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2013 Data Governance Professionals Organization (DGPO) Digital River Webinar
The document outlines the successful data governance practices at Digital River from 2008 to 2013, highlighting the essential processes that treat data as a strategic asset within the company. It discusses organizational changes, the implementation of data management functions, and the integration of data quality and master data management programs to address business challenges. The presentation also emphasizes the importance of establishing a governance structure and leveraging technology to enhance overall data quality and management efficiency.
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2013 Data Governance Professionals Organization (DGPO) Digital River Webinar
- 1. SUCCESSFUL DATA GOVERNANCE AT DIGITALRIVER 2008-13 2013 DGIQ BEST PRACTICE AWARD: FINALIST 11/6/2013 + by Deepak Bhaskar
- 2. AGENDA Introduction SpeakerBio: Deepak Bhaskar Company introduction: Digital River Data Governance Data Management Functions & Environmental Elements Organizational change & Data Management programs People, Process, Technology Integrating Programs: Data Quality and Master Data Management Digital River Business Challenges Batch mode for ERP Finance & Accounting Domain Real time for Postal Address & Geocode Domain Connecting the Dots Making Business Cases and Vendor Tool selections for programs SLA’s, KPI, Dashboards and Org Charts Recommendations Future roles and organizational structure Conclusion: Digital River Data Governance best practices 2
- 3. SPEAKER BIO: 3 Introduction DataGovernance Business Challenges Connecting the Dots Recommendations 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) Retail Marketing Associate (Barnes and Noble Booksellers) 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. Conclusion
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- 5. 5 $386M in 2012revenue $27B annual online transactions (ttm) 19 years of experience 30 offices around the world 50% revenue outside the Americas in 2012 1,400+ commerce experts 100+ countries where we do business 180 payment methods DIGITAL RIVER AT A GLANCE 5 UNMATCHED GLOBAL EXPERIENCE AND REACH 40 transaction currencies 30 site display languages 15 languages in customer service Technology Pioneer, Founded in 1994 Generates Revenue in Virtually Every Country on the Planet Introduction Data Governance Business Challenges Connecting the Dots Recommendations Conclusion
- 6. 6 LEVERAGE AN END-TO-ENDSUITE OF COMMERCE SOLUTIONS WITH OUR COMMERCE-AS-A SERVICE OFFERING Marketing Services SITE OPTIMIZATIONSEARCH EMAIL AFFILIATE DISPLAY ANALYTICS Commerce Experience Commerce Business Infrastructure Payments SHOPPING CARTCATALOGWCMS SEARCHAN -DIZING MERCHANDISING & PROMOTION LOCALI- ZATION RECOMMENDATION & PERSONALIZATION A/B TESTINGPRICING ADMIN TOOLS COMPLIANCE ORDER MANAGEMENTFRAUD LOCAL ENTITIES MERCHANT & SELLER OF RECORD INTEGRATION SERVICES BUSINESS INTELLIGENCETAX CLOUD ENABLEMENT CUSTOMER SERVICE SINGLE CONNECTION TO PAYMENTS GRID PSP SERVICES GATEWAY SERVICES IN-COUNTRY MERCHANT FULL- SERVICE ACQUIRING Introduction Data Governance Business Challenges Connecting the Dots Recommendations Conclusion
- 7. 7 SOFTWARE & SERVICES GAMES AND ENTERTAINMENT WORLD-CLASSCLIENTS TRAVEL E-TAIL Consumer Electronics Introduction Data Governance Business Challenges Connecting the Dots Recommendations Conclusion
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- 9. DATA MANAGEMENT ASSOCIATION(DAMA) Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations © DAMA-DMBOK2 (Apr 2012) Data Management Functions Environmental Elements 9 Conclusion
- 10. WHAT IS DATAGOVERNANCE? 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…) 10 Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations Conclusion
- 11. THE DATA MANAGEMENTWHEEL: BINARY VS. TERNARY In 2008 embraced DM which meant fundamentally changing the organizational structure of Digital River: ITBus ITBus 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 11 Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations Conclusion
- 12. ENTERPRISE DATA MANAGEMENTMATRIX ORGANIZATION & ACTIVITIES 12 Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations Conclusion
