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DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2025.data
 versity.net/sessionPop.cfm?confid=164&proposalid=16070\nAlmost three years 
 ago the Data Governance Journey was launched by the Data Governance and Inf
 ormation Architecture function at Regeneron Pharmaceuticals, Inc. The main 
 objective was to set the foundation for the development of a data-driven or
 ganization, including the establishment of data as a corporate asset, makin
 g the data Findable, Accessible, Interoperable, and Reusable. The journey b
 egan with addressing the most common and high-priority pain point: What dat
 a do we have, where is it, and how do I get access.\n\nThe deployment of a 
 Data Catalog addressed this pain point and more. The business started to ap
 preciate the need for roles and responsibilities (Data Owner, Data Stewards
 ) and processes that ensure the accuracy of the information by way of witne
 ssing tangible insights. A Data Literacy program was launched to support th
 e creation of a data-informed culture that promotes collaboration through a
  shared language and treats data as an enterprise asset. The program aimed 
 at enabling everyone with the role-relevant tools and information to succes
 sfully communicate with data, engage the community in sharing their knowled
 ge, solve challenges, break down silos, and provide development opportuniti
 es to become data literate.\n\nThe journey continued with a Data Quality Go
 vernance initiative, the primary topic of this session. We will delve into 
 the following topics.\n\n
 Comprehensive Framework: Learn about our four-phase Data Quality Governance
  (DQG) process—Define, Analyze, Fix, and Sustain—that systemati
 cally addresses data quality issues.\n
 Roles and Responsibilities: Understand the critical roles such as Data Stew
 ards, Data Owners, and Data Users, and their responsibilities in ensuring d
 ata quality.\n
 Implementation Methods: Discover various methods and tools, including inter
 nal workshops, business data steward dashboards, and root cause analysis, t
 o enhance data quality.\n
 Real-World Use Cases: Explore impactful business use cases, highlighting th
 e consequences of poor data quality on operations and compliance. \n
 Continuous Improvement: Learn about strategies for continuous data quality 
 improvement, including monitoring metrics, performing root cause analysis, 
 and engaging in data champion communities.\n
 Engagement and Support: Discuss the role of enterprise DQG shared services 
 in support of data quality initiatives.\n\nThis session will provide the au
 dience with perspectives and tips on developing a Data Quality Governance s
 trategy, supported by a journey that leads to the building blocks of Data G
 overnance. The deep dive into one of those blocks, Data Quality Governance,
  will provide a comprehensive understanding of the Data Quality Governance 
 (DQG) framework adopted and the resulting use case.
DTSTART:20250506T101500
SUMMARY:Journey to Data Governance via Data Catalog, Data Literacy, and Dat
 a Quality Governance
DTEND:20250506T105959
LOCATION: See Description
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