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, utilizing machine learning, can cut the time required for unification projects can cut down the time required by 90%, while scaling to more sources than any other approach.
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.
Join the Information Management institute and Tamr as we explore the practical advantages of using machine learning to achieve agile, scalable data mastering for the enterprise.
- You have dozens of operational data systems that are hard to master
- You are working to harmonize customer data at scale
- You are working to harmonize procurement or supply chain data at scale
- You're reconciling data stores post M&A
- You're working for an analytics center of excellence
- You're building next generation data engineering pipelines