOur PassionExperience “Digital River has been with us step-by-step as we’ve launched online stores. Their technology supports our online commerce capabilities in North America, Europe and Asia, and their marketing solutions help us acquire and retain new customers every day.” - Lance Binley, Logitech Vice President of Digital and E-Commerce Innovation

10 SERVICES 10 Store Architecture Store Content Local Fulfillment Customer Service Subscriptions Reporting & Analytics Locale Merchandising Email Marketing Search Optimization Affiliate Marketing Brand Development Currency Pricing Local/VAT Tax Support Global Processing Transaction Routing Fraud Screening Site Optimization WORLDWIDE PAYMENTS WORLDWIDE COMMERCE WORLDWIDE MARKETING Who We AreOur FocusOur PassionExperience Merchant Services A flexible, expandable e-commerce ecosystem perfectly suited to the needs of your business. YOUR CUSTOM ECOSYSTEM Innovation

11 PERFORMANCE MARKETING Who We AreOur FocusOur PassionExperience 11 Marketing expertise to acquire and retain customers. Search Engine Marketing services to help create a strategy that maximizes your pay-per-click ad spend Display Advertising to drive “eyeballs” to your sites and create the brand awareness needed to compete for market share Affiliate Programs and Networks to drive revenue through a community of pay-for- performance publishers Site Optimization to make sure customers find their way to your site Email Programs that match messages to your customers digital body language Advanced Analytics to provide the data points needed to manage key performance indicators Innovation

12 SOFTWARE & SERVICES GAMES AND ENTERTAINMENT WORLD-CLASS CUSTOMERS 12 TRAVEL E-TAIL EDUCATION Who We AreOur FocusOur PassionExperience Consumer Electronics Innovation

13 OPEN. MODULAR. ECOSYSTEM 13 Who We AreOur FocusOur PassionExperienceInnovation

14 BATCH MODE DATA CLEANSING: CENTRALIZING COMMERCE DATA BUSINESS CHALLENGE 1

15 EARLY YEARS (MID-90’S): SINGLE E-COMMERCE PLATFORM 15 IntroductionBusiness Challenge 1Business Challenge 2RecommendationsConclusion  At the heart of the web hosting business:  The order checkout workflow, which consists of:  Store homepage  Product detail Page  Shopping cart page  Bill to page  Ship to page  Payment processing page  Order confirmation page  Thank you page  Invoice page

16 TODAY: MANY CLOUD COMMERCE PLATFORMS (A RESULT OF ACQUISITIONS) 16 IntroductionBusiness Challenge 1Business Challenge 2RecommendationsConclusion E-Com1 E-Com2 E-Com3 E-Com4 E-Com5 E-Com6 E-Com7 E-Com8

17 BATCH MODE DATA CLEANSING: CENTRALIZING COMMERCE DATA Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion  In 2008 Digital River was dealing with Multiple commerce platforms  Cons:  Inefficient use of Developers and Functional teams  Confusion around definition of common terms  Inaccurate data being propagated across the systems  Longer times to close our books at the end of the month  Many manual work efforts  Digital River Solution:  Align all of the platform transaction data, as a Business Imperative with the aid of a Data Governance Program, to support creating a single source of truth (ERP)  Challenges:  Different source data capture points and multiple workflows  Different payments methods and fraud rates  Similar technology processes performed by different systems  Similar business concepts that used many terminologies 17

18 DATA MANAGEMENT ASSOCIATION (DAMA) Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion -Data Architecture: as an integral part of the enterprise architecture -Data Modeling & Design: analysis, design, build, test, deployment and maintain -Data Storage: structured physical data assets storage management -Data Security– support ensuring privacy, confidentiality and appropriate access -Data Integration & Interoperability – support data acquisition, transformation and movement (ETL), federation, or virtualization -Documents and Content – store, protect, index, and enable access to data found in unstructured sources (electronic files and physical records), and make data available for integration and interoperability with structured (database) data. -Reference & Master Data – manage gold versions and replicas -Data Warehousing and Business Intelligence – support managing analytical data processing and enable access to decision support data for reporting and analysis -Meta-data: integrate, control and deliver meta-data - Data Quality: define, monitor and improve data quality DATA MANAGEMENT BODY OF KNOWLEDGE (DMBOK) GOVERNANCE FRAMEWORK © DAMA-DMBOK2 (Apr 2012) 18

