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DAS Slides: Data Governance - Combining Data Management with Organizational Change

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Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. Join Nigel Turner and Donna Burbank as they provide an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization.

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DAS Slides: Data Governance - Combining Data Management with Organizational Change

  1. 1. Copyright Global Data Strategy, Ltd. 2019 Data Governance - Combining Data Management with Organizational Change Donna Burbank and Nigel Turner Global Data Strategy, Ltd. April 25th 2019 Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  2. 2. Global Data Strategy, Ltd. 2019 Donna Burbank 2 Donna is a recognised industry expert in information management with over 20 years of experience in data strategy, information management, data modeling, metadata management, and enterprise architecture. Her background is multi-faceted across consulting, product development, product management, brand strategy, marketing, and business leadership. She is currently the Managing Director at Global Data Strategy, Ltd., an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. In past roles, she has served in key brand strategy and product management roles at CA Technologies and Embarcadero Technologies for several of the leading data management products in the market. As an active contributor to the data management community, she is a long time DAMA International member, Past President and Advisor to the DAMA Rocky Mountain chapter, and was recently awarded the Excellence in Data Management Award from DAMA International in 2016. Donna is also an analyst at the Boulder BI Train Trust (BBBT) where she provides advice and gains insight on the latest BI and Analytics software in the market. She was on several review committees for the Object Management Group’s for key information management and process modeling notations. She has worked with dozens of Fortune 500 companies worldwide in the Americas, Europe, Asia, and Africa and speaks regularly at industry conferences. She has co- authored two books: Data Modeling for the Business and Data Modeling Made Simple with ERwin Data Modeler and is a regular contributor to industry publications. She can be reached at donna.burbank@globaldatastrategy.com Donna is based in Boulder, Colorado, USA. Follow on Twitter @donnaburbank Twitter Event hashtag: #DAStrategies
  3. 3. Global Data Strategy, Ltd. 2019 Nigel Turner Nigel Turner has worked in Information Management (IM) and related areas for over 20 years. This experience has embraced Data Governance, Information Strategy, Data Quality, Data Governance, Master Data Management, & Business Intelligence. He spent much of his career in British Telecommunications Group (BT) where he led a series of enterprise wide IM & data governance initiatives. After leaving BT in 2010 Nigel became VP of Information Management Strategy at Harte Hanks Trillium Software, a leading global provider of Data Quality & Data Governance tools and consultancy. Here he engaged with over 150 customer organizations from all parts of the globe. Currently Principal Consultant for EMEA at Global Data Strategy, Ltd, he has been a principal consultant at such firms as FromHereOn and IPL, where he has led Data Governance engagement with customers such as First Great Western. Nigel is a well known thought leader in Information Management and has presented at many international conferences. He also works part time at Cardiff University, where he is setting up a Student Software Enterprise company. In addition he has also been a part time Associate Lecturer at the UK Open University where he taught Systems & Management. Nigel is very active in professional Data Management organizations and is an elected Data Management Association (DAMA) UK Committee member. He was the joint winner of DAMA International’s 2015 Community Award for the work he initiated and led in setting up a mentoring scheme in the UK where experienced DAMA professionals coach and support newer data management professionals. Nigel is based in Cardiff, Wales, UK. Follow on Twitter @NigelTurner8 Today’s hashtag: # DAStrategies
  4. 4. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 24 - on demand Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 18 - on demand Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 28 - on demand Data Modeling at the Environment Agency of England - Case Study • April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner) • May 23 Master Data Management - Aligning Data, Process, and Governance • June 27 Enterprise Architecture vs. Data Architecture • July 25 Metadata Management: from Technical Architecture & Business Techniques • August 22 Data Quality Best Practices (w/ guest Nigel Turner) • Sept 26 Self Service BI & Analytics: Architecting for Collaboration • October 24 Data Modeling Best Practices: Business and Technical Approaches • December 3 Building a Future-State Data Architecture Plan: Where to Begin? 4 This Year’s Lineup
  5. 5. Global Data Strategy, Ltd. 2019 Today’s Topic Data Governance is both a technical and an organizational discipline, and getting Data Governance right requires a combination of Data Management fundamentals aligned with organizational change and stakeholder buy-in. This webinar provides an architecture-based approach to aligning business motivation, organizational change, Metadata Management, Data Architecture and more in a concrete, practical way to achieve success in your organization. 5
