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.
- 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
Grzegorz Przybycień - Senior Product Manager, Data Fabric, IBM
Grzegorz Przybycien is a Product Manager in IBM Data Governance & Privacy team. Grzegorz Przybycien has 20+ years of experience working in the Data & Governance space, previously working as product manager for SaaS products.