19 DATA MANAGEMENT ASSOCIATION (DAMA) Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion DATA MANAGEMENT BODY OF KNOWLEDGE (DMBOK) GOVERNANCE FRAMEWORK Data Governance: Involves planning, oversight, and control over data management and use of data © DAMA-DMBOK2 (Apr 2012) 19

20 DATA MANAGEMENT ASSOCIATION (DAMA) Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion © DAMA-DMBOK2 (Apr 2012) Data Management FunctionsEnvironmental Elements 20

21 WHAT IS DATA GOVERNANCE? Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion Data Governance has all the characteristics of any Strategic governance process Process People Technology Programs Management Governing body Procedures Plan Decision -making Business needs support Strategy Assets Digital River’s definition of Data Governance:- A set of processes that treats Data as a Strategic Area within the enterprise (just like Sales, Finance, HR, Sourcing, etc…) 21

22 BUSINESS IMPACT/BENEFITS AND RETURN ON OBJECTIVE Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion  A mechanism to convert raw Order/Transaction, Customer, Client, Vendor, Product and Other data collected from the shopper websites that we host for our clients, to 2 categories.  Clean Data (passed on to the ERP)  Dirty Data (requiring some clarification and remediation)  Digital River’s definition of Data Governance:-  A set of processes that treats Data as a Strategic Area within the enterprise 22

23 THE DATA MANAGEMENT WHEEL: BINARY VS. TERNARY Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion  In 2008 embraced DM which meant fundamentally changing the organizational structure of Digital River: IT Bus IT Bus DM Binary model: No Data Mgmt IT and Business frictions Ternary model: Data Mgmt No IT and Business frictions DM deployment  The DM is a process “wheel” owned by the Data Stewards  Data Stewards interface with Business and IT Stewards to carry out Data Management activities around remediating the Dirty Data 23

24 ENTERPRISE DATA MANAGEMENT MATRIX ORGANIZATION & ACTIVITIES Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion 24

25 SIMPLIFYING PLATFORMS DOING SIMILAR THINGS Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion E-Com1 E-Com2 - Accounting - Reporting - Billing - Client Management - Tax - Compliance - Accounting - Reporting - Billing - Client Management - Tax - Compliance - Accounting - Reporting - Billing - Client Management - Tax - Compliance  Challenge:  How can we centralize all of our platforms, creating one true source for all Accounting, Reporting, Billing, etc?...... E-Com8  Business functions spread across each platform  Decentralized structure 25

26 SOLUTION: ERP Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion  Commerce would continue to happen on platforms, and transmit to the ERP system in batches of data  Implement an ERP system, sourced from each of the separate e-commerce platforms E-Com1 E-Com2 E-Com8 SAP - ERP...... 26

27 SOLUTION: ERP SYSTEM FED BY COMMERCE PLATFORM DATA Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion ERPETL E-Com1 E-Com2 E-Com3 DATA QUALITY ERP Integration Structure (ETL) Extract Transform Load Content (Data Quality Tool) Quality Rules Governance Certification ERP DW BI REPORTING Process (ERP) Integration Productivity Controls Reporting Accuracy Flexibility Scalability Ancillary systems ERP MDM ETL drop zone TSS ® Stage...... > Commerce occurs on platforms, batches of data transmitted to ERP > DQP RFP: DQP Tool became an integral Technology component of the ERP Implementation 27

28 28 Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion DATA GOVERNANCE HAS A FOCUS ON POLICIES AND PROCESSES

29 29 Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion DATA QUALITY HAS A FOCUS ON DATA PROFILING

30 DATA QUALITY MEASURES THE LEVEL OF QUALITY DQ COMPONENTS: 30 COMPLETENESS Is all the requisite information available? Are data values missing, or in an unusable state? Example: Product ID code not present; missing fee amount; etc. CONFORMITY Are there expectations that data values conform to specified formats? If so, do all the values conform to those formats? Examples: Phone numbers in different formats; numbers with different decimal precision; etc. CONSISTENTCY Do distinct data instances provide conflicting information about the same underlying data object? Are values consistent across data sets? Do interdependent attributes always appropriately reflect their expected consistency? Examples: different meanings for Authorization Date or Contract End Date; etc. ACCURACY Do data objects accurately represent the “real-world” values they are expected to model? Examples: misspelled names, addresses; wrong product id codes; etc. DUPLICATION Are there multiple, unnecessary representations of the same data objects within your data set? Examples: duplicate customer name, site id; address; etc. INTEGRITY What data is missing important relationship linkages? Examples: A sale event cannot be linked to a marketing campaign; etc.

