Data Governance, SAS vinkling Hvordan kan data governance se ut i praksis? Hvordan komme i gang, og sammenhengen med andre SAS-produkter. Ved Terje Vatle, Business Advisor Nordic CoE Information Management, SAS Institute
HVORDAN KAN DATA SE UT I PRAKSIS? Steps (yellow = demo): 1. Discover quality Data Stewards Manage & Monitor 2. 2 Define stnds & glossary 3. 3 Evaluate and monitor Business Users Create & Consume IT Implement, Adapt & Extend 4. 4 Correct data issues 5. Overview across business and technology
FELLES STARTPUNKT 1 2 5 3 4
STEP 1: DISCOVER QUALITY OF DATA 1 +
STEP 2: DEFINE ENTERPRISE WIDE DATA QUALITY STANDARDS & BUSINESS GLOSSARY 2 Hvilken definisjon er den riktige? Hvor skal jeg finne den?
STEP 2: DEFINE ENTERPRISE WIDE DATA QUALITY STANDARDS & BUSINESS GLOSSARY Data Domain 2 Define term templates as per business needs By data domains such as Customer, Product Business Function By business functions such as Risk, Marketing, HR
STEP 2: DEFINE ENTERPRISE WIDE DATA QUALITY STANDARDS & BUSINESS GLOSSARY Granular Access 2 Protect valuable business data definition Granular role-based user access Data Governance Workflow Workflow approval process
STEP 3: EVALUATE AND MONITOR 3
STEP 4: CORRECT DATA ISSUES 4
STEP 5: OVERVIEW ACROSS BUSINESS AND TECHNOLOGY Data Relationship Views 5 Consolidate enterprise metadata Import metadata from all systems Visualize relationships in many views
STEP 5: OVERVIEW ACROSS BUSINESS AND TECHNOLOGY (USE ANY METADATA) 5 Web Client SAS Lineage Web Application SAS Relationship Service SAS OMR DataFlux Data Management Server Other
HVORDAN KOMME I GANG? Initiate Develop Consolidate Top-down Bottom-up EDG Readiness & Maturity Assessment Business Drivers Core Team Mobilization Scope Prioritize DG Strategy Definition Vision, Mission Statement & Guiding Principles DQ Analysis Business terms and rules definition EDG Organizational Framework Data Stewardship Model DG / DQ Processes Technical Capabilities Gap Analysis Data Profiling Impact & root cause analysis Quick wins analysis DQ standards & KPIs definition Risk, Cost, Benefits Prioritization DG Roadmap Business Case Change Management Plan Stakeholder Engagement Plan
FOUR MISTAKES TO AVOID Failing to Define & Design Data Governance Prematurely Launching a Council Treating DG as a Project Relying on the Big Bang
SAMMENHENGEN MED ANDRE SAS PRODUKTER Data Quality Advanced Data Management Advanced MDM Advanced
HVORDAN VIL DATA SE UT FREMOVER? New sources New use cases New governance Operational data sources Unstructured data Linking to new data sources Privacy & regulations Retention policy Web & social media Data Quality Sensors, smart meters, Internet of Things
MERGING THE TRADITIONAL AND BIG DATA APPROACHES Traditional Approach Structured & Repeatable Analysis Big Data Approach Iterative & Exploratory Analysis Business users determine what question to ask IT delivers a platform to enable creative discovery IT structures the data to answer that question Monthly sales reports Profitability analysis Customer surveys Brand sentiment Product strategy Maximum asset utilization Business users explore what questions could be asked
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