How to design a Data Governance Program Jim Nielsen 11/30/2016 Knowit 1
4 cold from the beer crate 11/30/2016 Knowit 2
1. Why Data Governance 2. Designing a Data Governance Program 3. Measuring Progress in a Data Governance Program 4. Good to know 11/30/2016 Knowit 3
4 Why Data Governance Knowit 11/30/2016
Information Management 5 Data Governance is the heart of GOVERNANCE DATA Knowit 11/30/2016
Why Data Governance? Issues Goals Impact Improved Access to accurate Align with business Improved customer experience Poor Data Quality & Lack centralized Data priorities and keep focus of Governance Enable fact-based leadership Facilitate stakeholder-centricity on creating business 360˚ view of Custome, Supplier, Risk etc.– value Increased up-selling and cross-selling Report inaccuracy ”single source of truth” Common Data Definition Automated stakeholder engagement Difficulty Improved Data Maintenance Process Improved Productivity accessing Data Common 3rd Party Customer Data Increased business Acquisition & Integration processes effectiveness and agility Difficulty targeting/profiling Improved reporting and analytics Faster time to market More accurate transaction reconcillation Improved ability to measure success Lack of compliance To be in compliance Reduced Risk 11/30/2016 Knowit 6
Data governance is the exercise of authority and control (planning, monitoring, and enforcement) over the management of data assets. - The Data Management Association International Data Governance is the exercise of decision making and authority for data-related matters - The Data Governance Institute 11/30/2016 Knowit 7
Pillars in a Data Governance program Chief Data (Protection) Officer Data Data Data Meta Governance Quality Privacy Data 11/30/2016 Knowit 8
Designing a Data Governance Program 11/30/2016 Knowit 9
There are 7 phases in a Data Governance Program 1 2 3 4 5 6 7 Monitor, Develop a value Prepare a Plan and Design the Deploy the Govern the Measure, statement roadmap Fund program program data Report • Scope • Business • Program plan • Operating model • Policies • Business Data • Measurement Atifacts • Vision Alignment • Change • Communication • Standards Glossary dashboard • Mission Statement Management and training plan • Processes • RACI Matrixes • Data Lineage • Objectives • Data governance • Roles and • Technology • Data Prioritization • Success maturity responsibilities Model Measures 11/30/2016 Knowit 10
Data Governance Maturity Data Governance Operating model 5 Eat the elephant in 4 small bites and start 3 with the head 2 Data Governance Reporting Data Roles & Responsibilities 1 0 Data Governance Programme Principles, Policies & Standards Vision DG Maturity Target DG Maturity Baseline DG Maturity 11/30/2016 Knowit 11
Which operating model should we use? Not one size fits all. Find the one that fits to your organisation Ensure the Operating Model fits the culture of the company 11/30/2016 Knowit 12
Roles and responsibilities • Data Governor • Data Owner • Data Steward • Data Quality Administrator • Data Custodian • Data Custodian 11/30/2016 Knowit 13
Roles and responsibilities Domain roles Data Governor Data Owner Data Steward Data Quality Admin Data Custodian Responsibilities • Appoint Data Owner • Establish data strategy for the • Operationalize data strategy • Measure DQ within • Execute new data initiatives • Approve and enable domain • Manage new data initiatives domain • Implement tools/technology resources for domain • Appoint Data Steward(s) • Define standards for data • Analyze root causes and strategy • Attest to effective data • Control resources for the • Prioritize CDEs for domain design remediations • Implement technical data management in place domain • Define business rules for DQ • Manage, execute and standards • Drive cross-domain • Sponsor and authorize data • Design DQ issue remediation track remediation plans • Implement access permission coordination initiatives plans • Monitor DQ controls plan • Approve CDE prioritization, • Document business metadata • Review and implement • Enable authorized feeds from DQ rules definition, metadata • Determine official sources of DQ standards official source documentation etc. data • Define technical data lineage • Assist in root case analysis and remediation Best practice • Typically most senior • Preferably with P&L • Deep knowledge of domain • Strong analytics • Deep platform knowledge and characteristics head of business unit responsibility data and consumer needs capabilities experience • Ability to influence • Sufficient seniority to enact • Capacity for day-to-day, • Solid understanding of • Ideally from within IT multiple domains change and deliver needs of hands-on execution client data data consumers • Typically a direct report to a • Full understanding of • Authority or influence on front Data Owner data quality principles end • Ability to manage multiple and measurement initiatives 11/30/2016 Knowit 14