- 13. 13 PEOPLE, PROCESS, TECHNOLOGY 13 >DataGovernance need not be invented from scratch: HR Governance Financial Governance Data Governance People HR associates Financial analysts; accountants Data Stewards Process Human Capital Management Finance & Accounting Data Management Technology HR 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 asset Labor force Financial assets & liabilities Data Policies & Regulations HR 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 owner VP of HR VP of Finance / CFO VP of Data Management / CDO (Chief Data Officer) Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations Conclusion
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- 15. 15 Data GovernanceIntroduction BusinessChallenges Connecting the Dots Recommendations Conclusion E-COMMERCE PLATFORM At the heart of the business: The order checkout workflow: 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 A way to convert raw data to Clean Data Dirty Data Definition of Data Governance A set of processes that treats Data as a Strategic Area within the enterprise
- 16. 16 ERP USE OFDATA GOVERNANCE PROGRAMS Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations ERPETL E-Com1 E-Com2 E-Com3 DATA QUALITY ERP 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 and MDM became Technology components of the ERP Implementation Conclusion
- 17. REAL TIME ADDRESSVALIDATION (RTAV) USE OF DQP 17 Business ChallengesData GovernanceIntroduction Connecting the Dots Recommendations COMMERCE PLATFORM-RTAV CALLS Conclusion
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- 19. 1919 Data GovernanceIntroduction BusinessChallenges Connecting the Dots Recommendations DATA GOVERNANCE HAS A FOCUS ON POLICIES AND PROCESSES Conclusion
- 20. 2020 DATA GOVERNANCE FOCUS:POLICIES & PROGRAMS Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations Conclusion
- 21. 2121 Data GovernanceIntroduction BusinessChallenges Connecting the Dots Recommendations Conclusion BUSINESS CASE STUDY AND ROI FOR DATA GOVERNANCE PROGRAM’S
- 22. 22 VENDOR DUE DILIGENCEFOR DATA GOVERNANCE PROGRAMS Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations Conclusion
- 23. DASHBOARDS Measures thelevel of data quality = rate of compliance with business rules (DQ Tool output) Measured monthly, after updates in Business Rules from previous report Data Stewards responsible for acting on 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 23 Data GovernanceIntroduction Business Challenges Connecting the Dots Recommendations Conclusion
- 24. THE TEAM EVOLUTION:DATA MANAGEMENT AT DIGITAL RIVER (2008-13) 24 Business Challenge 2Business Challenge 1Introduction Recommendations Conclusion Vice President Operations Vice President Strategic Technologies Sr. Director EDM Data Steward Data Steward Data Steward Enterprise Data Management Data Governance Steering Committee Vice President Operations Vice President Finance Sr. Director EDM Vice President Strategic Technologies Vice President Strategic Marketing Vice President Tax Vice President Enterprise Systems and Data Management Vice President Enterprise Systems and Data Management CFO Vice President Strategic Technologies Data Steward Manager Data Quality Data Steward Enterprise Data Management Data Governance Steering Committee Vice President Finance Vice President Strategic Technologies Vice President Tax Vice President Internal Systems CFO Vice President Internal Systems Vice President Product Manager Data Quality CIO Vice President Governance & Compliance Sr. Software Engineer Sr. Manager Data Governance, DQ Tool Product Manager Data Steward ERP Enterprise Data Management Data Governance Steering Committee Vice President Finance Vice President Tax Vice President Internal Systems CFO Vice President Internal Systems CIO Vice President Governance & Compliance Vice President Product Vice President Development CMO Sr. Manager Data Governance, DQ Tool Product Manager COO 2008 2010 2013 RECOMMENDATIONS
- 25. Data GovernanceIntroduction BusinessChallenges Connecting the Dots Recommendations Conclusion NEW ORG. ROLES CHIEF DATA OFFICER/VP OF DATA MGMT. 25 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
- 26. Data GovernanceIntroduction BusinessChallenges Connecting the Dots Recommendations Conclusion EXPANSION OF THE EDM MATRIX ORGANIZATION 26 * Chief Data Officer (typically reports to CTO, CIO, CEO, CMO, CSO) http://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* DQ MDRMDM LDM . . .Program Managers Senior DM Executives DataStewards*** DMA** 1 DMA** 2 DMA** 4 DMA** 3 DM Council/ Steering Committee
- 27. Data GovernanceIntroduction BusinessChallenges Connecting the Dots Recommendations Conclusion DATA GOVERNANCE SCOPE OF CONTROL © Copyright Baseline Consulting Group, 2013. Used with permission from SAS Institute.
