Andy Palmer

Professional Development is a priority at Tamr and key for all great early stage companies.

Instead of top-down MDM, organizations need to start with a machine learning-first data mastering approach, with a human in the loop to validate the results.

DevOps focuses on software development, quality assurance, and technology operations. DataOps emphasizes communication and collaboration between data professionals.

A modern DataOps infrastructure/ecosystem should be cloud first, highly automated, open/best of breed, loosely coupled and more for long term success.

Over the past two decades, there have been massive underlying technological and behavioral shifts in how companies manage and leverage their data as an asset.

“The source is not the master.” That statement hit me like a ton of bricks when I first heard it. It came from a conversation with Elena Alikhachkina, who at the time was leading a significant digital transformation initiative at…

Despite recent advancements in DataOps, the truth is we’re still early changing data is managed as an asset in companies. Read what is next for DataOps.

Microsoft customers can use Tamr to deliver mastered, analytics-ready data into Azure Synapse Analytics or leverage Synapse Spark for mastering data.

The U.S. Army is looking for new and better ways to harness data and use it as an asset to accomplish key strategic missions.

Analytic velocity: providing ever-faster time-to-answers to critical questions that help you achieve business goals. Here's how to accelerate it.

Data variety--the middle child of the three Vs of Big Data--is in big trouble. Learn how your enterprise can get started mastering data variety.

We’ve identified eight DataOps personas essential to delivering data as an enterprise asset — the key kinds of user roles powering modern enterprise data ecosystems.

The potential of DataOps infrastructure to close the gap between the value of an enterprise's siloed data and the consistent realization of that value.

These are the components of a DataOps ecosystem based on the environments we've seen people configuring over the past 8-10 years.

In 2019, it’s no longer a question of whether to become an analytics-driven organization, but how. Learn how to get the most out of analytics efforts.

We’re at a unique point in time when fundamental changes in data management create an opportunity for next generation data engineering pathways.

Organizations' big data management and engineering has become a competitive edge. Explore how DataOps teams are building next-gen data maintenance strategies.