Data Governance by stealth v0.0.2

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Data Governance by stealth v0.0.2

  1. 1. Selling Data Governance or DG by Stealth? C H R I S T O P H E R B R A D L E Y C H I E F D A T A O F F I C E R
  2. 2. P / 2 Introduction: Who Am I? My blog: Information Management, Life & Petrol http://infomanagementlifeandpetrol.blogspot.com @InfoRacer uk.linkedin.com/in/christophermichaelbradley/ Christopher Bradley Chris@chrismb.co.uk
  3. 3. P / 3 Introduction To Chris Bradley Chris is a leading Information Management strategist with 34 years experience in the Information Management field, Chris works with leading organisations including Total, Barclays, ANZ, GSK, Shell, BP, Statoil, Riyad Bank & Aramco in Data Governance, Information Management Strategy, Data Quality & Master Data Management, Metadata Management and Business Intelligence. He is a Director of DAMA- I, holds the CDMP Master certification, examiner for CDMP, a Fellow of the Chartered Institute of Management Consulting (now IC) member of the MPO, and SME Director of the DM Board. A recognised thought-leader in Information Management Chris is creator of sections of DMBoK 2.0, a columnist, a frequent contributor to industry publications and member of standards authorities. He leads an experts channel on the influential BeyeNETWORK, is a regular speaker at major international conferences, and is the co- author of “Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models”. He also blogs frequently on Information Management (and motorsport).
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  6. 6. P / 6 Recent Presentations DAMA UK Webinar: February 2015; “An Introduction to the Information Disciplines of the DAMA DMBoK” Petroleum Information Management Summit 2015: February 2015, Berlin DE, “How to succeed with MDM and Data Governance” Enterprise Data & Business Intelligence 2014: (IRM), November 2014, London, UK “Data Modelling 101 Workshop” Enterprise Data World: (DataVersity), May 2014, Austin, Texas, “MDM Architectures & How to identify the right Subject Area & tooling for your MDM strategy” E&P Information Management Dubai: (DMBoard),17-19 March 2014, Dubai, UAE “Master Data Management Fundamentals, Architectures & Identify the starting Data Subject Areas” DAMA Australia: (DAMA-A),18-21 November 2013, Melbourne, Australia “DAMA DMBoK 2.0”, “Information Management Fundamentals” 1 day workshop” Data Management & Information Quality Europe: (IRM Conferences), 4-6 November 2013, London, UK “Data Modelling Fundamentals” ½ day workshop: “Myths, Fairy Tales & The Single View” Seminar “Imaginative Innovation - A Look to the Future” DAMA Panel Discussion IPL / Embarcadero series: June 2013, London, UK, “Implementing Effective Data Governance” Riyadh Information Exchange: May 2013, Riyadh, Saudi Arabia, “Big Data – What’s the big fuss?” Enterprise Data World: (Wilshire Conferences), May 2013, San Diego, USA, “Data and Process Blueprinting – A practical approach for rapidly optimising Information Assets” Data Governance & MDM Europe: (IRM Conferences), April 2013, London, “Selecting the Optimum Business approach for MDM success…. Case study with Statoil” E&P Information Management: (SMI Conference), February 2013, London, “Case Study, Using Data Virtualisation for Real Time BI & Analytics” E&P Data Governance: (DMBoard / DG Events), January 2013, Marrakech, Morocco, “Establishing a successful Data Governance program” Big Data 2: (Whitehall), December 2012, London, “The Pillars of successful knowledge management” Financial Information Management Association (FIMA): (WBR), November 2012, London; “Data Strategy as a Business Enabler” Data Modeling Zone: (Technics), November 2012, Baltimore USA “Data Modelling for the business” Data Management & Information Quality Europe: (IRM), November 2012, London; “All you need to know to prepare for DAMA CDMP professional certification” ECIM Exploration & Production: September 2012, Haugesund, Norway: “Enhancing communication through the use of industry standard models; case study in E&P using WITSML” Preparing the Business for MDM success: Threadneedles Executive breakfast briefing series, July 2012, London Big Data – What’s the big fuss?: (Whitehall), Big Data & Analytics, June 2012, London, Enterprise Data World International: (DAMA / Wilshire), May 2012, Atlanta