Data Democratization and AI Drive the Scope for Data Governance
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The webinar discusses how data democratization and AI enhance data governance, highlighting the importance of data integrity and collaboration across business initiatives. Speakers Ken Beutler and Achim Granzen emphasize the unique data integrity journey for organizations, and the tools needed for effective governance, such as APIs, metadata management, and data quality assessment. The session aims to provide actionable insights for establishing a robust data governance framework that aligns with business goals.
Data Democratization and AI Drive the Scope for Data Governance
- 1. How Data Democratization and AI Drive the Scope for Data Governance Ken Beutler, Sr. Director of Product Management, Precisely Guest Speaker: Achim Granzen, Principal Analyst, Forrester
- 2. Housekeeping Webinar Audio • Today’s webcast audio is streamed through your computer speakers • If you need technical assistance with the web interface or audio, please reach out to us using the Q&A box Questions Welcome • Submit your questions at any time during the presentation using the Q&A box. If we don't get to your question, we will follow-up via email Recording and slides • This webinar is being recorded. You will receive an email following the webinar with a link to the recording and slides
- 3. Today’s speaker Achim Granzen Guest Speaker Principal Analyst, Forrester Ken Beutler Senior Director, Product Management, Precisely
- 4. Stepping Up Data Governance Data Democratization gives everyone a voice Data Integrity is the foundation of data activities Data Drives insights Risk & compliance safeguard innovation Data collaboration leverages your assets 7
- 5. LOCATION QUALITY GOVERNANCE ENRICHMENT INTEGRATION Your unique data integrity journey will reflect your business needs Data Integrity Data integrity is a journey 8 • 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. Data Integration Data Observability Data Quality Geo Addressing Spatial Analytics Data Governance Data Enrichment Consistency Accuracy Context Data Integrity Foundation MONITORING ORCHESTRATION SECURITY AUTHENTICATION ENTITLEMENTS 9 APIs AGENTS DATA CATALOG
- 7. Precisely Data Governance module core capabilities Provide ownership and accountability of data assets via roles and responsibilities Data stewardship Link data assets with business goals, KPI’s, and metrics Visualization Visually connect impact analysis, data lineage and business processes with related data assets 3D data lineage Utilize AI techniques to automatically tag data for categorization or to relate data together Machine learning Aggregate data quality results and present data governance scores by asset Metrics & scoring Understand your data with definitions, context and crowdsource updates Business glossary Customize your operating model for reporting issues, questions or approvals Workflow Harvest metadata and allow business and technical metadata to be searchable Data catalog Document policies and standards and their relationships to data Data policy management No code configuration to enable collaboration & adoption Flexible metamodel 10
- 8. Leading governance programs 11 Business accountability for master data and an operating model for data maintenance and data governance Business rules and standards for data, accessible by those providing it, and consistent across relevant business processes Well-defined data processes to ensure that master data is captured correctly, on time, by the right person in support of the business processes they support Tools to capture, monitor and enforce data standards and business rules that are appropriate and ‘fit for purpose’ Successful data governance programs
- 9. Data governance framework 12 Policies, processes, standards • Operating model • Roles & responsibilities • Data governance team • Ownership • Escalation structure Structure • Operating model • Roles & responsibilities • Data governance team • Ownership • Escalation structure Strategy • Vision statement • Objectives & goals • Building business case • Building high level roadmap • Alignment to data strategy Technology • Glossaries • Metadata repository • Business & technical lineage • Workflows • Enable collaboration Metrics • Statistics & analysis • Progress tracking • Issues monitoring • Data governance scores • Data quality scores Communication • Rollout Plan • Communication Plan • Training Plan • Onboarding Data Stewards Plan • Program Management Data Governance
- 10. Business-driven data governance methodology 13 Identify business assets Define business-impacting characteristics Implement & measure Data governance initiated! • Critical fields • Business glossary • Business lineage • Ownership • Data quality standards There are many interconnected data assets across the organization. By prioritizing and focusing on specific business data sets, the data governance program will have achievable goals to demonstrate continuously over time. • KPI’s & business objectives • Transformation metrics • Data quality standards & metrics • Cycle times/Curation times • Volumes/counts
- 11. Precisely delivers business-ready data 14 Data that is trusted Data that is easy to find and understand Data that’s ready to deliver outcomes Precisely
