Summary
Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.
Several factors determine the quality of your enterprise data like accuracy, completeness, consistency, to name a few. But there's another factor of data quality that doesn't get the recognition it deserves: your data architecture.
In this webinar Grzegorz Przybycien, Senior Product Manager, IBM Data Governance, will discuss essential elements of a modern data architecture approach to improve your data quality.
Key takeaways:
- Identifying critical data elements (CDEs) and their associated service level objectives to direct organizational efforts towards the right topics, such as data feeding regulatory reporting requirements.
- Augmented AI-based identification of Critical Data Elements (CDE) at scale
- Augmented AI-based generation of DQ rules leads to decrease of false positive issues
- Automated and simplified setting goals (SLAs) for data freshness, completeness, validity across CDEs
Please join us in this on-demand recording. Share your questions below and register to watch here.
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Grzegorz Przybycień
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