The Data Roundtable
A community of data management experts
.@philsimon on the collision between the two.
Master data management (MDM) provides methods for unifying data about important entities (such as “customer” or “product”) that are managed within independent systems. In most cases, there is some kind of customer data integration requirement for downstream reporting, and analysis for some specific business objective – such as customer profiling for
I've spent much of my career managing the quality of data after it was moved from its sources to a central location, such as an enterprise data warehouse. Nowadays not only do we have a lot more data – but a lot of it is in motion. One of the
.@philsimon asks some fundamental questions about taking the next step with #bigdata.
In my previous post I discussed the practice of putting data quality processes as close to data sources as possible. Historically this meant data quality happened during data integration in preparation for loading quality data into an enterprise data warehouse (EDW) or a master data management (MDM) hub. Nowadays, however, there’s a lot of
We had just completed a four-week data quality assessment of an inside plant installation. It wasn't looking good. There were huge gaps in the data, particularly when we cross-referenced systems together. In theory, each system was meant to hold identical information of the plant equipment. But when we consolidated the