31 THE DATA QUALITY PROGRAM (DQP): PROCESS COMPONENT Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion Identification Impact assessment Clarification & remediation Monitoring ITBus. 1.Identification: > Top Data Areas of importance > Top 5 issues/concerns in Data Areas > Provide unfiltered dataset to EDM 2.Impact assessment: > EDM loads dataset to TSS for Profiling > EDM writes up potential Business Rule > EDM sets up a workshop 3.Clarification & remediation > Data Steward attends Business Rules workshop > Data Steward clarifies and sign-off Business Rules > EDM Implement Business Rules 4.Monitoring > EDM builds the Data Quality dashboard > EDM conducts regular Data Quality compliance monitoring > Objective: > Improving the Quality of your Data through a strategic framework and a tactical methodology 31

32 DATA QUALITY PROGRAM (DQP FOR ERP): PEOPLE COMPONENT Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion > Roles & responsibilities: > Data Management (DQP Manager, Data Stewards) > Handle the implementation and regular review of their assigned rules (monthly data quality meetings, rules sign off, Data Quality policy enforcement, etc…) > Business Owners: > Own the determination of Business rules. Engage their Data Stewards when an update/new rule is required. > IT SMEs: > Build and maintain the interfaces between data consuming systems and the DQP application Identification Impact assessment Clarification & remediation Monitoring ITBus. > Objective: > Centralize the management of quality rules for all enterprise data elements 32

33 DQP ROLES Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion 33

34 DQP: ERP IMPACT ASSESSMENT Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion Attribute Unique Values MinMax Null Dist % Business Rules Platform Id1GAT 0 Permissible values are GAT, TLA, or GNT. Nulls are not allowed. When the value is TLA, it must be recoded to TA. Customer Id3721674232827896130 Nulls are not allowed. When a value is present, this field is a pass through. Bill To Address Id39044429340857497210 Nulls are not allowed. When a value is present, this field is a pass through. Ship To Address Id39044429340857497210 Nulls are not allowed. When a value is present, this field is a pass through. Site Id216bhautezitvee0 No Nulls Allowed. Permissible Value set are determined within ERP (location of master list to be determined) Site Owner Id151bhautezitvee0 No Nulls Allowed. Permissible Value set are determined within ERP (location of master list to be determined) DQP: ERP Clarification & Remediation > DQ Tool Business Rules were recorded in a Business Rule Book > Each rule was approved and signed off by a Business Steward > DQ Workshop Document 34

35 DQP: ERP CLARIFICATION & REMEDIATION Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion  Where do we implement the Business rules? E-Com1 E-Com2 E-Com3 ERP DATA QUALITY ETL drop zone TSS ® payment_type varchar2 (32 byte) Visa payment_id number (2) 1 pay_method char (2 byte) VS payment_method varchar2 (32 byte) VISA payment_method Visa 1 VS payment_method VISA Impact assessment Identification ITBus. Clarification & remediation Monitoring...... Staging Each Business Rule is against a column: > If the Payment method column value is: ‘Visa’, ‘1’, ‘VS’ > Then recode the Payment Method column value to ‘VISA’ 35

36 DQP: ERP MONITORING Business Challenge 1IntroductionBusiness Challenge 2RecommendationsConclusion  Measures the level of data quality = rate of compliance with business rules (DQ Tool output)  Data Quality is measured monthly, after updates in Business Rules from previous report  Data Stewards responsible for acting on DQ Dashboard metrics  Over 400+ attributes have business rules fired.  Consistently achieving 15-20% increase in the quality of data as a result of data cleansing 36

37 REAL TIME ADDRESS VALIDATION FOR COMMERCE STORES BUSINESS CHALLENGE 2

38 THE ON-DEMAND TECHNOLOGY ADVANTAGE 38 Who We AreOur FocusOur PassionExperienceInnovation An Average Day, We Support: 1.5+ billion API calls Serve 60 million pages Send 3+ million emails Process 300,000 orders Create 5 authorizations/sec Host 6+ terabytes of digital content Industry Leading 99.997% Uptime Managed to < 40% Utilization 7 Triple Redundant Servers Worldwide