GA, “A Model Driven Data Governance Framework For MDM - Statoil Case Study” “When Two Worlds Collide – Data and Process Architecture Synergies” (rated best workshop in conference); “Petrochemical Information Management utilising PPDM in an Enterprise Information Architecture” Data Governance & MDM Europe: (DAMA / IRM), April 2012, London, “A Model Driven Data Governance Framework For MDM - Statoil Case Study” AAPG Exploration & Production Data Management: April 2012, Dead Sea Jordan; “A Process For Introducing Data Governance into Large Enterprises” PWC & Iron Mountain Corporate Information Management: March 2012, Madrid; “Information Management & Regulatory Compliance” DAMA Scandinavia: March 2012, Stockholm, “Reducing Complexity in Information Management” (rated best presentation in conference) Ovum IT Governance & Planning: March 2012, London; “Data Governance – An Essential Part of IT Governance” American Express Global Technology Conference: November 2011, UK, “All An Enterprise Architect Needs To Know About Information Management” FIMA Europe (Financial Information Management):, November 2011, London; “Confronting The Complexities Of Financial Regulation With A Customer Centric Approach; Applying IPL’s Master Data Management And Data Governance Process In Clydesdale Bank “ Data Management & Information Quality Europe: (DAMA / IRM), November 2011, London, “Assessing & Improving Information Management Effectiveness – Cambridge University Press Case Study”; “Too Good To Be True? – The Truth About Open Source BI” ECIM Exploration & Production: September 12th 14th 2011, Haugesund, Norway: “The Role Of Data Virtualisation In Your EIM Strategy” Enterprise Data World International: (DAMA / Wilshire), April 2011, Chicago IL; “How Do You Want Yours Served? – The Role Of Data Virtualisation And Open Source BI” Data Governance & MDM Europe: (DAMA / IRM), March 2011, London, “Clinical Information Data Governance” Data Management & Information Management Europe: (DAMA / IRM), November 2010, London, “How Do You Get A Business Person To Read A Data Model? DAMA Scandinavia: October 26th-27th 2010, Stockholm, “Incorporating ERP Systems Into Your Overall Models & Information Architecture” (rated best presentation in conference) BPM Europe: (IRM), September 27th – 29th 2010, London, “Learning to Love BPMN 2.0” IPL / Composite Information Management in Pharmaceuticals: September 15th 2010, London, “Clinical Information Management – Are We The Cobblers Children?” ECIM Exploration & Production: September 13th 15th 2010, Haugesund, Norway: “Information Challenges and Solutions” (rated best presentation in conference) Enterprise Architecture Europe: (IRM), June 16th – 18th 2010, London: ½ day workshop; “The Evolution of Enterprise Data Modelling”
  7. 7. P / 7 Recent Publications Book: “Data Modelling For The Business – A Handbook for aligning the business with IT using high-level data models”; Technics Publishing; ISBN 978-0-9771400-7-7; http://www.amazon.com/Data-Modeling-Business-Handbook-High-Level White Paper: “Information is at the heart of ALL Architecture disciplines”,; March 2014 Article: The Bookbinder, the Librarian & a Data Governance story ; July 2013 Article: Data Governance is about Hearts and Minds, not Technology January 2013 White Paper: “The fundamentals of Information Management”, January 2013 White Paper: “Knowledge Management – From justification to delivery”, December 2012 Article: “Chief INFORMATION Officer? Not really” Article, November 2012 White Paper: “Running a successful Knowledge Management Practice” November 2012 White Paper: “Big Data Projects are not one man shows” June 2012 Article: “IPL & Statoil’s innovative approach to Master Data Management in Statoil”, Oil IT Journal, May 2012 White Paper: “Data Modelling is NOT just for DBMS’s” April 2012 Article: “Data Governance in the Financial Services Sector” FSTech Magazine, April 2012 Article: “Data Governance, an essential component of IT Governance" March 2012 Article: “Leveraging a Model Driven approach to Master Data Management in Statoil”, Oil IT Journal, February 2012 Article: “How Data Virtualization Helps Data Integration Strategies” BeyeNETWORK (December 2011) Article: “Approaches & Selection Criteria For organizations approaching data integration programmes” TechTarget (November 2011) Article: Big Data – Same