Example for Regulatory Reporting domain Senior executive who provides guidance and has Data Governor ultimate responsibility for data within domain No bureacracy Frederik Holmgren, CDO use existing board Senior business leader responsible for effective structure and Data Owner management of data within domain Camilla Sjölund, processes Global Head of Products A subject matter expert within the domain responsible Data Steward for executing and ensuring that data is managed Karin Fröberg, according to policies and standards Global Product SME’s Data Qualty Admin Responsible for measuring data quality, designing and Responsible for development or administration of Data Custodian Casper Pedersen, executing remediation plans, and maintaining DQ systems and ifrastructure to support data management Henrik Serlow, Data & Information standars Chief programmer IT Management DQ resp. 11/30/2016 Knowit 15
Business Data Glossary What is a data element? Create a common understanding of • A column? what things means, • A row? with practical • A dataset? examples to avoid ambiguity What is a attribute to a data element? • Simple attribute: • Name, Definition, Identifier, Datatype, MaxSize/MinSize, MaxValue/MinValue • Complex attribute: • Consist of underlaying data elements • Address: country, city, postal code and street 11/30/2016 Knowit 16
Which data element should be priorities? • GDPR • 120 CDE’s 1. Regulator/ Compliance 71 2. P/L 3. Risk 14 25 4. Usage 10 19 92 2 • IFRS 9 • BASEL IV • 129 CDE’s • 45 CDE’s 11/30/2016 Knowit 17
Measuring Progress in a Data Governance Program 11/30/2016 Knowit 18
Data Management metrics Target Trend Value T T-1 T-2 T-3 T-4 T-5 A Governance ... >90% 80 82 81 80 72 71 B ... >85% 87 94 84 92 82 78 C ... >90% 70 45 44 47 43 44 D ... >90% 85 84 84 78 71 66 E ... >80% 89 95 91 87 82 79 Data Quality ... 100% 100 100 100 88 74 72 A B ... >90% 56 54 55 54 55 55 C ... 100% 85 80 75 70 65 50 D ... 100% 84 68 66 63 61 58 E ... 100% 95 90 90 88 89 87 F ... 100% 95 94 96 96 93 93 A Data Privacy ... >90% 100 90 85 83 82 82 B ... >90% 50 42 43 39 41 40 ... 100% 100 100 95 97 5 6 C ... 100% 85 85 80 60 55 55 D E ... >95% 90 92 88 91 90 94 Meta-data ... >80% 77 83 82 75 79 86 A ... 100% 95 88 74 72 66 55 B ... >90% 88 80 82 81 80 72 C ... >80% 72 74 75 75 74 75 D ... >70% 84 85 86 85 86 85 E ... >60% 74 88 74 72 66 55 F
20 Good to Know Knowit 11/30/2016
Where will you meet residence? Competing priorities and Data ownership and other Politics!! lack of resources territorial issues Lack of cross-business Resistance to Lack of executive unit coordination accountability sponsorship and buyin Inexperience with cross- Lack of business Lack of data governance functional initiatives justification understanding 11/30/2016 Knowit 21
These general advise stills holds • Clear lines of communications • Regular interaction with executive management • Ensure communication methods to enforce policies at the steward and stakeholder level • Demonstrate staying power! Data governance is a change issue and requires involvement of all stakeholder. • Invite stewards, project managers, stakeholders etc. to provide status updates on critical initiatives that affect the data 11/30/2016 Knowit 22
These general advise stills holds • No Ivory tower, no silver bullets. Use real life examples to get buy in from local business units. • Ensure the Operating Model fits the culture of the company • Ensure solid alignment between Business & IT • Clearly defined and measureable success criteria • Small iterations vs. all or nothing 11/30/2016 Knowit 23
Jim Nielsen Business Advisor & Vd, Knowit Decision Danmark +45 21 12 21 21 jim.nielsen@knowit.dk 11/30/2016 Knowit 24
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