How to Build Data Governance Programs That Lasts: A Business-First Approach
0 likes135 views
Data analytics and Artificial Intelligence play an increasingly pivotal role in most modern organizations. To keep those initiatives on track, enterprises must roll out data governance programs to ensure optimal business value. Data governance has become a fundamental element of success, a key to establishing the component of the data integrity framework in any business. The most successful data governance programs use a business-first approach, delivering quick wins and cultivating sustained success throughout the organization. Unfortunately, many organizations neglect to implement such programs until they experience a negative event that highlights the absence of good data governance. That could be a data breach, a breakdown in data quality, or a compliance action that highlights the lack of effective controls. Once that happens, there are several different paths a data governance initiative might take. A typical scenario often plays out this way: The executive team calls for implementation of a company-wide data governance program. The newly-minted data governance team forges ahead, engaging business users throughout the organization and expecting that everyone will be aligned around a common purpose.
Ad
More Related Content
Similar to How to Build Data Governance Programs That Lasts: A Business-First Approach (20)
More from Precisely (20)
Ad
Recently uploaded (20)
Ad
How to Build Data Governance Programs That Lasts: A Business-First Approach
- 1. How to Build Data Governance Programs That Last A Business-First Approach David Normandeau | Sr. Director Sales, Financial Services Sam Darmo | Senior Sales Engineer
- 2. Exploding need for trusted data 83% of CEOs want their organisation to be more data-driven Digital transformation investments to top $6.8 trillion globally by 2023 68% of Fortune 1000 businesses now have CDOs – up 6x in the last decade Global data infrastructure spending expected to reach $200 billion this year Data is the fuel for decision-making today 2 IDC IDC Gartner Forbes
- 3. Source: Corinium Intelligence 2021 73% say a lack of technology or services to facilitate data integration is creating challenges for their teams 82% say data quality concerns represent a barrier to their digital transformation projects 80% find it challenging to ensure data is enriched at scale consistently 82% say deploying accessible location data across their enterprises is challenging There’s still work to do 3 We surveyed 300+ C-Level Data Executives in the Americas, EMEA and Asia Pacific
- 4. Data integrity is… data with maximum accuracy, consistency, and context for confident business decision-making Data Integrity
- 5. LOCATION QUALITY GOVERNANCE ENRICHMENT INTEGRATION Your unique data integrity journey will reflect your business needs Data Integrity Data integrity is a journey 5 • Every journey to data integrity is unique and driven by business initiatives • Market trends are accelerating the need for data integrity • Precisely addresses needs across the data integrity journey The Precisely Data Integrity Suite unites the steps to data integrity that unlock incremental value
- 6. How to Build “Business-First” Data Governance?
- 7. Session takeaways • How to build a program that links to the business goals and value. • How to engage across stakeholder levels. • How to prioritise the data and capabilities that matter most initially. • How to show consistency, demonstrate success and build trust.
- 8. 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
- 9. “We need to govern our data!” 9 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”
- 10. Successful programs link Data Governance to business goals
- 11. Business goals inform your steps Data to minimize risk Data to make decisions Data to run 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 Optimise working capital Enhance customer care Improve product traceability Reduce attrition Lower operating expenses Facilitate M&A
- 12. How business goals drives the Data Governance. Data to minimize risk Data to make decisions Data to run 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
- 13. 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
- 14. 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 ”
- 15. 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 “What do we need to personalize our outreach to reduce churn?”
- 16. 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
- 17. The Value Story • 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… Value metrics come together at each level to tell a complete story that resonates. As a result… Lead to
- 18. Session takeaways • Link data governance program initiatives to higher-level business goals, stakeholders, and business outcomes. • Communicate Governance Value across three levels – Strategic, Operational, and Tactical. • Deploy Data governance capabilities that directly serve as both painkiller and vitamins to protect and grow business. • Quantify business impact with the value metrics that resonate across each level.
- 20. Successful programs prioritize the data that matters
- 21. 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
- 22. Successful programs build value across three levels
- 23. 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
- 24. Business-first Data Governance Prioritize what matters
- 25. Business-first Data Governance Build value across 3 levels Operational Strategic Tactical