Problems? BeyeNETWORK and TechTarget. (July 2011) Article “10 easy steps to evaluate Data Modelling tools” Information Management, (March 2010) Article “How Do You Want Your Data Served?” Conspectus Magazine (February 2010) Article “How do you want yours served (data that is)” (BeyeNETWORK January 2010) Article “Seven deadly sins of data modelling” (BeyeNETWORK October 2009) Article “Data Modelling is NOT just for DBMS’s” Part 1 BeyeNETWORK July 2009 and Part 2 BeyeNETWORK August 2009 Web Channel: BeyeNETWORK “Chris Bradley Expert Channel” Information Asset Management http://www.b-eye-network.co.uk/channels/1554/ Article: “Preventing a Data Disaster” February 2009, Database Marketing Magazine
  8. 8. P / 8 Data Governance Foundations W H A T I S D A T A G O V E R N A N C E W H Y D A T A G O V E R N A N C E B U S I N E S S C A S E
  9. 9. P / 9 • DQ & MDM Tool Workflow:
  10. 10. P / 10 DAMA Framework IM Disciplines (DMBoK 1) DATA ARCHITECTURE MANAGEMENT DATA DEVELOPMENT DATABASE OPERATIONS MANAGEMENT DATA SECURITY MANAGEMENT REFERENCE & MASTER DATA MANAGEMENT DATA QUALITY MANAGEMENT META DATA MANAGEMENT DOCUMENT & CONTENT MANAGEMENT DATA WAREHOUSE & BUSINESS INTELLIGENCE MANAGEMENT DATA GOVERNANCE
  11. 11. P / 11 What Is Data Governance? The Design & Execution Of Standards & Policies Covering … › Design and operation of a management system to assure that data delivers value and is not a cost › Who can do what to the organisation’s data and how › Ensuring standards are set and met › A strategic & high level view across the whole organisation To Ensure … › Key principles/processes of effective Information Management are put into practice › Continual improvement through the evolution of an Information Management strategy Data Governance Is NOT … › A “one off” Tactical management exercise › The responsibility of the Technology and IT department alone T H E E X E R C I S E O F A U T H O R I T Y A N D C O N T R O L , P L A N N I N G , M O N I T O R I N G , A N D E N F O R C E M E N T O V E R T H E M A N A G E M E N T O F D ATA A S S E T S . ( D A M A I N T E R N A T I O N A L )
  12. 12. P / 12 Data governance – alternate definitions “Data Governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets.” (DAMA International) “Data Governance is a quality control discipline for adding new rigor and discipline to the process of managing, using, improving and protecting organizational information.” (IBM Data Governance Council) “Data Governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.” (Data Governance Institute) “Data Governance is the formal orchestration of people, processes, and technology to enable an organization to leverage data as an enterprise asset.” (MDM Institute)
  13. 13. P / 13 Data Governance – a simple definition “The process of managing and improving data for the benefit of all stakeholders”
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  15. 15. P / 15 Why Is Data Governance Critical? _Higher volumes of data generated by organisations _Proliferation of data-centric systems _Greater demand for reliable information _Tighter regulatory compliance _Competitive advantage _Business change is no longer optional; it’s inevitable _Big Data explosion (and hype)
  16. 16. P / 16 Benefits Of Data Governance _ Assurance and evidence that data is managed effectively reduces regulatory compliance risk and improves confidence in operational and management decisions _ Known individuals, their responsibilities and escalation route reduces the time and effort to resolve data issues _Improved opportunity to rapidly and effectively exploit information for customer insights and competitive advantage _ Increased agility and capability to respond to change and events faster through joint understanding across users and IT _Reduced system design and integration effort _Reduced risk of departmental silos and duplication leading to reconciliation effort and argument I N F O R M A T I O N T H A T I S T R U S T E D A N D F I T F O R P U R P O S E
  17. 17. P / 17 A typical average company loses 30% of revenue and turnover through poor data quality Millions of UK National Health Service patient records sold to insurance firms On average, organizations waste 15-18% of budgets dealing with data inaccuracies The US economy loses $3.1 trillion a year because of poor data quality Why Data Governance?
