During a time characterized by uncertainty and an unparalleled change, businesses and the global economy must demonstrate resilience to weather the storm. As a relatively new position in enterprises, Chief Data Officers (CDOs) have had to prove their resiliency every …
In 2020, it’s no longer a question of whether to become an analytics-driven organization, but how. Successful analytics-driven organizations will build information architectures that match workloads to answer the questions that create the most value for the business. The one-size-fits-all database is long dead, and so is the one-size-fits-all data/analytics platform. Download this ebook to learn more.
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.
As more organizations look to leverage data as an asset, the limitations of traditional MDM solutions have become a pressing challenge. Learn how Agile Data Mastering solves this challenge.
This report from O’Reilly covers: starting with the business question, understanding your data, selecting the data to use, and more.
Answering Business Questions with More Data in Less TimeAre your big data and analytics up to par? Nearly half of the global company executives in a recent Forbes Insight/Teradata survey certainly don’t think theirs are. This book examines how things…
Enterprise Data Unification Powered by Machine Learning Tamr’s solutions enable our customers to achieve transformational analytic and operational outcomes by taking a fundamentally different approach to the age-old challenge of data integration. We attack the enterprise data variety problem —…
Tamr was founded to tackle large-scale data management challenges in organizations where extreme data volume and variety require an approach different from legacy technologies.
As enterprises mature, they need approaches to data management that can solve critical issues around speed and scale in an increasingly larger and more complex data environment.
This paper defines the concept and process of data unification and compares different technical approaches to achieving the desired end-state of clean, accurate, consolidated data sets.