39 E-COMMERCE TAILORED TO YOUR NEEDS 39 Our partners complement existing systems, address specific technology requirements, and evolve with the market and your growing business over time. Who We AreOur FocusOur PassionExperienceInnovation

40 API FIRST METHODOLOGY 40 Who We AreOur FocusOur PassionExperienceInnovation APIs

41 CLOUD COMMERCE BILLING & SHIPPING ADDRESS ORDER ERRORS 41 Business Challenge 2Business Challenge 1IntroductionRecommendationsConclusion  Incorrect Cloud Commerce Billing and Shipping Address Order Errors  Challenges:  Increased Lost / Returned Package costs  Incorrect taxation on orders  Cons:  Increased customer service costs  Unsatisfied customers  Loss of products and sales  Potential for undetected fraud  Many manual work efforts to go around the challenge  Digital River Solution:  Digital River implemented Real-Time Address validation (RTAV). A Data Quality Traffic Monitor/Router and a Data Quality Tool were selected for the RTAV.  Enterprise Software licenses were acquired and Country Postal Templates and Country Postal Subscriptions were subscribed to.  Data Management team was made responsible for the and Data Governance and Data Quality efforts pertain Addresses.  And DQ efforts moved upstream from ERP batch to real-time.

42 BUSINESS IMPACT/ BENEFITS AND RETURN ON OBJECTIVE FOR RTAV 42 Business Challenge 2Business Challenge 1IntroductionRecommendationsConclusion

43 DUE DILIGENCE: ADDRESS DATA QUALITY VENDOR REVIEW 43 Business Challenge 2Business Challenge 1IntroductionRecommendationsConclusion

44 LENGTH OF TIME RTAV HAS BEEN IN PLACE/PROGRAM EVALUATION 44 Business Challenge 2Business Challenge 1IntroductionRecommendationsConclusion DQP: HOW RTAV WORKS

45 SCALE OF THE RTAV RELEASE PROCESS SOLUTION (ENTERPRISE) 45 Business Challenge 2Business Challenge 1IntroductionRecommendationsConclusion

46 DQP: REAL TIME ADDRESS VALIDATION (RTAV) 46 Business Challenge 2Business Challenge 1IntroductionRecommendationsConclusion E-Com Platform 3 E-Com Platform 2 E-Com Platform 1 ETL Global Postal Directories DQP Tool ERP System Traffic Router Real Time Cleansing Hourly Batch Cleansing Bad Addresses Cleansed Addresses Clean Addresses Impact assessment Identification ITBus. Clarification & remediation Monitoring Business Consumers/Owners IT Owners, Code Owners, Tech. SME’s Data Stewards Countries covered N.America (2) W. Europe Bundle (16) LAM Bundle (1) APAC Bundle (2 Multi-byte, 1 single byte) Future Expansion E.Europe expansion APAC expansion LAM expansion Data Quality & Traffic Monitoring Service 3 Data Center red. solution Load balanced Code Promotion (Dev, Sys).. Platform Release Cycle Data Quality & Profiling Discovery Tool 1 Data Center solution with backup Load balanced Code Promotion, Dev, Sys, Int, Prod ERP Release Cycle

47 THE TEAM EVOLUTION: DATA MANAGEMENT AT DIGITAL RIVER (2008-13) 47 Business Challenge 2Business Challenge 1IntroductionRecommendationsConclusion 2008 2010 2013

48 OVERALL BENEFITS OF THE DATA QUALITY PROGRAM 48 Business Challenge 2Business Challenge 1IntroductionRecommendationsConclusion  Data Quality provides - Single, independent environment manages all business rules that ensures data quality for ERP  DQ Traffic Routing Tool and DQ Tool provides the ability to conduct Real Time Address validation for the Commerce platforms and other batch mode cleansing functionality for the ERP  DQP Tool Advantage: When new e-commerce platforms are integrated to the ERP, existing business rules are reused, minimizing redundant development, and centralized management of Business rules  DQP: A 4-step process that requires People, Process and Technology to support our Data Governance efforts  2010 Pitney Bowes Software survey - 2/3 of organizations (revenues > $1Billion), have Data Governance activities underway (including MDM projects) http://www.information-management.com/newsletters/data_governance_MDM_maturity_ROI-10022164-1.html