- 28. THE TEAM EVOLUTION:DATA MANAGEMENT AT DIGITAL RIVER (2008-13) 28 Business Challenge 2Business Challenge 1Introduction Recommendations Conclusion Vice President Operations Vice President Strategic Technologies Sr. Director EDM Data Steward Data Steward Data Steward Enterprise Data Management Data Governance Steering Committee Vice President Operations Vice President Finance Sr. Director EDM Vice President Strategic Technologies Vice President Strategic Marketing Vice President Tax Vice President Enterprise Systems and Data Management Vice President Enterprise Systems and Data Management CFO Vice President Strategic Technologies Data Steward Manager Data Quality Data Steward Enterprise Data Management Data Governance Steering Committee Vice President Finance Vice President Strategic Technologies Vice President Tax Vice President Internal Systems CFO Vice President Internal Systems Vice President Product Manager Data Quality CIO Vice President Governance & Compliance Sr. Software Engineer Sr. Manager Data Governance, DQ Tool Product Manager Data Steward ERP Enterprise Data Management Data Governance Steering Committee Vice President Finance Vice President Tax Vice President Internal Systems CFO Vice President Internal Systems CIO Vice President Governance & Compliance Vice President Product Vice President Development CMO Sr. Manager Data Governance, DQ Tool Product Manager COO 2008 2010 2013 CONCLUSION
- 29. 29 Data GovernanceIntroduction BusinessChallenges Connecting the Dots 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 Management Programs (DM) 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 29 Impact assessmen Identification IT Bus. Clarification & remediation Monitoring
- 30. 30 DEEPAK BHASKAR Sr. Manager,Data Governance, Trillium Product Governance and Compliance Digital River, Inc. http://www.linkedin.com/in/dbhaskar1 DB_2008 dbhaskar03 dbhaskar2008
Editor's Notes
- #2 Slide 1: Title Hello, Good Morning, Good Afternoon and Good Evening Ladies and Gentlemen. Thank you for joining us from where ever you all are based out of, around the world. And welcome to the November webinar organized by the ‘Data Governance Professionals Organizations’. Today we will review: Successful Data Governance at Digital River: 2013 DGIQ Data Governance Best Practice Award: Finalist The 2013 DGIQ conference is an annual conference that judges a number of submissions and selects a Best Practice Award Winner and Finalist. The award is given to practitioners in recognition of the business value and technical excellence they have achieved in the design and implementation of an outstanding data governance program
- #3 Slide 2: Agenda Todays’ agenda includes, An Introduction section includes some information about me and my background We’ll then review the company: Digital River Then dive into the presentation and talk about Data Governance, which will include: Data Management Functions & Environmental Elements Organizational change & Data Management programs People, Process, Technology Integrating Programs: Data Quality and Master Data Management After which I will provide an overview of the primary Business challenges Digital River faced from a Data Management perspective The 1st challenge was: Batch mode for ERP Finance & Accounting Domain And the 2nd issues was: Real time for Postal Address & Geocode Domain We’ll look at the how the challenges relate to People, Process and Technology. Review key Data Management programs solutions that we’ve put in place, and then I’ll provide some I’ll Connect the Dot on: Making Business Cases for such programs Vendor Tool selections for programs SLA’s, KPI, Dashboards and Review Org Charts Finally I’ll review some Recommendations Which will look at potential future roles and reasons for change in organizational structures And then I’ll conclude with some key points as a result of the Digital Rivers DG best practices we’ve put in place
- #4 Slide 3: Speaker Bio With Digital River - 10 years, the past 5.5 years in a Governance role. Other roles I’ve held at Digital River include: 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) Retail Marketing Associate (Barnes and Noble Booksellers) 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 So let’s take a look at who is Digital River. And what we do.
- #5 Slide 4: Digital River Company Overview Digital River is the Revenue Generation Experts in Global Cloud commerce. Our unique combination of global expertise and Innovative technology gives businesses the freedom to realize their greatest potential. We see opportunity around every corner for our clients and we believe in Greater Possibilities. Digital River was founded in 1994 and is headquartered in Minnetonka, Minnesota. The company has been publically traded since August 1998 (NASDAQ:DRIV). And as a US public company we adhere to SOX and SAS 70 standards. Gartner and Forrester are think tanks that regularly review companies annually in various industry segments. Gartner rates vendors upon two criteria: completeness of vision and ability to execute. Digital River’s continues to be recognized in the challengers quadrant. In the Nov 2011 Gartner Magic Quadrant for e-Commerce Digital River was placed in the Challenger’s quadrant. The 2011 position is better because we are ‘higher’ in the ‘challengers’ quadrant, and fewer competitors are grouped near Digital River in the Magic Quadrant. Digital River was also listed in the 2012 Q3 Forrester Wave B2C Commerce Suite as a Strong performer. So lets look at some numbers of the company.
- #6 Slide 5: Digital River at Glance Digital River is a Global company, with a 2012 revenue of $386M, $64M of which is an R&D investment We managed over $27 Billion in annual online transactions during the past 12 trailing months. There’s complexity in doing business in global online markets. The good news we simplify the complexity and manage the risk on a global scale to enable a great user experience In 2012 we generated more than 50% of our revenue outside the Americas, We have over 19 years of experience that brings Over 1,400 commerce experts as employers investing 3 Million hours per year focused on growing our clients revenue We have 30 offices around the world and opened offices in India and Russia in the past 2 years as we continue to expand our global footprint in order to meet our client needs of doing business in other countries. We do business in over 100+ countries. 38 Patents have been issued to Digital River in Commerce, Marketing and Payments to help solve the tricky issues of Cloud Commerce And we manage over 180 payment methods Also as part of our Unmatched Global experience and reach We can transact in 40 currencies (localized payment methods), but sites can display in 185 currencies Sites display in more than 30 languages. Customer care is 24X7 via the phone and email in 15 languages Overall we generating Revenue in virtually every country. So these were some key metrics of where Digital River has achieved success. And to be successful in Going global, we have to work local. Companies we work with, really value this. We are continually expanding our global capabilities to support our clients growing global businesses. Digital River is Invested in our clients’ Global success – so how do we do this?