  18. 18. P / 18 3 motivations for Data Governance 2. Pre-emptive Governance 1. Reactive Governance 3. Proactive Governance
  19. 19. P / 19 Motivations For Data Governance _Tactical exercise _Efforts designed to respond to current pains _Organisation has suffered a regulatory breach or a data disaster R E AC T I V E GOV E R N A NC E _Organisation is facing a major change or threats _Designed to ward off significant issues that could affect success of the company _Probably driven by impending regulatory & compliance needs PRE-EMPTIVE GOVERNANCE _Efforts designed to improve capabilities to resolve risk and data issues. _Build on reactive governance to create an ever-increasing body of validated rules, standards, and tested processes. _Part of a wider Information Management strategy PROACTIVE GOVERNANCE “If your main motivation for Data Governance is Regulation & Compliance, the best you can ever hope to achieve is just to be compliant”
  20. 20. P / 20 Aligning with Business Motivation
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  22. 22. P / 22 What is the Business Motivation Model? The language of strategic planning is often inconsistent. The BMM provides us with a Consistent Language to articulate business strategy. “The BMM is a technique in which one determines an ultimate goal and determines the best strategy for attaining the goal in the current situation” Mission Strategies Tactics Vision Goals Objectives A statement describing the aims, values and overall plan of an organisation. e.g. “To be the leading creator and protector of wealth.” The strategic plan. e.g. “Defend our current customer base to reduce churn and increase repeat business” A concise statement of a desired change. e.g. “To be the leading provider of wealth management services in our major target markets within the next 5 years.” A high level statement of what the plan will achieve. e.g. “Improve customer satisfaction (over the next five years)” A Course of Action that channels efforts towards objectives e.g. “Call first-time customers personally” The outcome of projects improving capabilities, process, assets, etc. e.g. “Develop an operational customer call centre by June 30, 2015”
  23. 23. P / 23 The Business Motivation Model Example The Motivation Model resonates well with business sponsors › Business Stakeholders can often find business architecture models difficult to understand › The Business Motivation model resonates well with business stakeholders allowing us to talk in Business terms › Helps move away from point solutions to focus on business outcomes
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  25. 25. P / 25 Validate and Refine Business Goals _ A Goal is a statement about a state or condition of the enterprise to be brought about or sustained through appropriate Means. _ A Goal amplifies a Vision — that is, it indicates what must be satisfied on a continuing basis to effectively attain the Vision. _ A Goal should be narrow — focused enough that it can be quantified by Objectives. _ A Vision, in contrast, is too broad or grand for it to be specifically measured directly by Objectives. However, determining whether a statement is a Vision or a Goal is often impossible without in depth knowledge of the context and intent of the business planners. In light of the mission and vision and the influencer pressures, validate and refine the goals of the organisation
  26. 26. P / 26 Look for Levers Derive a set of measurable levers of business value and growth by cascading down the drivers of income in your business. _ The levers are intended to be durable even as business strategy shifts. Value levers indicate which business dimensions need to be analysed for change projects. _ Business consultants use the matrix to understand which business architecture dimensions have the greatest impact on each lever, focusing attention on those dimensions most relevant to the levers in focus. Look for levers that can help you address the goals
  27. 27. P / 27 Improvement Levers Example Increase price Increase volume Improve mix Improve process Reduce cost of inputs Improve warehouse utilisation Increase productivity Decrease staffing Optimize scheduling Optimize physical network Decrease staffing Use alternative distribution Lower Customer Service & Order Management Costs Lower I/S costs Lower Finance / Accounting costs Lower HR costs Improve capital planning/ investment process Reduce inventories Reduce A/R increase A/P o Profit-driven marketing efforts: • Target “best” customers • Offer “best” product mix • Improve pricing management • Proactive production planning for inventory management • Most profitable capacity allocation/utilization o Reduced sales management layers o Focus on high-profit accounts o Improved inventory flow visibility • Lower transportation costs • Higher facilities utilization • Less “fire fighting” o Better carrier evaluation/mgmt. o Higher quality Customer Service o Improved Supply Chain visibility • Improved order fill rates • Significantly lower cost • More consistent service • Faster problem resolution o Improved capital stewardship • Increased capital productivity • Reduced inventory investment • Reduced receivables investment o Automated PO requisitions o Improved information for evaluating vendors o Automation of some scheduling functions o Single point of entry eliminates data re-entry and improves accuracy o Faster data reconciliation o Automated billing processes o Automated payroll processes o Moderately lower safety stock inventory o Moderately improved A/R and A/P management Increase revenues Decrease costs Reduce selling costs Reduce distribution costs Reduce administrative costs Increase gross profit Decrease operating expenses Capital deployment Cost of capital Increase net operating profit after tax (NOPAT) (I/S) Improve capital allocation (B/S) Enterprise Value Map VALUE LEVERS TRANSFORMATION BENEFIT (Outcome) AUTOMATION BENEFIT Align benefits with Information
  28. 28. P / 28 Example Decomposition What are our corporate goals? What are the priorities and battlegrounds given our corporate goals? What IT assets and data do we need to support these capabilities? How will our business model change over the next three to five years? What are the key capabilities that will maximize value creation in the business? How do we optimize our IT operating model to deliver the required business capabilities? Earn all Our customers’ business Drive a strong Customer culture Enhanced branch Capability and Power in frontline Transform Service delivery And processes Customer centricity Front office empowerment Channel and Product operational excellence Customer Profile management Relationship management Customer analytics Offer design Product management Integrated Data store Enterprise Service bus Channel platforms Product platforms Security platforms End-user computing Customer analytics engine Universal customer master Integrated sales & Service front end Internet platform transformation Core banking transformation Vision Strategic agenda Business objectives Business capabilities IT capabilities IT investments
  29. 29. P / 29 Where does Data Governance Fit?
