Master data management (MDM) software turned 15 years old this year.
Originally launched in 2004 by SAP, master data management systems aimed to help resolve the data unification problem by creating a central source of standardized references to customers, products, employees, suppliers, physical assets and other data across their many IT systems.
MDM is valuable, but it’s also slow, labor-intensive, and costly. As the scale of MDM projects increases to millions of entities and hundreds or thousands of data sources, the traditional methods often fail.
Mike Stonebraker will share his view on how MDM technology and MDM organizations must change to fulfill the promise of MDM at scale. In this latest Data Science Central webinar, we will review:
Why large enterprises need data management solutions that solve data mastering challenges at scale
Why traditional, rule-based, data mastering options are struggling to keep up
How Machine Learning can be used to address large-scale data mastering challenges
Mike Stonebraker, CTO & Co-Founder - Tamr, Inc.
Stephanie Glen, Editorial Director - Data Science Central