- #7 Slide 6: Commerce-as-a-Service Digital River offers a suite of solutions that deliver Commerce-as-a-service to our clients through: Marketing Services. Commerce Experience. Commerce Business Infrastructure. Payments. Let’s take a look at more detail here: Marketing Services. Our clients rely on the depth of our e-marketing services to help them acquire and retain customers. This includes: Search, SEO, Email, Affiliate, Display, Analytics and Site Optimization Commerce Experience. We offer a multi-tenant, cloud-based e-commerce platform that can scale vertically or horizontally. This includes things like Catalog Management, Merchandising and Promotions, Pricing Management, Localization needs, A/B Testing among others capabilities Commerce Business Infrastructure. A key differentiator for Digital River is our commerce business infrastructure, let’s look at this capability in more detail. This includes Tax management, Fraud, Compliance, Customer Service and Cloud enablement Payments. We provide customers with a single connection to the payments grid and take care of the legal, regulatory, and compliance complexities of running an e-business. This includes Gateway Services, Payment Service Provider Services, In-Country Merchant and provide a single connection to a Payments Grid
- #8 Slide 7: CLIENTS Digital River has helped some of the world’s leading brands build successful online businesses. We focus on global enterprises and SMBs in industries. This includes being the primary SELLING PARTNER FOR these LEADING GLOBAL COMPANIES Even though Digital River started in Software and Games we expanded our expertise to New market segments such as: Consumer Electronics including clients like Lenovo, Seagate, Logitech, Western Digital, etc., Travel includes clients like Orbitz, Expedia, Travelocity etc., Games and Entertainment includes clients such as Razer, Wizards of the Coast, Capcom, etc., E-tail includes clients like e-Bay, American Apparel, Euro Florist, etc., and as mentioned our initial focus was: Software services serve the needs of clients like Microsoft, Autodesk, Trend Micro, Nuance, Sonic, VMware, etc., Digital River sells client products and services through internet and a direct sales force. We serve software, consumer electronics, game product manufacturers, and online channel partners, who including retailers and affiliates in the United States, Austria, Brazil, China, Germany, Korea, Ireland, Japan, Luxembourg, Mexico, Singapore, Sweden, Taiwan, and the United Kingdom. We continue to expand our client base. There‘s around 50 client brands identified here, some of them are big names in the future of technology
- #9 Slide 8: What is Data Governance? As is typical to the industry, Digital River acquired other companies over the years. However, many of these platforms had similar and repetitive process, among them; the shopping cart, postal validation, fraud checks, payment processing, taxation, inventory and fulfillment etc. So we had a decentralized structure with many platforms and the need to consolidate the systems became important. Since there were similar Business functions spread across each platform. So the challenge was to Create a Centralized True Source, for all of the data consumed by accounting, reporting, billing etc. That’s where data governance comes to the picture. But what is Data Governance?
- #10 Slide 9: DAMA Wheel and Hexagon We looked at various data governance frameworks and came across this one from the DATA Management Association (DAMA) They broke down Data Governance to 2 main areas: Data management functions and Environmental Elements The Data managements includes a number of areas: Data Architecture: as an integral part of the enterprise architecture Data Modeling & Design: analysis, design, building, testing, deployment and maintenance of Data Models Data Storage: has to do with structured physical data assets storage management (as you all know this is being virtualized and is moving into the cloud) 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 However, our initial focus was Data Quality, Meta-data management and Master data management. I will touch upon these later in the presentation. Even though there were needs in other areas such as Data Security, Data Modeling and more recently in Data Storage, we had IT teams that wanted to continue to manage those areas in the traditional IT Governance landscape. So Data Management Digital River focused on these 3 first to build: Data Governance: which ‘ Involves planning, oversight, and control over data management and use of data ’ Environmental elements include Organization & Culture which includes significant change such as set up an EDM Team, hiring Data Stewards and Communication plans and Hosting Lunch and Learn sessions It also includes Activities like setting up a Data Council and Data Governance Steering Committee and scheduling regular meetings to review road map and priorities They set the Deliverables that are necessary for them to see progress, these include things like SLA’s, KPI’s, Dashboards etc. Roles and Responsibilities includes interactions within the EDM team between Managers and Data Stewards and external interaction such as with various Departments whose Business stewards and Technology stewards get involved and participate in meetings Practices and Techniques include things like setting up a Data Quality program with standard operating procedures Technology requires bringing in an external tool, in our case we have Trillium Software Systems, a division of Harte Hanks to manage our Data Quality, Discovery and Profiling pieces for both real time and batch activities. But we have looked at a number of solution for Master Data management and Meta-data management These environmental elements help build the Goals and the Principles for the overall operation of Data Governance and Data Management organization activities
- #11 Slide 10: Data Governance Definition So when we looked at Data Governance we found it involved attributes such as: People Process Technology Strategy, Programs, Mangement Plans Governing Body, Business needs Decision Making, Procedures among others…. It showed that Data Governance has all characteristics of any Strategic Governance Process, so the definition we came up with was: Data Governance is a set of processes that treats Data as a Strategic Area within the enterprise just as any other strategic area of vital importance to the organization. But this meant a Cultural and Organization Change