  6. 6. Global Data Strategy, Ltd. 2019 Gartner’s View – 21st Century: The Rise of the Data Driven, Digital Business 6 • To manage its people, a digital company needs a HR function to manage recruitment, skills & people retention • To manage its IT, needs an IT function to ensure its technology investment supports its business aspirations • To manage its processes, needs Process management to drive the design and deployment of its automated and manual processes • To manage its data, needs a Data management function to develop & improve its data assets to underpin its People, Process & Technology functions
  7. 7. Global Data Strategy, Ltd. 2019 DAMA – Data Disciplines Data Management Book of Knowledge (DMBOK) – Version 2 2017 11 DATA MANAGEMENT DISCIPLINES FUNCTIONAL AREAS Data Architecture Data Modelling & Design Data Storage & Operations Data Security Data Integration & Interoperability Documents & Content Reference & Master Data Data Warehousing & Business Intelligence Data Quality Data Governance Meta-data 7
  8. 8. Global Data Strategy, Ltd. 2019 Aligning Business and Data Strategy www.globaldatastrategy.com A Successful Data Strategy links Business Goals with Technology Solutions Level 1 “Top-Down” alignment with business priorities Level 5 “Bottom-Up” management & inventory of data sources Level 2 Managing the people, process, policies & culture around data Level 4 Coordinating & integrating disparate data sources Level 3 Leveraging data for strategic advantage Copyright 2019 Global Data Strategy, Ltd
  9. 9. Global Data Strategy, Ltd. 2019 Data Governance – a Simple Perspective Data Governance is a business led continuous process of improving data for the benefit of all data stakeholders 9 What is Data Governance – the Global Data Strategy Definition Business Led Continuous Process • Data is a business asset and so must be owned by the business • Data Governance must be a business as usual activity; it’s not a project with an end date Improving Data • Governance must demonstrate business improvement through better data • Monitoring data without an improvement agenda is pointless Benefit • The benefits of Data Governance must be real and measurable • All stakeholders (Business, IT, Customers, Suppliers, Regulators etc.) should recognize the benefits it brings to them
  10. 10. Global Data Strategy, Ltd. 2019 Lack of Governance & Accountability for Data: All Too Common 10 In many organisations, nobody is formally responsible for data and its governance… …so bad data never gets systematically fixed “If we are all supposed to be responsible for data, no one is responsible and nothing changes” (Quote from senior GDS client – 2019)
  11. 11. Global Data Strategy, Ltd. 2019 How Can You Ensure Data Governance is Successful? 11 Main enablers of success Have a clear vision of what good looks like Focus on & prioritize the data that really matters Set realistic expectations Use a recognised industry framework Align efforts with business benefits Equip people to succeed Learn from best practice Recognise that there is no ‘one size fits all’ solution
  12. 12. Global Data Strategy, Ltd. 2019 Data is Part of a Wider Enterprise Architecture • Enterprise Architecture provides a high-level view of the people, processes, applications, and data of an organization • Putting data in business context, e.g. • How does data link to the rest of my organization? • If I change data, what business processes are affected? • Customer Journey Maps • Customer Journey Maps w/ Data Overlay
  13. 13. Global Data Strategy, Ltd. 2019 Sample Business Motivation Model 13 Corporate Mission Corporate Vision Goals & Objectives To provide a full service online retail experience for art supplies and craft products. To be the respected source of art products worldwide, creating an online community of art enthusiasts. Artful Art Supplies ArtfulArt C External Drivers Digital Self-Service Increasing Regulation Pressures Online Community & Social Media Customer Demand for Instant Provision Internal Drivers Cost Reduction Targeted Marketing 360 View of Customer Brand Reputation Community Building Revenue Growth C Accountability • Create a Data Governance Framework • Define clear roles & responsibilities for both business & IT staff • Publish a corporate information policy • Document data standards • Train all staff in data accountability C Quality • Define measures & KPIs for key data items • Report & monitor on data quality improvements • Develop repeatable processes for data quality improvement • Implement data quality checks as BAU business activities C Culture • Ensure that all roles understand their contribution to data quality • Promote business benefits of better data quality • Engage in innovative ways to leverage data for strategic advantage • Create data-centric communities of interest • Corporate-level Mission & Vision • May already be created or may need to create as part of project. • Project-level, Data-Centric Drivers • External Drivers are what you’re facing in the industry • Internal Drivers reflect internal corporate initiatives. • Project-level, Data-Centric Goals & Objectives • Clear direction for the project • Use marketing-style headings where possible
