1 Big Data Governance ISACA Chapter Annual Conference Sarova Whitesands Hotel, Mombasa 29th - 31st July, 2015 Prof. Ddembe Williams KCA University
2 Presentation Overview 1. What is Data Governance and why is it important? 2. What are the key concepts of Data Governance and Best Practices you need to know 3. What are some steps you can take to create a successful Data Governance strategy? 4. Risks and Issues to Avoid 5. Recommendation and Way Forward
3 Quantity & Centrality of Data Many public organizations and private produce and collect a broad range of different types of data in order to perform their tasks. The extraordinary quantity and centrality of data collected by governments, private sector and the Civil Society make these data particularly significant as a resource for increased transparency.
4 4Vs of Big Data? & #! Velocity Variety Veracity Big data is any type of data Complex structured and unstructured - such as text, sensor data, 4 audio, video, , log files, etc. that can enable data scientists to gain insights when these data types are analyzed together
5 Big Data Governance Can help Build Effective Applications Health Astrophysi cs Emergency Manageme nt Manufactur ing Metrologic al Catastroph es Climate Change Disease Epidemics Business Intelligence Security 5 Africa can take advantage of Big Data to gain useful knowledge and identify unexpected phenomena in many societal or global challenges;
6 What is Data Governance? Data Governance: is an organizational approach to data management, formalized as a set of policies and procedures that encompass the full life cycle of data Tools, policies and processes to: - Improve data quality and reduce data redundancy - Protect and secure sensitive data - Ensure data and IT compliance with National Regulations - Encourage use of data, correctly - Platform for robust data analytics
7 The Pillars of Data Governance Data Governance Metrics of Data Governance Information Lifecycle Management Master Data management Data Driven Decisions Metadata Management Data Quality Data Privacy and security
8 Governance Pillars: Data Quality Data quality is about having data that is fit for purpose. Benefits of Data Quality: - Accuracy in reporting and business decisions - Time and cost savings by removing redundant data storage - Reduced time spent on manual data reconciliation - Build trust in your data
9 Governance Pillars: Metadata Metadata is data about the data. - Business Processes data glossary - Technical data dictionary - Operational Data Dictionary - Data lineage Benefits: Consistent understanding of data definitions Traceability of data transformations Reduced data redundancy Save time and effort of tracking down data or reconciling duplicated data Ability to identify ahead of time possible consequences and impacts of any changes to processes, storage, applications or reports.
10 Governance Pillars: Masterdata Management (1) Finance Production Marketing Finance Product ion Masterdata
11 Governance Pillars: Masterdata Management (2) Masterdata: Critical data used across the organization by multiple departments Masterdata management - Process and policies to achieve consistent master data, which is managed centrally Benefits - Single source of all Masterdata, managed centrally and disseminated
12 Governance Pillars: Data Analytics - Data analytics is the tools and processes for data discovery to improve business outcome. Analytics is the discovery and communication of meaningful patterns in data. - Analytics Governance is the setting of policies and procedures to better align users with the investments in analytic infrastructure. Benefits Cost saving in analytics (system and people) Improved system performance of production servers Data consistency across business divisions
13 Governance Pillars: Privacy & Security Data privacy ensure legal expectation and public expectation of data is met from its creation to archival. Data security protects the data from data loss and data corruption Benefits - Data Privacy and Data Security ensures valuable data is protected from misuse and loss
14 Governance Pillars: Information Life Cycle Management A systematic, policy-based approach to data collection, use, retention and deletion. Benefits Improve timeliness in reporting Improve system performance through the process of archiving unused data
15 Create a Successful Data Governance Strategy 1. Conduct Governance Maturity Assessment 2. Create a roadmap for areas needing improvement 3. Implement Data Governance Committees 4. Start workgroups in all Governance Pillars 5. Measure Governance benefits and improve
16 Step 1: Governance Maturity Evaluation Evaluate where your organization is, and where you want to be Identify areas of weakness, pressure points, or strengths that can be built upon