- #12 Slide 11: Data Management Wheel Traditionally Business and Technology folks predominantly focused on code related functionalities and requirements. This has typically been a Binary model So even though historically, data has fallen between the cracks and not been a focus for these groups, the volumes of data we deal with, identified, that we need to change that. So one of the challenges was to convert the Digital River Business and IT/Dev. binary working groups by the introduction of a Data Management Wheel. This DM team would help interface between Business and IT/Dev. wheels from a data perspective in a Ternary model. That meant A Fundamental change was needed in how things work. With the DM Deployment, a Ternary model is in play that Required new roles and processes. The Data Management wheel was owned by Data Stewards Data Stewards worked with Business & IT Stewards to carry out Data Management activities around remediating Dirty data. This launched Data Quality cleansing effort for master/transactional data for the ERP, which has been in use since 2008 But before I get into that let me review the Data Governance and Data Management Team
- #13 Slide 12: Data Management Matrix organization What initially started off with a Data Governance and Data Management focused effort on Data Quality for the ERP has expanded as shown here. Let’s take a look at this image quickly. In 2008 a dedicated Enterprise Data Management Team (EDM) was started. In 2008 a Sr. Director for Data Management was hired to manage a team of 3 Data stewards, who were hired to assist with the data cleansing effort. A Data Management Charter was written up along with Data Management, Data Quality policies to provide oversight and controls on the management and use of the data in support of the ERP. The idea was that cleansing solutions would eventually move upstream to source data on commerce platforms and cleansing would eventually be done in Real time on the commerce platforms; which happened in 2010-11 It became evident that Data Governance and Data Management were Processes, which involved Data Stewardship of Programs such as Data Governance, Data Quality, Master Data and Meta data in order to support the ERP and Data Warehousing More recently, other areas are becoming prominent and will need to be addressed as we delve with more unstructured data, data in cloud, privacy and security concerns and regulatory data needs. So to fully answer what is Data Governance let’s compare this matrix organization structure and activities to other Strategic Governance Areas in the organization
- #14 Slide 13: People, Process, Technology Most strategic governance areas in organizations involve People, Process ,Technology and other required focus areas Here we see how a HR Governance structure is laid out and how a Financial Governance structure is laid out. The People component involves HR associates and Financial analysts The Process in place are Human capital management and F&A related The Technology component involve a HR or Accounting system The Functional Programs involve various Management and practice areas for HR and F&A The Managed asset for the groups are the HR labor force and financial assets/liabilities The Policies and Regulations involve internal HR policies such as new hire employee policies and F&A SOX, SEC, SAS 70 policies Functional responsible leaders are various HR and F&A managers such as Training managers, Comptroller, Tax managers etc. Finally they have a Process Owner such as VP for HR or Finance. So we needed a similar Data Governance structure with roles, responsibilities and activities clearly laid out. With The People component involves Data Stewards The Process in place is a Data Management practice The Technology component are tool for Data Quality, Master Data Management, MDR systems The Functional Programs involve various Management practice areas for the Data Quality Program, MDM program, MDR program The Managed asset for the group is the Corporate Data assets/liabilities The Policies and Regulations involve internal privacy laws, SOX, Data Management policies Functional responsible leaders such as Data Quality managers, MDM Manager, Meta-Data managers etc. Finally to have a Process Owner such as VP for Data Management or a Chief Data Officer. Even though things have changed over the years with regard to having all these pieces for Data Governance to be successful, you do not need to build it out all at once. They can be build out over time, depending on the volume of data your organization handles, the desired expectations at various data boards/councils/streering committees etc. Success will come over a long time since Data Management efforts are not run like projects with start and end dates but more like programs that are continuous programs as with any other strategically vital area in an organization
- #15 Slide 14: Now lets briefly look at the core Digital River Business And then let’s look at the Business challenges and how we went about using these Data Governance and Data Management programs to solve them for (batch mode and real time activities)
- #16 Slide 15: So as mentioned earlier Digital River and subsidiaries were founded in 1994. Over time Digital River specialized in Cloud Commerce outsourcing. At the heart of this business is the Online order checkout workflow. This 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 As we acquired many companies over the years these processes were decentralized and business functions spread across each platform. As a publicly traded company it was important for us to efficiently be able to close our books at the end of the month, quarter or year and report to the SEC. This was taking longer to complete with each acquisition and entailed many manual efforts. So the challenge was how do we go about centralizing all of our platform and create one true source for all Accounting, Reporting, Billing etc. The solution involved implementing an ERP system in the back-end where platforms would take the commerce and transmit the data in batches to the ERP ‘Batches of data’ meant transporting orders/transaction data such as settlement/fulfillment for a period of time (like a day) in a collection at a time to the ERP However, we needed a way to convert raw data into 2 categories Clean data and Dirty Data The image shows where Data Quality comes in order to cleanse data, based on Business rules the Data Steward resolved with a Business steward. The end goal was to build a practice area focused on Data Management based on the definition we had for Data Governance which was: ‘a set of processes that treats Data as a Strategic Area within the enterprise’ So lets look at some of the processes for both the batch mode ERP effort and the real time mode Real Time Address Validation effort
