Written by Tamr
In a recent blog post published by Mike Stonebraker, our CTO & Co-Founder at Tamr, Inc, he sheds light on the analytical method that supports decision-making known as data mastering. This practice, often referred to as Master Data Management (MDM), turned 15 years old this year. For decades now, enterprises have been creating Independent Business Units (IBUs) that have significant freedom of action. Consequently, IBUs have manifested “silos” of information relevant to their specific roles.
Why do people want to integrate silos?
This phenomenon gave rise to MDM in order to boost agility across IBUs and generate business value through the integration of certain entities from the silos. In the information age, agility is increasingly becoming a key ingredient in the recipe for effective decision-making. Mike, the 2014 A.M. Turing Award winner, joined Data Science Central for a webinar sharing his view on the enormous business case for silo integration.
Silo integration becomes especially game-changing for cross-selling between business units, reducing regulatory risk, and acquiring the best price by integrating suppliers across IBUs. During the webinar, Mike uses Toyota Motor Europe and General Electric (GE) to illustrate the recent adoption of data mastering undertakings at scale.
Save millions with data mastering at scale
For example, Toyota Motor Europe is mastering data for Toyota customers all across the European continent. To grasp the gravity of this project, it is worth noting that this entails over 30 million raw records across 250 databases recorded in 40 different languages. This is well above and beyond the scope of traditional data warehouse initiatives.
In essence, the live audience learned that data mastering projects have skyrocketed in the past decade and a half. One of the most notable examples quoted by Stonebraker is GE, which has about 100 different business units from GE Appliances to GE Aviation. The multinational conglomerate, which is headquartered in Boston, estimated $100 million in annual savings from successful data mastering of their suppliers.
In additional to learning about why every enterprise is laden with data silos, Stonebraker explores:
- Why traditional master data management systems must evolve to meet the challenges of data mastering at scale
- How machine learning coupled with data unification harnesses innovation and provides a competitive advantage