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DESCRIPTION:Click for Latest Location Information: http://dgiq2024west.data
 versity.net/sessionPop.cfm?confid=160&proposalid=15371\nWhat’s the qu
 ality of your data? Do you really know? Can you prove it? Th
 ese are easy questions to ask but expensive to answer. Quality is ofte
 n assumed based on end-user feedback. If nobody’s complaining, t
 he quality must be good, right? The need for high-quality trusted data
  continues to grow with increased scrutiny around AI and Analytical project
 s, consumer privacy regulations, regulator attestation requirements, and mo
 re. Purchasing a tool is often seen as the solution, yet they are regu
 larly deployed with little adoption and with marginal, if any impact on the
  Data Community.\n\nA successfully deployed and adopted Data Quality progra
 m starts with understanding and addressing the key barriers to adoption fro
 m technology, business, and organizational lenses.\n\nHow did we achieve th
 is?\n\n
 Thinking of Data Quality as an integrated end-to-end self-service program c
 overing (1) Data Quality Standards, guidance, and expectations, (2) DQ Tool
 ing, (3) Data Catalogs, (4) Governance Platforms, (5) Defect Management, (6
 ) Real-time Transparent Communication, (7) Technology AND Business Con
 sumer Dynamics, and (8) Existing Daily Routines and Time Constraints\n
 Simplification, automation, and re-use of capabilities (removing any non-ef
 fective activities)\n
 Partnering with Executive Leadership, Data Producers, and Data Consumers on
  solutions and capabilities that save time and add value to their daily rou
 tines\n
 Establishing shared KPIs that allowed Data Producers and Consumers to share
  in the success of the program while providing accountability and ownership
 \n
 Establishing KPIs that measure the value of your program to Executive Leade
 rship for continued support\n\n
DTSTART:20240604T111500
SUMMARY:Deploying a Data Quality Program That Doesn't Fail
DTEND:20240604T115959
LOCATION: See Description
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