- #17 Slide 16: The Scale of the ERP solution was Enterprise wide and there were numerous platforms on the left feeding data to it in hourly batches The scale of the 2008 ERP implementation was Enterprise wide, and took 18 months to complete. For data to be managed as an asset, Data Stewards met with the Business Owners in meetings called workshops and gathered business rules for terms that were critical to Accounting/Finance/Marketing teams. Data Stewards would then use the Business rules to enhance, cleanse and remediate the data issues using a Data Quality tool that that we acquired called Trillium. The Data Quality tool was selected as a result of an RFP process that evaluated 4 industry vendors in the DG, DQP space (and as a reference we used Gartner Magic Quadrant vendors for Data Quality) In this image you can see Commerce transaction would continue to occur on the e-Commerce platforms, and ETL would move it to the ERP in batches. Batches of data means the orders and other transactions such as settlement/fulfillment for a period of time. The decentralized structure of Digital River was to be eventually changed to a Single Source of Truth via the ERP, and the Data Quality Tool becomes an integral part of the effort. The strategy was broken down to 4 main components: ETL would Extract transactions from the platforms, Transform the attributes to a Staging environment and call the Data Quality tool to fire Business Rules to cleanse the data before the Load to ERP. The Data Quality piece would manage the Data Content, it’s Quality, Governance and Certification of the business rules for attributes identified by F&A as priorities to close our books. and set up cleansed attributes that ETL would load to ERP. The ERP provided Integrity of the data and included process controls to ensure productivity. And BI Reporting would provide flexibility and scalability for accurate reports to be presented to the various groups at an efficient pace. Data Stewards become the People component, Data Quality and MDM became the Technology component. We build policies and programs around the Data Quality, MDM and Meta data registry areas. And the Managed asset was Data. Once we had some 400 business rules loaded into the Data Quality Tool, It became our single point for Data Governance and we now had the capability to reuse the F&A Business rules whenever we acquired another company and/or integrated commerce platform transaction data into the ERP load. However, the ideal way to manage data quality was in real time at the source of entry into our eco-systems, which is at the Commerce platform on the left of the image. So lets at a look at a Real time application next
- #18 Slide 17: This goes back to the heart of our business, which is the Online order checkout workflow. This 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 We focused on getting our Customer Address correct for each order. Since our business involved both Digital Downloads and Physical Shipments, it maybe necessary to only gather billing information for Digital or both Billing and Shipping information for Physical goods to be shipped. In that regard, our Data Quality Technology partner, Trillium provides us with a subscription based reference Data set of Postal Addresses from Postal Authorities for numerous countries that we do business in. The Data Management team along with our Technology partners on the Commerce and Payments side of our business integrated a Real Time Address Validation call for Bill To and Ship to Addresses to be validated against the postal directories of various countries. It was an important step for us considering the volume of sales we do for our clients and the expectations clients have that we do this right. There’s also the regulatory Tax component that would need accurate addresses for proper taxation to occur and this reduced our Shipping losses and fraud rates. So the Goal was to have a single source of truth for all Country Postal Address information. The Data Quality Tool and Postal Directories were the ideal solution we had hoped for in an ideal enterprise license model. The Data Quality Shopper Store Real Time Address Validation Service (RTAV) was initiated at Digital River in Q1 2010. It took a year to deploy to our production environments. It has been successfully used the past 3 years. And the client base using RTAV is expanding. Coming back to the Order Check out workflow at the source platform We had to tear up the order checkout process in order to insert the Data Quality Tool calls for RTAV checks The Data Quality Tool is integrated into the process to enhance the quality of the data about to be distributed and consumed across internal systems For the Address Domain data cleansing is at the source before consumption into Digital River’s ecosystem Our Data Steward/Trillium Software Engineer manages the Postal Directory Updates, Software upgrades to the Servers, Involved in constant monitoring and notification of processing issues As the Business lead I manage the: - Data Quality Tool Product business relationship - Purchase of any additional and necessary pieces of Data Quality Tool software, Country Templates and Country Postal Directories and - Build internal relationships to expand the use of the DQP internally The checkout workflow starts when a product is place in a shopping cart and an order number gets assigned. At that point the shopper is invoked to supply their Billing/Shipping Address for verification (which is validated against the DQ tool address for the particular country). During the Address validation check with the country postal directories, the DQ tool provides a match level code to flag certain address text lines for further correction. These extend from match code 1 to 6: 0. Exact match: Input data successfully matched to directory 1. City/Province or postcode fail: No city/Postcode found/City misspelled 2. Street name failure: No street name on input, either misspelled/missing 3. House number range failure: Range doesn’t match the postal directory 4. Street components failure: Avg. score lower than the acceptable score 5. Multiple possible matches to directory: Input data incomplete 6. Ambiguous Match: a match exists, but too many corrections are required to make the match viable. e.g. Rural Record Failure. Once addresses have been corrected/ validated, the shopper proceeds to enter their payment information (credit card, wire transfer or other) and complete the order.
