How to Achieve Trusted Data with a Business-First Approach to Data Governance
Download as PPTX, PDF0 likes168 views
The document discusses a business-first approach to data governance, highlighting the challenges organizations face in implementing effective data governance programs, including low awareness among data users and high failure rates of governance initiatives. It emphasizes the importance of aligning data governance with business goals to drive value and outlines various strategies for improving data quality and user productivity through targeted governance efforts. Additionally, the document presents specific metrics and examples illustrating the potential ROI and operational efficiencies gained from a well-structured data governance approach.
How to Achieve Trusted Data with a Business-First Approach to Data Governance
- 1. How to Achieve Trusted Data with a Business-First Approach to Data Governance 2022 Cam Ogden | VP of Product Management Precisely
- 2. 65% of data citizens don’t know how data governance impacts their role The Need for Business-First Governance 80% of governance initiatives fail to deliver expected outcomes 74% of data leaders struggle to calculate the ROI of data governance projects Gartner Forbes HBR
- 3. “We need to govern our data!” A Typical Governance Story LEADERSHIP DATA GOVERNANCE TEAM BUSINESS USERS DATA GOVERNANCE TEAM BUSINESS USERS LEADERSHIP INCITING EVENT Governance spends more time fighting data fires. Business quickly loses interest; stops attending meetings Program investment is deprioritized Asked to help with definitions, approvals, and ownership. Team is tasked with putting program in place Exec calls for a data governance program “We need to get the business involved!” “How does this help me do my job?” “We’re spending a lot more time fighting data fires. We need more meetings…” “These meetings are a waste of time!” “I’m not seeing the ROI”
- 4. How to Build “Business-First” Data Governance
- 5. Successful programs link Data Governance to value drivers
- 6. Business goals inform your steps REPORTING & COMPLIANCE ANALYTICS & INSIGHTS OPERATIONAL EXCELLENCE Data protection Risk and fraud Privacy Safety Regulatory compliance Internal reporting Net Promoter Score Website traffic Targeted marketing Customer retention Buying patterns Customer 360° view Optimize working capital Enhance customer care Improve product traceability Reduce attrition Lower operating expenses Facilitate M&A
- 7. How data drives the business REPORTING & COMPLIANCE ANALYTICS & INSIGHTS OPERATIONAL EXCELLENCE Data protection Risk and fraud Privacy Safety Regulatory compliance Internal reporting Net Promoter Score Website traffic Targeted marketing Customer retention Buying patterns Customer 360° view Optimize working capital Enhance customer care Improve product traceability Reduce attrition Lower operating expenses Facilitate M&A
- 8. Mapping data governance business value Goal Org Stakeholders Expected Outcomes DG Objective DG Capabilities Improve personalization of customer goods and services Marketing Sales Finance • Increase NPS by 5% • 17%+ repeat customer purchases • 11% reduced churn • Establish a common view of trusted customer data assets • Data Catalog • Data Lineage • Approval Workflow • Data Integrity rules Increase sales and revenue through faster speed-to-market Marketing R&D Finance • $15M+ top-line revenue • 25% increased deployment speed • Establish stage gates, rules, policies, and quality measures from Ideation through Commercialization • DQ rules • Business process monitoring • Data quality metrics Increase user productivity by improving time- to-insights Business Analytics IT Data Office • Improve decision- accuracy by 22% • Reduce time-to- insight by 45% • Launch data literacy campaign across business data SMEs • Data lineage • Data Catalog • Automated workflow Reduce supply chain costs associated with errors in orders Vendor Management Finance Supply Chain • Reduce COGS by 4% • Improve OTIF by 15% • Establish common semantics view across order fulfillment data • Impact analysis • DQ rules • Business process monitoring
- 9. Governance as a “painkiller” and “vitamin” Goal DG Objective DG Capabilities Improve personalization of customer goods and services • Establish trusted view of customer data assets • Data Catalog • Data Lineage • Approval Workflow • Data Integrity rules Increase sales through faster speed-to-market • Establish stage gates, rules, policies, and quality measures for Commercialization process • DQ rules • Business process monitoring • Data quality metrics Increase user productivity by improving time-to- insights • Launch data literacy campaign across business data SMEs • Data lineage • Data Catalog • Automated workflow Reduce supply chain costs associated with errors in orders • Establish common semantics view across order fulfillment data • Impact analysis Centralized collection of customer data elements used for marketing and promotion Data profile providing additional context on volume, counts, location, and contents Data lineage flow of upstream/downstream relationships Impact analysis to business processes, metrics, and analytics Approved governance ownership indicating data is certified for access and use Automated approval workflow to grant access to data at source Data integrity metrics to indicate data that is accurate, consistent, and trusted Quality monitoring to trigger notifications below acceptable values P A I N K I L L E R “ M u s t H a v e s ” V I T A M I N “ B o n u s ”
- 10. Successful programs prioritize the data that matters
- 11. Focusing on what matters (critical data adding value) Data Selection of data maintained at the system level (tables and fields) Information Information required to run the business and conduct daily operations KPIs / Performance Measures / Analytics Measuring process effectiveness & enabling sound business decisions Actionable Insights & Business Value Strategic enterprise and organizational business value drivers CRITICAL DATA
- 12. Prioritizing what matters Goal Org Stakeholders Expected Results DG Objective DG Capabilities Improve personalization of customer goods and services Marketing Sales Finance • Increase NPS by 5% • 17%+ repeat customer purchases • 11% reduced churn • Establish a common view of trusted customer data • Data Catalog • Data Lineage • Approval Workflow • Data Integrity rules “We need to personalize our outreach to reduce churn.”