- 12. 3 methods to connect data to business value 15 e.g., Inventory management, customer onboarding, new product introduction, financial reconciliation, etc. e.g., SAP S/4 implementation(s), data remediation system migrations, data science & engineering, etc. e.g., Enterprise KPIs / metrics, data privacy & protection, strategic business drivers, etc. Bottom up Middle out Top down Critical data that drives business processes and operations Middle out Critical data assets that have operational, compliance and analytical business impacts Bottom up Critical information driving business goals, objectives, KPIs, and metrics Top down
- 13. Connect across all 3 business levels 16 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 “We don’t know where to start; we have so much data…" “We struggle with getting business ownership and interest” “We don’t have an approach for how we need to govern our data” “We don’t know how to measure what ‘good’ looks like…” “The business rejected other tools because they were too technical” Pain Points
- 14. The Precisely advantage Line of sight from important business initiatives to critical data assets Business value visibility Data assets in simple business language that is easy to understand Easy to understand More meaningful context about the data including origination, value, usage, and transformations Deeper understanding Single place to find data assets and be confident that they are accurate, consistent, and contextualized Democratized repository
- 15. Q&A
Editor's Notes
- #4: Your speakers today are Paul Rasmussen from Product Management who is responsible for the Data Observability module and Shalaish Koul from Sales Engineering. I will now turn this over to Paul to get us started
- #9: But data integrity is a journey. That’s one thing we hear loud and clear when we talk to our customers. Everyone is on a journey to continuously improve the integrity of their data, better understand their business, and ultimately better serve their customers. There are many different steps along the path to data integrity…. like integrating siloed data, measuring its quality, adding location intelligence, and enriching it with 3rd party data to name just a few. But we have learned from our customers is that there isn’t a standard, linear journey to data integrity that works for everyone… and that the days of large corporate initiatives are dead. Customers told us that their business and IT teams are working more closely together than ever… jointly identifying the specific scope that delivers meaningful business impact. And as a result, they tackle data integrity through distinct projects that give them business value… no matter where those steps fit into this journey… and then plan their next move. And not surprisingly, that mean they want solutions that give them the freedom to make those choices. Data integrity is a journey. It’s continuous. And it requires best-in-class solutions working together to deliver value to the business.
- #10: The seven modules of the Data Integrity Suite are built on proven Precisely technology. Not only do the Suite’s modules work seamlessly together, they also work alongside the portfolio of Precisely products, enabling you to easily adopt Suite capabilities for new use cases whenever you choose.
- #16: We think of our approach as “top down, bottom up, middle out.” This refers to connecting business objectives (at the top), to the data that supports them (at the bottom), and the processes that run the business (in the middle). This is based on proven practical experience with hundreds of customers across all industries. We do see that customers requires all of these capabilities to deliver meaningful results as quickly as possible. Top Down: This is where traditional data governance tools live driven by business goals, KPIs, regulatory and compliance Bottom Up: This is the domain of data catalogs and technical metadata management tools and addresses the technical users Middle Out: This is where data quality and data management tools excel. This part is often overlocked by governance and catalog tools. ---------------------------------------------------------------------------------------------------- We think of our approach as “top down, bottom up, middle out.” This refers to connecting business objectives (at the top), to the data that supports them (at the bottom), and the processes that run the business (in the middle). It’s based on proven practitioner expertise with hundreds of companies across all industries. Data Leadership requires all of these capabilities, along with the ability to start from where you are and deliver meaningful results as quickly as possible. Top Down: Critical information driving business goals, objectives, KPIs, regulatory and compliance This is key to getting business stakeholder adoption, or communicating data value to executive sponsors This is where traditional data governance tools live and are effective cause there is an urgent need or issue that has C-level visibility Middle out: Critical data driving business processes, operations, strategic sourcing, and R&D innovation This is where data quality and data management tools excel. This is often overlooked by governance and catalog tools. Bottoms up: Critical data assets that have analytical business impacts (data science, data engineering, analytics). This is the domain of data catalogs and technical metadata management tools and meets the needs to technical users