- #19 Slide 18: Now let’s connect the dots to how we successfully put these Data Governance and Data Management processes and practices in place For that, lets look at the Enterprise Data Management Matrix image we reviewed earlier and focus on some programs
- #20 Slide 19: Here we see the Data Governance Program. Data governance describes the strategy and processes for defining corporate data policies coupled with the organizational structures that include data governance councils and data stewards put in place to monitor, and ensure compliance with those data policies. The objective of data governance is to institute the right levels of control to achieve one of three outcomes: Identify data issues that might have negative business impact; Prioritize those issues in relation to their corresponding business value drivers; and Have data stewards take the proper actions when alerted to the existence of those issues.
- #21 Slide 20: For Digital River, the Data Quality program was critical component to bring about the success of the Data Governance and Data Management practice at Digital River. It involves Data Profiling to understand quality challenges, Data cleansing and enhancement such as with the Address Domain we focused on for the Real Time application. It involves Transformation and correction of the data, such as using recodes and decodes or mapping table rules as was the case for the F&A terms for the ERP Data Matching and consolidation as is the case for managing Customer, Client or other domains in order to avoid setting up duplicates (as is the case in MDM applications) And Monitoring of already implemented business rules, so the quality doesn’t degrade over time and can be managed effectively in partnership with Business stewards As such these programs are necessary to be rolled out systematically, making the necessary business case each time there’s a need or opportunity. But, if you are starting to set up a Data Governance and Data Management team for the 1st time, you might say how do we make the business case and get the program started. Let’s take a look at how we did it at Digital River.
- #22 Slide 21: In each case, for both the Batch mode and Real Time mode we had to make the business case to our Executive staff in order to get their approval and the necessary budgets to get the program started and the justification to hire Data Stewards and set up the team. Business impact/ benefits and return on RTAV objective In Q4 2009 an ROI study similar to the one above was conducted for the Real Time Address validation (RTAV) effort. We repeated using a 4-block template similar to the one conducted for the ERP DQ tool ROI effort. It summarizes Revenue retention, This included taking to various departments to understand $ impacts as a result of weekly manual efforts that were extrapolated for a year Shipping cost savings, This was based on customer reports of lost or missing packages and the costs around customer service efforts and product replacement. A short 1 month window was reviewed for all clients and extrapolated to annual impact A 3-5 year ROI analysis (Capital expenses vs. Operational expenses) and Other soft benefits. Postal address formats/templates vary by country. If 5-7% of a country’s postal addresses change monthly as a result of People moving, New streets being built, Companies going bankrupt etc., it makes sense to purchase the postal directory subscriptions regularly; monthly, quarterly, semi-annually, etc. depending on the country. We identified specific countries that were resulting in the most postal challenges for Digital River and those were first targeted for Postal Directory subscription purchases. The Digital River executive staff and board reviewed the ROI for pros and cons of getting RTAV in place for our shopper stores and approved Data Management to proceed with a request for proposal (RFP) where various vendors were reviewed against our statement of work (SOW), let’s take a look at the RFP process and Vendor selection process
- #23 Slide 22: In 2010, the RFP initiated brought 4 vendors for the Real Time Address Validation effort and Postal Address subscriptions were reviewed and selected using a template similar to the one above. As you can see it categorized Leaders vs Challengers on the Vertical axis against Speed to Market and Cost on the Horizontal axis Costs were gathered for licenses, training, set up etc. and vendor were allowed to make presentations for internal review. A Vendor score card was used to identify the winning vendor. The RTAV DQ tool procurement process was initiated and enterprise contracts for Product licenses and Country postal directory subscriptions acquired. Hardware was procured and set up in our multi datacenter redundant architecture. Vendor professional services contractors were brought in to assist with the software install and production deployment. So what is required after Data Governance and Data Management programs are operational? Well, as with most organizations, that set up SLA’s, KPI’s and Dashboards, we did the same focused on Data Quality
- #24 Slide 23 We put together a Dashboard. ERP Here you see the ERP Data Quality Reporting and Monitoring Dashboard The Monitoring step is about measuring the rate of compliance to a business rule We’ve been historically been able to show Data Quality improvement as a metric (as shown here) You see an increase in Data Quality as a result in application of Data Quality Tool business rules to various ETL Processes for various platforms Hence Data Quality for the batches is measurable against the business rules that were signed off And Data Stewards are responsible for the monitoring Data Quality Dashboards for the ERP are gathered regularly: These Dashboard measure the level of data quality which is equal to the rate of compliance with business rules (Data Quality tool output). Data Quality is measured monthly, with updates in Business Rules. These are then compared from previous month’s report to see if there are any adverse trends and changes. Data Stewards are responsible for acting on the DQ Dashboard metrics which may require a re-validation of the business rule with the Business owner/Technical owner OR which may require changing the Business rule to a newly certified Business rule. Overall, on a monthly basis, we track and monitor Data Quality business rules for over 400+ attributes that are required for the ERP. The DQP increases the Data Quality level by approximately 15-20%. Both internal users and external Clients who use the Business Intelligence reports benefit from the Data Quality program. And thanks to the ERP, ETL, BI, the Data Governance and Data Quality efforts, Digital River is now able to close its Accounting/Financial/Marketing books shortly after a Month end, Quarter end or Year end! RTAV The RTAV service and Data quality solution is capable of managing various language and country combinations that we have to provide Real Time Address Validation coverage against. For some Asian countries we are working out the details around double byte character Web Storefronts and RTAV formats. By providing Real Time Address Validation to our clients on their multi-lingual shopper stores we have added a highly scalable feature solution. It is a remarkable achievement. As our clients expand their Global businesses to new countries, we’ll continue to support the shopper purchases via RTAV subscriptions to postal directories from new countries. For the RTAV Monthly dashboards have been set up to track clients benefiting from RTAV and the order volumes of addresses cleansed. We are also tracking the number of countries covered by our RTAV solution.
