3 Things CDOs Need to Know about Data Mastering at Scale

As a Chief Data Officer (CDO), you can recognize that while traditional Master Data Management (MDM) techniques might have worked well for the type and scale of data mastering challenges that existed 15 years ago when the solution came about, today’s data challenges require a different approach.  

Increasingly, utilizing these traditional approaches on the vast amounts andvariety of datathat enterprises are accumulating is slow, labor intensive, and extremely costly. As you work to help your enterprise fully leverage its data as an asset, here are three things to keep in mind aboutdata mastering at scale. 

The fact is, traditional approaches to data mastering produce traditional results—which is to say, limited. The velocity and variety of data has outgrown the old approaches we used, limiting a corporation’s ability to quickly and cost-effectively analyze data. Let’s explore this.

Exploring the limitations of traditional MDMMaster Data Management defines and manages an enterprise’s critical data to provide a single point of reference. This single truth allows you to accurately answer basic questions about any metric or KPI. This has been invaluable for companies seeking a single source of truth about an entity that other sources can reference further down the stream. There’s a lineage to the records and flexibility in how the records are created. Absent of errors, there’s no duplicates or unmatched data—creating accurate views of each entity.

The problem is that MDM requires a human-intensive process to deliver rules-based truths. This is a complex way of saying that it’s not scalable, and moreover, its dependent on constant manual reviews of exceptions. This means that there will be a large portion (and ever growing portion) of data that remains unmastered, and the only solution will be for enterprises to pay a premium in resources.

Extract, Transform & Load (ETL)creates a global schema up front.