In 2019, it’s no longer a question of whether to become an analytics-driven organization, but how.
Tamr’s Real World Evidence Integration Solution tackles the data challenge with a powerful data harmonization process that streamlines real world evidence integration for large volumes of disparate data to deliver transformational outcomes.
Download this white paper to learn how Tamr uses machine learning to help organizations build a data-driven approach to taxonomy design.
Tamr’s CDISC Conversion Solution tackles data challenges with a powerful data harmonization process, driven by human-guided machine learning that can replace traditional in-house tools prone to delays and errors.
What is data stewardship and what are the areas where machine learning can help data stewards? Download our guide to machine learning to find out.
In this chapter from the book Making Databases Work: The Pragmatic Wisdom of Michael Stonebraker, Ihab Ilyas discusses the academic project that eventually become Tamr.
In this chapter from the book ‘Making Databases Work’, Nik Bates-Haus discusses his experience recruiting Tamr’s core engineering team and building the first release of the company’s commercial product for data unification.
In this chapter from the book ‘Making Databases Work’, Andy Palmer discusses what it’s like to run a company with Michael Stonebraker.
The process of migrating your data to new software is a lot like moving into a new house: you want to consolidate and clean. That’s the role played by Tamr’s data unification and cleaning services during a migration.
Learn how enterprises have applied three generations of AI to address data unification challenges.
New compliance changes have brought about the need for effective solutions that enable smooth, ongoing operations and robust risk analytics for trade reconciliation.