- #25 Slide 24: Innovative solutions and Problem solving The implementations of Data Governance and Data Management is catered to multiple groups identified by the Data Governance Steering committees. We have continued to make progress even as corporate org-charts were changing over the years. We leveraged the same Data Quality Tool, Trillium Software for batch mode cleansing and real-time cleansing. We are going Global with these solution for our clients to benefit from it. The evolution of the Data Management team, our Data Quality tools used, organizational reporting structure and dashboards created over the years show that we have consistently proved our successes in this topic. Our systematic approach and innovation culture allow us to deliver new Data Governance and Data Quality related capabilities Let’s look at some final recommendations I have:
- #26 Slide 25: The first recommendation is regarding Leadership and segregation of the Data Governance and Data Management team Historically, Gartner has documented large number of the Fortune 500 companies have initiated efforts in Data Governance and Data Management, However, a high % failure rate is attributed for Data Governance and Data Management efforts in organization. Among the primary reasons for this is the that current teams are under an IT/MIS organizational structure. This: Prevents independent governance of Data, Data is NOT managed as a Strategic Asset There are conflict of interests between Technology and Data Management It is difficult to enforce Quality rules across the enterprise There are High cost and low returns Data becomes silo-driven (like IT…) There’s a sense of Responsibility without Authority The recommendation here is to have a separate team that’s responsible for Data Governance and Data Management. That treats: Data to be Governed as an Independent Asset Provides a centralized authority: CDO / VP Data Mgmt responsible for Data Improves control over compliance and financial risks There’s clear accountability for all aspects of data Results in Cost reductions from uniform DM processes Data rules are now scalable across the enterprise, and over time (growth, acquisitions…) And Data Governance and Data Management are no longer dependent on IT strategy
- #27 Slide 26: The 2nd recommendation is to expand the EDM Matrix organization. Where Ownership of such a Data Management team lies with a CDO (e.g Bank of America, Federal Communications Commission etc.) Who works closely with a Data Management Steering committee Program managers are identified for the DQP, MDM, MDR, LDM, DRDP etc. EDM Matrix Org. with Program leaders, Data Stewards and Data Management Areas (such as Accounting, Marketing, Payments, HR or other business critical strategic departments)
- #28 Slide 27: Other companies set up the Data Governance scope of contol As part of their IT Governance Here you see an image from Baseline Consulting (a premier data governance consulting firm acquired by SAS institute). They’ve broken it down to 2 sections Governance and Management With a Tactical approach being taken in the Data Quality end and things get more strategic on the Governance end Notice the scope of control gets larger on the strategic side
- #29 In Conclusion
- #30 Slide 29: Digital Governance at Digital River Conclusion <Review the Slide> Since Jan 2008 the Data Governance, Data Quality programs and the Data Stewardship programs at Digital River have been rolled out and expanded. The Data Governance Steering Committee continues to allow the Data Management operations to operate independently even while being engaged to meet immediate Business needs that typically take multiple years to get in place e.g. the ERP project, RTAV activities. Thus far, the Data Management team has operated on a small footprint; however, as Global commerce expands in our hyper-connected world via Social Media, Cloud, and Mobile, we expect to continue to manage data quality in our enterprise systems via batch cleansing mode or real time cleansing mode. We also expect to be engaged in Big Data cleansing. As Digital River continues to re-organize and make adjustments to strategy, we expect Data Governance and Data Quality to be part of various Steering committees that the Executive team is setting up for collaborative engagement across the enterprise. Overall, we continue to treat data as a strategic and valued asset to Digital River even as we adapt to business/technological disruptions in the market, internal cultural changes and corporate re-orgs. These are the reasons for continued DG, DQ, EDM best practices, progress and change at Digital River.
- #31 Slide 30: And with that I want to Thank everyone for giving me the opportunity to share the Digital River success story. If you have any questions that you’d like to discuss outside of this session, my contact information is on this slide. I’m active on LinkedIn and welcome contact via that means as well. I’ll now open up to Q & A and hand over control back to Shannon the DGPO Rep to help moderate this section. Thank You.