  14. 14. Global Data Strategy, Ltd. 2019 Business Capability Models • A business capability model outlines the core functional areas of the organization. • Note: this is not the same as an organizational chart • Capabilities can be overlaid with key data domains to create a “heat map” of cross-functional data usage. 14 Core Business Shared Services Artful Art’s Business Capabilities Etc. – sample subset only Product Development R&D Product Management Product Manufacturing Packaging Marketing Product Messaging Branding Product Launch Campaign Development Lead Generation Pricing Sales Pipeline Management Customer Relationship Quotes & Tenders Research & Development Branding & Go-to-Market Partner Management Sales & Distribution Human Resources Recruitment Employee Training Performance Management Legal Compliance Contract Management Data Domains Customer Product Account Etc.
  15. 15. Global Data Strategy, Ltd. 2019 The Critical Role of Data Architecture & Governance 15 A high-level data architecture provides the roadmap for data strategy & associated governance. Business data model What data do we prioritize? Where is this data used? Business process models Where is this data stored? Data architecture diagram What rules apply to this data? Business rules & policy What is the quality of the data? Data quality dashboard Business Glossary This architecture provides a guide for small, targeted projects for business value to add additional detail. What data best supports our Brokers? What data best supports our Customers? What data can we use to best Price our Policies? Home Auto Commercial What external data can we use for business advantage?
  16. 16. Global Data Strategy, Ltd. 2019 Process Models • Process models are a helpful tool for describing core business processes. • “Swimlanes” outline organizational considerations • Data can be mapped to key business processes to understand creation & usage of information. 16 Identifying key data dependencies in core business processes
  17. 17. Global Data Strategy, Ltd. 2019 Customer Journey Map • A customer journey map outlines key phases of the customer in their “journey”. • They are similar to a process model, but with a different focus & perspective. • Creating a data overlay is a helpful way to see the key data touched at each point in the journey. • Journey maps can be created for other industries as well, e.g. Student, Patient, etc. 17
  18. 18. Global Data Strategy, Ltd. 2019 Applying a Structured Data Governance Framework Organization & People Process & Workflows Data Management & Measures Culture & Communication Vision & Strategy Tools & Technology Business Goals & Objectives Data Issues & Challenges
  19. 19. Global Data Strategy, Ltd. 2019 The Benefits of Applying a Data Governance Framework • Aligns Data Governance efforts with high priority business needs and key data • Ensures a holistic approach to Data Governance: • People • Process • Technology • Can help to identify areas where: • No additional action is required • Components are partly in place but need enhancement or further development • Little or nothing is in place and so new workstreams are required • Provides a baseline and helps define targets for a Data Governance program • Structured approach, but recognises that every organization is unique and so activities can be tailored to each organization 19
  20. 20. Global Data Strategy, Ltd. 2019 Building the Data Governance Framework 20 Vision & Strategy Organization & People Processes & Workflows Data Management & Measures Culture & Communications Tools & Technology Is there a clear understanding of the strategic goals of your organization & the need for enterprise data governance? Who are the key data stakeholders within and outside your organization? Do business process design and operations management take data needs into account? Has key data been identified, defined and analysed? Has the importance of data been communicated across the organization? Is there a data communications plan? Is there a coherent data architecture in place to define and guide how data is captured, processed, stored and used? How does your organization rely on data – now and in the future? Who are the primary data producers, consumers & modifiers? Are there any specific data management / improvement processes in place? Have data models been built – conceptual / logical / physical? Is the value of good data management understood and championed by senior managers? What primary IT systems and platforms are used to store and process key data? What impact are data problems currently having on your organization? Are individuals formally accountable for data ownership? Are there issue and workflow management processes to address data problems? Has the relationship between business processes and data been mapped? Do all employees and third parties receive data awareness and improvement education and training? Do design gateways exist to ensure data needs are taken into account in new & modified platforms? Do you have a data governance policy? Are employees trained in good data management practices? Has there been any analysis of the efficiency and effectiveness of how data is managed within operational business processes? Are data shortcomings known, measured & recorded? Are there communication channels for communicating best practice in data management? What specialist data management tools are currently in use? What are the overall expected benefits of better data governance? Are there any channels through which data shortcomings can be highlighted and investigated? How does the business and IT interact to manage data improvement? Are there are formal standards & rules specifying how data should be managed and improved? Are there internal success stories that could be used to promote better data management across the organization? What metadata is captured and stored?