49 WHAT OTHER CHANGES COULD POTENTIALLY WORK BETTER? FURTHER RECOMMENDATIONS

50 Recommendations PEOPLE, PROCESS, TECHNOLOGY 50 Business Challenge 1Business Challenge 2IntroductionConclusion > Data Governance need not be invented from scratch: HR GovernanceFinancial GovernanceData Governance PeopleHR associates Financial analysts; accountants Data Stewards Process Human Capital Management Finance & AccountingData Management TechnologyHR systems Accounting systems (G/L; Tax; Treasury) Data Quality; MDM; MDR systems Functional Programs Skill set mgmt Recruiting Benefits mgmt Compensation framework Contractor mgmt Training Budget & forecasting Treasury Financial reporting Tax Investment Mgmt Data Quality Program MDM Program MDR Program Managed assetLabor force Financial assets & liabilities Data Policies & RegulationsHR policies SOX, SAS 70, SEC, IFRS, etc… Privacy laws; HIPAA; SOX; DM Policies; etc… Functional leaders Training Mgr Recruitment Mgr Benefits Mgr Comptroller Tax Mgr Investment Mgr DQP Mgr MDM Mgr MDR Mgr Process ownerVP of HRVP of Finance / CFO VP of Data Management / CDO (Chief Data Officer)

51 Recommendations NEW ORG. ROLES CHIEF DATA OFFICER/VP OF DATA MGMT. 51 Business Challenge 1Business Challenge 2IntroductionConclusion CIO / VP Technology Manager / Director CDO / VP Data Mgmt. Data Governance + IT Governance Focus: Process Mgmt Focus: Data Mgmt  Data Governed as an Independent Asset  Centralized authority: CDO / VP Data Mgmt.  Improved control over compliance and financial risks  Clear accountability for all aspects of data  Cost reductions from uniform DM processes  Data scalable across the enterprise, and over time (growth, acquisitions…)  Data Management no longer dependent on IT strategy  Cannot be governed Independently  Not managed as a Strategic Asset  Conflict of interests between Technology and Data Management  Difficult to enforce Quality rules across the enterprise  High cost and low returns  Data becomes silo-driven (like IT…)  Responsibility without authority

52 Recommendations EXPANSION OF THE EDM MATRIX ORGANIZATION 52 Business Challenge 1Business Challenge 2IntroductionConclusion * Chief Data Officer (typically reports to CTO, CIO, CEO, CMO, CSO) http://en.wikipedia.org/wiki/Chief_data_officerhttp://en.wikipedia.org/wiki/Chief_data_officer ** Data Management Area: typically determined using a Data Consumption Matrix (regularly updated) *** Data Stewards can either belong to the EDMO, remain in their respective DMA, or both. CDO* DQMDRMDMLDM... Program Managers Senior DM Executives Data Stewards *** DMA** 1 DMA** 2 DMA** 4 DMA** 3 DM Council/ Steering Committee

53 Recommendations DATA GOVERNANCE SCOPE OF CONTROL 53 Business Challenge 1Business Challenge 2IntroductionConclusion © Copyright Baseline Consulting Group, 2013. Used with permission from SAS Institute.

54 WHAT ARE THE LESSONS LEARNED? CONCLUSION

55  Data Governance and the DQP: Managed process oversight to ensure that data-related processes and controls are being followed  Data Governance at Digital River  Is a Strategic and Permanent investment to treat Data as a Strategic Asset  It exists through a functional Enterprise Data Management program  Data Quality Program (DQP)  A 4-step process. Requires People, Process and Technology to support our Data Governance efforts  Reduces Operational costs for order checkout and info. delivery processes  Reduces Risk exposures (financial, regulatory, market and strategic)  Both Require:-  An organizational change to the Ternary model (Business / Data / IT)  A “Data Governor Authority” (e.g. VP of Data Mgmt.) and a dedicated EDM team  Effective use of Data Quality tools (for Profiling, Discovery, Cleansing etc.)  Contrary to many beliefs the Data Quality Tool is NOT a Database  It is a repository of business rules; Rules can be managed and reused. DATA GOVERNANCE AT DIGITAL RIVER 55 ConclusionBusiness Challenge 1Business Challenge 2RecommendationsIntroduction Impact assessment Identification ITBus. Clarification & remediation Monitoring

56 56 DEEPAK BHASKAR Sr. Manager, Data Governance, Trillium Product Governance and Compliance Digital River, Inc. http://www.linkedin.com/in/dbhaskar1 DB_2008 dbhaskar03 dbhaskar2008


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