Traditional approaches to master data management have been around for decades. As the volume of data has grown, and potential value of analytics has exploded, enterprises seeking to compete on analytics are struggling to scale their mastering efforts to the surfeit of data sources available to analysts. Creating data engineering pipelines to unify this data at scale is more important — and harder — than ever. See how an agile approach to data mastering, fueled by machine learning, cuts the time required for unification projects by 90%, while scaling to more sources than any other approach. The video above is a 10 minute “best of” culled from a recent webinar presentation by Tamr’s Technical Sales Lead Mark Marinelli.
The benefits of data unification are well understood. Gartner estimates that by 2019, “organizations that provide agile, curated internal and external datasets for a range of content authors will realize twice the business benefits as those that do not.” Given the scale of enterprise data, automation is key to agility and scale. And automation can only be achieved with some human oversight to make sure the results are fast and accurate.
You can watch the full one hour recorded webinar with Information Management Institute and Tamr here.