  21. 21. Global Data Strategy, Ltd. 2019 Building the Data Governance Framework 21 Vision & Strategy Organization & People Processes & Workflows Data Management & Measures Culture & Communications Tools & Technology Is there a clear understanding of the strategic goals of your organization & the need for enterprise data governance? Who are the key data stakeholders within and outside your organization? Do business process design and operations management take data needs into account? Has key data been identified, defined and analysed? Has the importance of data been communicated across the organization? Is there a data communications plan? Is there a coherent data architecture in place to define and guide how data is captured, processed, stored and used? How does your organization rely on data – now and in the future? Who are the primary data producers, consumers & modifiers? Are there any specific data management / improvement processes in place? Have data models been built – conceptual / logical / physical? Is the value of good data management understood and championed by senior managers? What primary IT systems and platforms are used to store and process key data? What impact are data problems currently having on your organization? Are individuals formally accountable for data ownership? Are there issue and workflow management processes to address data problems? Has the relationship between business processes and data been mapped? Do all employees and third parties receive data awareness and improvement education and training? Do design gateways exist to ensure data needs are taken into account in new & modified platforms? Do you have a data governance policy? Are employees trained in good data management practices? Has there been any analysis of the efficiency and effectiveness of how data is managed within operational business processes? Are data shortcomings known, measured & recorded? Are there communication channels for communicating best practice in data management? What specialist data management tools are currently in use? What are the overall expected benefits of better data governance? Are there any channels through which data shortcomings can be highlighted and investigated? How does the business and IT interact to manage data improvement? Are there are formal standards & rules specifying how data should be managed and improved? Are there internal success stories that could be used to promote better data management across the organization? What metadata is captured and stored?
  22. 22. Global Data Strategy, Ltd. 2019 Example Data Governance Organizational Structure 22 Executive Level • Executive support • Data advocacy • Vital to have ELT level sponsor and champion Strategic Level • Sets strategic direction for Data Governance • Owns the Data Governance Roadmap • Identification of working groups as needed • Funding within budgeted amounts for data quality & governance • Identification of data stewards for key data areas • Arbiter in the case of conflicting needs, definitions, or priorities around cross-functional use of data. Tactical Level Data Owners & Data Stewards by Data Domain (e.g. Property, Customer, Finance etc.) Executive Sponsor Dept. Head 1 Dept. Head 3 Dept. Head 6Dept. Head 4 Dept. Head 5Dept. Head 2 Data Domain Working Groups Data Steward • Create Data Improvement Plans for Data Domains • Propose and progress data improvement initiatives • Report progress to Data Governance Steering Group • Escalate cross-domain issues and barriers to DG Steering Group Updates & Escalation Creation & Direction Technical Role Business Role Key Updates & Escalation Executive Leadership Team Data Governance Steering Group Other SMEs (e.g. DPO, HR, Legal) Data Owner 1 Data Governance Lead IT Representative CIO Business Data Stakeholders Technical Data Stakeholders Other SMEs e.g. DPO, HR Data Owner 2 Data Owner 3 Data Owner 5Data Owner 4 Data Owner 6 Invited Data Stewards Lead Role Data Improvement Plans & Activities Leadership Management & Progression
  23. 23. Global Data Strategy, Ltd. 2019 Sample Organizational Structures 23 The Data Governance Organization is not the same for all Large Global Telco Small Professional Development Organization Data Governance Steering Group IT Data Subject Matter Experts Business Data Stewards Data Governance Working Group Legal & Regulatory Data Subject Matter Experts Large Consumer Energy Company Data Steering Group Data Quality Board Data Steward 2Data Steward 1 Data Steward …15 UK Energy Company Data Owners Divisional Data Stewardship Groups Data Stewards Lead Data Stewards Data Governance Steering Group