- 13. Successful programs build value across three levels
- 14. Value metrics across three levels Strategic • Business Transformation Lead • CDO / Data & Analytics Lead • CIO Value Metrics: Business Impact / ROI • Process enablement • KPI’s / PPI’s Value Metrics: Performance Improvement • Data Quality (e.g. Accuracy) • # of touches Value Metrics: Efficiency & Effectiveness • Volume / counts • Completeness • Accessibility • Curation times • Scale (# Systems managed) • Data Error % (Rework %) • Cycle time vs SLA’s • Timeliness / availability • Customer sentiment • Project acceleration Operational • Business Process Lead • Data Governance Lead • Data Management Lead • Information Architect Tactical • Business Data SME • Data Analyst / Scientist • Data Steward • Data Maintenance & Quality • Data Engineer
- 15. Building the Business Case Operational Data error % • 10,000 Customer records • Total cost per error is $50 (2 people * 10 minutes * $2.50/minute) • Assume 25% of errors can be avoided with a governance program = 2,500 • 2500 * $50/savings = $125,000 Each data error takes two people to fix Each error takes each person 10 minutes to fix Each person costs $2.50 per minute (fully loaded cost)
- 16. • Master data degrades 2-7% per month (Gartner) • Every data error has a 6X impact on business processes • Avg. cost to fix a data error is $20 • Avg. cost to remediate a data error that impacts a business process is $16,500 • 17% higher data accuracy for organizations that have a global data governance operating model • 13% higher data accuracy for organizations that have established data governance automation • 37% lower “IT projects delivered over budget” for organizations with Data Stewards Choosing benchmarks that resonate Value tied to your specific governance strategy Benchmark data Conservative estimate of benchmark data
- 17. Value metrics across three levels Strategic • Business Transformation Lead • CDO / Data & Analytics Lead • CIO Operational • Business Process Lead • Data Governance Lead • Data Management Lead • Information Architect Tactical • Business Data SME • Data Analyst / Scientist • Data Steward • Data Maintenance & Quality • Data Engineer $182M $14M $28M Full Realization Expected Target Minimum Target Increased Revenue from New Products $2.4M $04M $1.1MM Full Realization Expected Target Minimum Target Cost of Delayed Product Information $3.7MM $1.1M $1.7M Full Realization Expected Target Minimum Target Cost of Inaccurate Product Data
- 18. Use Case • Catalog assets • Terms defined • Quality rules developed • Data owners identified • Issue requests Tactical Value Metrics (Inputs) • FTE Productivity • Data Literacy index • Adoption / NPS • Cycle time • Data sharing Strategic Value Metrics (Outcomes) • Our supplier data setup process has decreased by 25%... • We’re able to identify top 20 vendors 33% faster for contract renegotiations… • And we’ve increased FTE productivity by 20% due to data self-service … • We’ve catalogued 10,000 supplier data assets… • Defined the top 50 critical supplier terms … • Aligned on key rules and policies for each… • And our data quality is showing 90+% accuracy and consistency for supplier spend data… Lead to
- 19. Business-first Data Governance Link to business goals Goal P A I N K I L L E R S V I T A M I N S
- 20. Business-first Data Governance Prioritize what matters
- 21. Business-first Data Governance Build value across 3 levels Operational Strategic Tactical