  30. 30. P / 30 DATA ARCHITECTURE MANAGEMENT DATA DEVELOPMENT DATABASE OPERATIONS MANAGEMENT DATA SECURITY MANAGEMENT REFERENCE & MASTER DATA MANAGEMENT DATA QUALITY MANAGEMENT META DATA MANAGEMENT DOCUMENT & CONTENT MANAGEMENT DATA WAREHOUSE & BUSINESS INTELLIGENCE MANAGEMENT DATA GOVERNANCE › Enterprise Data Modelling › Value Chain Analysis › Related Data Architecture › External Codes › Internal Codes › Customer Data › Product Data › Dimension Management › Acquisition › Recovery › Tuning › Retention › Purging › Standards › Classifications › Administration › Authentication › Auditing › Analysis › Data modelling › Database Design › Implementation › Strategy › Organisation & Roles › Policies & Standards › Issues › Valuation › Architecture › Implementation › Training & Support › Monitoring & Tuning › Acquisition & Storage › Backup & Recovery › Content Management › Retrieval › Retention › Architecture › Integration › Control › Delivery › Specification › Analysis › Measurement › Improvement Data Governance Is At The Heart Of ALL Information Management Disciplines Information Management Disciplines DAMA-International
  31. 31. P / 31 Data Governance Part Of An Overall EIM Framework I N F O R M A T I O N I S A T T H E H E A R T O F T H E B U S I N E S S & M U S T B E M A N A G E D E F F E C T I V E L Y T O D R I V E V A L U E
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  33. 33. P / 33 Introducing Data Governance B E N E F I T S & P I T F A L L S
  34. 34. P / 34 4 – MAYBE MORE Organisational Models For DG 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. DATA CENTRIC Business appointed FT or PT roles accountable for improvement of key data domains wherever created or used across an organisation, e.g. Data Stewardship. SYSTEMS CENTRIC System owner(s) become(s) the data owner for all data created, amended & deleted by the system for which he / she is responsible. CONTINGENT There is no single best model for data governance, either when initiating data improvement activities, or as Business As Usual. The best model is dependent on the type of data and the circumstances of each initiative, at each stage of maturity.
  35. 35. P / 35 Data Governance Organisations DATA GOVERNANCE COUNCIL The primary and highest authority organisation for data governance. Includes senior managers serving as executive data stewards, DM Leader and the CIO. DATA STEWARDSHIP STEERING COMMITTEE One or more cross-functional groups of coordinating data stewards responsible for support and oversight of a particular data management initiative. DATA STEWARDSHIP TEAM One or more business data stewards collaborating on an area of data management, typically within an assigned subject area, led by a Coordinating Data Steward. DATA GOVERNANCE OFFICE Exists in larger organisations to support the above teams.
  36. 36. P / 36 Data Stewards EXECUTIVE DATA STEWARD Senior Managers who serve on a Data Governance Council. COORDINATING DATA STEWARD Leads and represents teams of business data stewards in discussions across teams and with executive data stewards. Coordinating data stewards are particularly important in large organizations. BUSINESS DATA STEWARD A knowledge worker and business leader recognized as a subject matter expert who is assigned accountability for the data specifications and data quality of specifically assigned business entities, subject areas or databases.