  24. 24. Global Data Strategy, Ltd. 2019 Federated, Global Manufacturing & Retail 24 Data Innovation Council Supply Chan R&D Etc Etc Operations Consumer Marketing Data Architecture Digital Transformation Team “Digital Update” Data Innovation Teams Agile Project Teams Agile Project Teams Agile Development Cycles
  25. 25. Global Data Strategy, Ltd. 2019 5 Models of Data Governance & Stewardship Model Description Process Centric Process owner(s) become(s) the data owner for all data created, amended & deleted by the business process for which he / she is responsible (e.g. Claims process, Billing process, etc.) Systems Centric System owner(s) become(s) the data owner for all data created, amended & deleted by the IT system for which he / she is responsible (e.g. CRM, Billing System, etc.) Data Domain Centric Business appointed full or part-time roles accountable for improvement of key data domains, created, stored or used across an organization (e.g. Patient, Student, Product, Customer, etc.) Organization Centric Business appointed FT or PT roles accountable for improvement of key data domains on the basis of departmental boundaries (e.g. Finance, Marketing, Clinical, etc.) or geographical locations. Blended In large and complex organizations, an overall Data Governance program may consist of combinations of some or all of the above models 25 • There are diverse ways to implement data stewardship, unique to each organization.
  26. 26. Global Data Strategy, Ltd. 2019 Which Data Governance & Stewardship Model to Apply? Model Advantages Drawbacks Process Centric • Works well where E2E processes are clearly defined & owned • Processes create & update data, so synergies self-evident • Data is a measure of process efficiency • Data improvement usually requires process change • Helps engage business stakeholders • Multiple data ownership where data is used / shared across processes (e.g. Product, Customer) • Process & data objectives may clash, e.g. speed versus quality • Process-centric view of data may encourage multiple partial improvements, so sub-optimizing opportunities Systems Centric • Effective where master / system of record data sources are in place • Aligns with IT view of the world, so helps secure IT support for Data Governance initiatives • Business systems owners (if they exist) often key budget holders & potential data owners • No E2E perspective of data problems, particularly upstream and downstream sources and processes • Tends to encourage IT centric, rather than business centric, view of problems and solutions • Ineffective if business ownership of systems not embedded in current organizational culture Data Domain Centric • Potential complete view & ownership of key, shared data across the organization • Easier to define and enforce data policies and business rules • Simplest and most transparent Data Governance model • Only practical in smaller, more centralized organisations • Funding Data Governance can be problematic • Danger that others in organization feel little or no responsibility for the data Organization Centric • Aligns Data Governance with existing organizational / functional boundaries & authority • Easier to answer the “What’s in it for me?” question from stakeholders • May be best in widely dispersed, global organizations • Can reinforce existing siloed structures and thinking • Harder to analyse and address E2E cross-business issues with data, so again sub-optimizing opportunities • May discourage cross-business collaboration and reinforce “My data” attitudes Blended • Ensures that ownership and stewardship are unique to a specific organization and best aligns with existing culture • May best meet the needs of large, global, complex organizations • DG actors selected on basis of pragmatism, not current role • Can create greater complexity of Data Governance roles and responsibilities • Requires strong Data Governance leadership from the centre to work 26
