1 Data Governance Best Practices Rebecca Bolnick Chief Data Officer Maya Vidhyadharan Data Governance Manager Arizona Department of Education
2 Key Issues 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?
3 Key Issues 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 What is Data Governance? Data Governance: An organizational approach to data management, formalized as a set of policies and procedures that encompass the full life cycle of data
5 What is Data Governance? Tools, policies and processes to: Improve data quality and reduce data redundancy Protect sensitive data Ensure data and IT compliance with federal and state regulations Encourage use of data, correctly Platform for robust data analytics
6 In a World without Data Governance Has this ever happened to you? Funding denied due to erroneous data Lost instructional time because of late data Letter Grade negatively impacted because of enrollment error Time wasted cleaning up the same data issues year after year
7 Key Issues 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?
8 The Pillars of Data Governance Data Governance Measure Results of Data Governance Information Lifecycle Management Master Data Management Data-Driven Decisions Metadata Management Data Quality Data Privacy and Security
9 Governance Pillars: Data Quality Data quality is about having data that is fit for purpose. Benefits Accuracy in reporting and business decisions Time and cost savings by removing redundant data storage and reduced time spent on manual data reconciliation Build trust in your data
10 Governance Pillars: Metadata Metadata is data about the data. Business data glossary Technical data dictionary Operational Data Dictionary Data lineage
11 Governance Pillars: Metadata 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.
12 Governance Pillars: Masterdata Management BEFORE AFTER Registrar Acad. Coaches Finance Registrar Acad. Coaches Finance Masterdata
13 Governance Pillars: Masterdata Management Masterdata The critical data used across the organization by multiple divisions Masterdata management Process and policies to achieve consistent master data, which is managed centrally Benefits Single source of all Masterdata, managed centrally and disseminated
14 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 Improved business decisions
15 Governance Pillars: Privacy and Security Data privacy ensures 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
16 Governance Pillars: Information Lifecycle Management A systematic, policy-based approach to data collection, use, retention, and deletion. Benefits Improved timeliness in reporting Improved system performance through the process of archiving unused data
17 Key Issues 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?
18 Easy Data Governance Approach Conduct Governance Maturity Assessment Create a roadmap for areas needing improvement Implement Data Governance Committees Kickstart workgroups in all Governance Pillars Measure Governance benefits and improve
19 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
20 Gartner Governance Maturity Model
21 Governance Maturity Exercise 10 minutes
22 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
23 Step 3: Data Governance Committee Structure Executive Council (Strategic) Data Governance Board (Strategic) Data Steward Committee (Tactical) E S C A L L A T I O N S T R A T E G Y
24 Data Governance Committees Roles and Responsibilities Data Governance Executive Council Level of Focus Who? High, Strategic Senior leadership Roles Champion data governance Provide guidance on tools, policy and processes Sponsor, approve and champion the governance goals in all pillars Communicate expectation and requirements in data governance Address and resolve escalated issues Provide incentives and impetus for data decision making
25 Data Governance Committees Roles and Responsibilities Data Governance Board Level of Focus Middle, Strategic Who? Management with authority to assign work and make process, policy decisions Roles 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
26 Data Governance Committees Roles and Responsibilities Data Stewards Committee Level of Focus Who? Detailed, Tactical Data owners, data managers, or data analysts who implement, work with or enter data Roles 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.
27 Step 4: Workgroups in Pillars Based on decision by Data Governance Committees, spin up workgroups to address critical need areas Tips for focus areas: Quick wins Pressure points Highest Risks
28 Data Quality Workgroup Suggestions 1. Evaluate data for: a) Accuracy b) Uniqueness or duplication c) Consistency d) Conformed to standards e) Timeliness 2. Conduct Data Profiling using a robust data profiling tool 3. Set up continuous improvement projects to bridge the gap between current state and desired state. 4. Make changes to avoid using data that is not meeting the accuracy standards as determined by the Governance Committee
29 Data Analytics Workgroup Suggestions 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.
30 Data Privacy and Security Workgroup Suggestions 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.
31 Information Lifecycle Workgroup Suggestions What is our policy regarding digitizing paper documents? Identify critical documents and set policy to store in proper systems. What is our data archival strategy in all databases and applications? How do we archive structured data (data in databases) to reduce storage costs and improve performance? Something to consider: Store all archive data ion a separate machine so the existing production servers are freed up. This eliminates the lengthy process of data restoration from tapes.
32 Metadata Management Workgroup Suggestions Create a data dictionary Chief Data Stewards and teams to develop and maintain key business terms. Identify critical data elements. Link business terms with technical artifacts. (e.g., Vendor refers to the Supplier table in XYZ database). Tag each business term with its definition, alias/synonyms/homonyms, link to related business terms, Data Steward/Owner, business policy, privacy and security indicators Create technical data dictionary, led by the Chief Data Stewards Create operational metadata Whether the job run failed or had warnings Which database tables or files were read from, written to, or referenced How many rows were read, written to, or referenced When the job started and finished Something to consider: Make this central Metadata repository of Business glossary, technical data dictionary, operational data dictionary easily queryable and available to all users. A good Metadata tool is a great investment for this.
33 Masterdata Management Workgroup Suggestions Identify all your Masterdata What is the single version of truth for each master data, its attributes and relationships between data entities? Determine what changes in history will be captured (versioning) Identify what databases and applications currently or should pull from this central source so there is data consistency across systems in naming, relationships and values.
34 Conclusions and Wrap-Up
35 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
36 Recommendations Things you should do: Obtain solid leadership sponsorship Tie benefits to real goals Obtain stakeholder buy-in Focus on governance and process Continuous improvement Things to Avoid: 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
37 Putting it into Action Monday Morning Identify your champions Conduct maturity assessment to identify critical needs Develop your roadmap Next 90 Days Form workgroups to focus on your organization s top 3 critical data governance needs Establish a system to measure the benefits of data governance (e.g. time savings, number of duplicates or errors) Next 12 Months Reevaluate maturity Measure and communicate benefits and successes of governance across the organization Evolve your strategy to address new issues or tackle the next in line
38 Recommended Resources PTAC Data governance checklist EdFacts data collection process www2.ed.gov/about/inits/ed/edfacts IBM Data Governance Unified Process by Sunil Soares
39 Thank You Rebecca Bolnick Chief Data Officer Maya Vidhyadharan Data Governance Manager
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