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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Hi everyone,

You can watch the on-demand recording here and download the slides here.

Please share any of your questions below.

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.

------------------------------
Grzegorz Przybycień
------------------------------

Hi Grzegorz, nice to meet you.

I Enjoy the Webinar a lot, it is a very interesting functionality and I am sure that it will be very welcome by our clients. The Data Quality processes are the most used by our WKC clients.

My question is: when will we have this new SLA management functionality available in WKC SaaS?

. Thanks in advance.

Hi everyone,

You can watch the on-demand recording here and download the slides here.

Please share any of your questions below.

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.

------------------------------
Grzegorz Przybycień
------------------------------