  37. 37. P / 37 Data Governance Activities
  38. 38. P / 38 What’s the evidence? _Starting “bottom up” _Gather the facts – horror stories work well _Undertake Data Quality profiling _Publish DQ metrics › Unconscious competition › Teases out who is responsible for the data › Improvement Projects begin to self form › Ultimately becomes self policing › Data Governance (lite) starts to emerge as the way to address the issues › Momentum & an appetite for DG created E X P O S E T H E P R O B L E M
  39. 39. P / 39 Perception is important _Don’t call it Data Governance (at least at the start) _Start Small _Promote Data Improvement Projects (vs a Data Governance strategy) _Who is responsible for the data? W H A T ’ S I N A N A M E ?
  40. 40. P / 40 Identify Best Practices _Identify in-house good guys _Does anyone actually do it well? _What are current best practices _Where is there some passion & emotion about data, it’s quality and meaning? _Often found in downstream areas who are impacted day to day; e.g. › ETL developers › BI users › Customer Service operators › DBA’s I S A N Y O N E D O I N G I T W E L L ?
  41. 41. P / 41 Join it up _Identify the Islands of excellence / atoll's of mediocrity _Join them up _Community of interest _Promote as best practice _Evolve Organisation structures › Do not set up the target DG organisation too early › Have the target in mind › Develop transition steps I S L A N D S O F E X C E L L E N C E ?
  42. 42. P / 42 Land & expand _Community of interest evolves best practices that work in your environment › A gentle steer & guidance is always useful › Operating models & processes emerge _Communicate successes & widen COI _Establish common glossaries › Always useful across the organization › What do you mean by XYZ? _Infiltrate Data Governance into existing processes › Jump on transformation programs › “Customer First” › Process Improvement _Grow incrementally & eventually “top down” support emerges U N D E R T H E R A D A R ?
  43. 43. P / 43 I N D E P E N D E N T O R C O E X I S T E N T ? MDM & DG
  44. 44. P / 44 Benefits$ # projects / reused objects Portfolio planning / design for reuse Project by project / without reuse Design for reuse: First projects hit a “cost” as there is nothing in place that can be re-used / leveraged for benefit. Project based accounting discourages infrastructure investment. Seed Money: To not penalise initial projects, but rather encourage them to do the “right thing” for the corporation, seed money helps with provision of resources, budget offset etc. Design for reuse: Once a few reusable artefacts, models, Master Data objects, reusable methods, skills etc. are established, projects start to reap big benefits Design in isolation: Initially no interaction outside the confines of “the project” and just a few interfaces will appear attractive as no wider considerations need to be made Design in isolation: Costs increase dramatically with Increasing number of point to point interfaces, undo- redo work as clashes about data concepts explode. Portfolio vs Per Project
  45. 45. P / 45 Plan Big – implement small Business Initiative / Project 1 Some of MD area A needed here Some of MD area B needed here Business Initiative / Project 2 More of MD area A needed here Some of MD area C needed here More of MD area B needed here Business Initiative / Project 3 More of MD area C needed here More of MD area B needed here More of MD area A needed here Business Initiative / Project 4 Lots of MD area D needed here More of MD area B needed here More of MD area A needed here Project4 later includes MD for area D MDM program cannot deliver Data Subject Area D at this time for Project 4. Project 4 gains exemption to add this MD later IN THE CONTEXT OF THE BIG PICTURE IMPLEMENT MDM IN ALIGNMENT WITH BUSINESS INTITIATIVES
  46. 46. P / 46 A Framework For Data Governance
  47. 47. P / 47 Why might DG fail? _Lack of business leadership and commitment _Failure to link Data Governance to organisational goals and benefits _Giving people data responsibility but not equipping them to succeed _Failure to focus on the data that really matters _Placing too much emphasis on data monitoring and not data improvement _Thinking new technology will alone solve the problems _Forgetting Data Governance must embrace all who use data across an organisation _Not delivering benefits early and regularly
  48. 48. P / 48 Data Governance Readiness Assessment Source: R.Brennan
  49. 49. P / 49 Typical Data Governance Operating Models Source: Mitre Source: Informeta Source: Informatica Source: Collibra