  27. 27. Global Data Strategy, Ltd. 2019 Aligning Governance with Current Corporate Culture • Governance structure should reflect the culture of the organization • Formal ‘top’ down or more federated / ‘collaborative’ • Agile or Waterfall • Meetings or Wikis • Align with Business Environment • Offense or Defense - Risk or Opportunity? • Pace and Timing • Complement existing Governance structures & roles • Easier to gain business and IT stakeholder commitment • Empowers data owners and data stewards if they already have existing authority for other types of Governance (e.g. systems, processes etc.) • Work with what works….easier path to success • Language Matters • ‘Minutes’ or ‘Action Log’ • Steering Committee, Council, Tribe, Collaborative, etc. 27 Key Learning Points
  28. 28. Global Data Strategy, Ltd. 2019 Data Governance Organization: Use Cases 28 ORGANIZATION DRIVERS DATA GOVERNANCE PRIORITIES Professional Services (UK) • Endemic data quality (DQ) issues causing process inefficiencies • Unplanned IT & data architecture; heavy reliance on spreadsheets • Repeated manual data cleansing • No formal channels for data collaboration • Extend ToR of IT Governance Board to embrace DG & Data Architecture • Create Working Groups to tackle urgent DQ issues • Link DG efforts to Customer Journey Maps • Focus on Governance of new data architecture, rather than current data estate Insurance (Global) • Merger of two insurance companies created need for better data integration • Legacy companies had different data cultures & priorities • Lack of collaboration between legacy data specialists • Create cross-company Communities of Interest (CoIs) themed around data management topics (e.g. Data Analytics) • Form Working Groups to tackle high priority cross- business data problems • Start with bottom up approach to lay the ground for a future strategic DG program Manufacturing (Global) • Highly competitive industry with growing competitive threat • Some master data sources in place, but data quality issues • Overreliance on spreadsheets to produce reports • Form a DG Steering Group to start and nurture a cross-business data community & network • Align with Business Process focused / engineering culture • Pilot DG concepts and practices via a Proof of Concept pilot to win support for a DG program • Run pilot to demonstrate DG benefits and support a business case for global roll out
  29. 29. Global Data Strategy, Ltd. 2019 Summary • Data Governance is part of a wider business strategy • Business-centric architecture models can facilitate data governance • Organizational structure and roles are critical to success • Aligning with each unique corporate culture is key – one size does not fit all 29
  30. 30. Global Data Strategy, Ltd. 2019 DATAVERSITY Data Architecture Strategies • January 24 Emerging Trends in Data Architecture – What’s the Next Big Thing? • February 18 Building a Data Strategy - Practical Steps for Aligning with Business Goals • March 28 Data Modeling at the Environment Agency of England - Case Study (w/ guest Becky Russell from the EA) • April 25 Data Governance - Combining Data Management with Organizational Change (w/ guest Nigel Turner) • May 23 Master Data Management - Aligning Data, Process, and Governance • June 27 Enterprise Architecture vs. Data Architecture • July 25 Metadata Management: from Technical Architecture & Business Techniques • August 22 Data Quality Best Practices (w/ guest Nigel Turner) • Sept 26 Self Service BI & Analytics: Architecting for Collaboration • October 24 Data Modeling Best Practices: Business and Technical Approaches • December 3 Building a Future-State Data Architecture Plan: Where to Begin? 30 Join Us Next Month
  31. 31. Global Data Strategy, Ltd. 2019 About Global Data Strategy, Ltd • Global Data Strategy is an international information management consulting company that specializes in the alignment of business drivers with data-centric technology. • Our passion is data, and helping organizations enrich their business opportunities through data and information. • Our core values center around providing solutions that are: • Business-Driven: We put the needs of your business first, before we look at any technology solution. • Clear & Relevant: We provide clear explanations using real-world examples. • Customized & Right-Sized: Our implementations are based on the unique needs of your organization’s size, corporate culture, and geography. • High Quality & Technically Precise: We pride ourselves in excellence of execution, with years of technical expertise in the industry. 31 Data-Driven Business Transformation Business Strategy Aligned With Data Strategy Visit www.globaldatastrategy.com for more information
  32. 32. Global Data Strategy, Ltd. 2019 Questions? 32 • Thoughts? Ideas?

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