17 Governance Maturity Model Maturity Level Characteristics 5. Optimised Improvement feedback to data quality, with a mix of data management skills, knowledge etc Type of Metrics to use Data plus feedback for changing the Data 4. Managed Measured data. Formal proactive team Data plus feedback for control 3. Defined 2. Repeatable 1. Initial Data defined and Institutionalised but no dedicated team in place Data dependent on individual, There is informal data management Ad-hoc, Sporadic effort to repair data Specialised area, Finance, marketing etc. Project Management Base line
18 Step 2: Create a Roadmap How to go from your current level of maturity to where you want to be Use results of Maturity Assessment to identify critical needs Move on to step 3 and have Data Governance committees develop roadmap
19 Data Governance Committee Structure E s c a l a t I o n Steering Committee Data Governance Board Data Technical Committee S r a t e g y
20 Data Governance Committees Roles and Responsibilities Data Governance Steering Committee Level of Focus High, Strategic Who? Roles Senior leadership Champion data governance Provide guidance on tools, policy and processes Sponsor, approve and champion the governance goals in all pillars Communicate expectations and requirements in data governance Address and resolve escalated issues Provide incentives and impetus for data decision making
21 Data Governance Committees Roles and Responsibilities Level of Focus Who? Roles Middle, Strategic Data Governance Board Management with authority to assign work and make process, policy decisions Creates and approves policies, processes and tools and define standards to meet Governance goals May initiate or select tools, policies and processes Prioritizes Governance works in all pillars Assigns Governance work Address and resolve issues escalated to this level Participate in workgroups in pillars Meet regularly to address data issues
22 Data Governance Committees Roles and Responsibilities Data Technical Committee Level of Focus Who? Roles Detailed, Tactical Data owners, data managers or data analysts who implement, work with or enter data. Lead and organize workgroups for addressing continuous improvement projects in each Governance Pillar Conduct data training Suggest policies, business rules, processes for each Governance Pillar, to the Data Governance Board Participate in tool selection processes Communicate concerns, issues, and problems with data to the individuals who can influence change.
23 Key Workgroups to be Formed: Data Analytics How many reports do we create, in what areas, and how often? How long does it take to produce each report? What is the time/cost of producing a new report? Are there canned reports we could create to serve multiple purposes? Something to Consider: - Business Intelligence Competency Center (BICC) to educate users, evangelize business intelligence, and develop reports. Having a robust Data Warehouse becomes a key deliverable.
24 Key Workgroups to be Formed: Data Privacy and Security Where is our sensitive data? Has the organization masked its sensitive data in production and non-production environments (development, testing, and training) to comply with privacy regulations? Are database audit controls in place to prevent privileged users, such as DBAs, from accessing private data, such as employee salaries? Set policy and processes to protect sensitive data.
25 Risks and Pitfalls to Avoid Lack of interest or knowledge Disinterested or disengaged executive sponsorship - Members of the data governance council who don't care. - Resistance to change - Ignorance about data governance Getting everyone on the same bus - Limited authority/power of the data governance council - Poor communications through the organizational hierarchy Taking the right approach - Scope is too wide ("big bang") or too narrow - Piecemeal execution without a clear road map and defined release cycles Not showing the value - No measurable deliverables - Lack of or a poor reporting framework - Low value due to incorrect priorities
26 Dos Recommendations: Dos & Don'ts Obtain solid leadership sponsorship Tie benefits to real goals Obtain stakeholder buy-in Focus on governance and process Continuous improvement Don'ts Lack of shared vision and commitment Viewed purely as an IT effort Separate data quality efforts Weak or disjointed metrics Don t boil the ocean Technical goals masquerading as business goals
27 Value of Big Data Governance Improving Organisatinal accountability, transparency, responsiveness and democratic control Promoting citizens self-empowerment, social participation and engagement Building the next generation of empowered knowledge workers Fostering innovation, efficiency and effectiveness in services Creating value for the wider economy Understanding the value chain of Bid data governance
28 Thank You! I wish you a Pleasant Stay in Mombasa Questions & Answers
29 Key References Ubaldi, B. (2013), Open Government Data: Towards Empirical Analysis of Open Government Data Initiatives, OECD Working Papers on Public Governance, No. 22, OECD Publishing:
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