  50. 50. P / 50 Common Themes In Operating Models & Frameworks Understand Business Drivers & build a foundation Set the Scope Assess Current position Determine readiness for DG Build Business Case Understand Business Motivation Define the Organisation & approach to introduce DG DG implementation Program Communicatio n plan Organisation structure & bodies Education plan Roles & Responsibilities Apply Create and apply policies Execute communicatio n plan Organisation structure & bodies Execute education & mentoring Develop & roll out standards & procedures Introduce DG Processes Introduce DGO Establish Principles Monitor, Report & Measure Adherence to Principles DG Metrics Feedback & continuous improvement DG Knowledge Base
  51. 51. ESTABLISH FOUNDATION ESTABLISH STRATEGY BUILD BUSINESS CASE AGREE DG SCOPE ESTABLISH AS-IS SITUATION ESTABLISH DG PROGRAM CREATE COMMUNICATION STRATEGY ORGANISATION OWNERS STEWARDS CUSTODIANS STAKEHOLDERS COUNCIL DG GROUPS WORKING GROUPS DGO DIRECTION PRINCIPLES & STANDARDS POLICY PROCESS PROCEDURES Control & Report Control & Report REPORTING & ASSURANCE PERFORMANCE MANAGEMENT CONTINUOUS IMPROVEMENT MATURITY MODEL ContinuousActivity InitialActivity PLAN FOR IMPLEMENTING DATA GOVERNANCE Typical Data Governance Operating Model
  52. 52. P / 52 What is the Business Motivation What Information do we need to run our business What business processes & capabilities must we have What roles are necessary to operate our business What systems do we depend upon to run our business Key is to understand the Business motivation & its operation
  53. 53. P / 53 Data Governance Office D A T A G O V E R N A N C E H A S T O U C H P O I N T S T H R O U G H O U T T H E P R O J E C T L I F E C Y C L E V I A L I A I S O N W I T H T H E D A T A G O V E R N A N C E O F F I C E ( D G O , S I M I L A R T O P M O )
  54. 54. P / 54 Early Step: EIM Maturity Assessment IM Disciplines IM Enablers 2 1.5 2 1.5 1.5 2 1.5 1.5 1.5 2 4 4 4 3 4 4 3.5 4 3.5 4 0 1 2 3 4 5 IM Principles Data Governance IM Planning Data Quality IM Lifecycle Management Data Integration & Access Data Models & Taxonomy Metadata Management Master Data Management DW & BI Information Management Maturity Assessment Current Target 1.5 1.5 1.5 2 1.5 1.5 3.5 3.5 4 3.5 3 3 0 1 2 3 4 5 People Processes Executive Sponsorship/Leadership Technology Compliance Measurement Information Management Enablers Maturity Assessment Current Target
  55. 55. P / 55 Applying The Framework Maturity Assessment Current Status Vision & Strategy Org. & People DM & Measures Processes & W/flows Comms & Training Tools & Technology . Roadmap Implementation Plan Business Justification DGVision Business Drivers Desired State
  56. 56. P / 56 By aligning the various activities and providing an overarching management framework can: _ Identify the dependencies and boundaries of the activities, _ Reduce the likelihood of duplication, and _ Ensure tighter integration across the frameworks. Architecture Framework (TOGAF) IT Governance (COBIT) Business Analysis (BABOK) Data Managemen t (DMBOK) Project Managemen t (PMBOK) IT Service Managemen t (ITIL) Informs Governs System Developmen t (SDLC) Aligning multiple frameworks?
  57. 57. P / 57 Data governance must be embedded within broader governance frameworks. Data governance is designed to govern the data management practices. Data governance is informed by the enterprise information architecture. A closer look at Data Governance The exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. (DAMA International) Data governance is NOT a tactical one off exercise nor the responsibility of the IT Function alone
  58. 58. P / 58 Summary _ Business ownership is key _ Communication is vital _ Must connect and align Data Governance with business motivations, strategies and goals – current & future _ This is not simply an IT problem. Requires holistic solutions – people, process, and technology _ It’s essential to outline & communicate what success can deliver and is delivering _ Establish the current baseline and maturity _ Deliver early & incrementally _ Demonstrate success & real business benefits to sustain business support _ Ensure accountable people are equipped to succeed – knowledge, methods & tools; training & mentoring _ Stealth DG is possible – up to a point Data Governance
  59. 59. P / 59 chris@chrismb.co.uk @inforacer uk.linkedin.com/in/christophermichaelbradley/ +44 7973 184475 infomanagementlifeandpetrol.blogspot.com Contact

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