Tamr Insights

What are key considerations for cloud migration financial organizations want answered. Tamr identifies three key areas finance want addressed.

How the U.S. Government can use machine learning and spend analytics to negotiate better contracts and save taxpayer money using spend optimization.

July saw a series of major announcements in public cloud migrations at financial institutions. Tamr looks at what's driving this migration to the cloud.

The importance of delivering successful digital projects - be it in retail banking or institutional - is only heightened by our changing behaviors forced by Covid.

A few Tableau extensions to help data analysts, data engineers and DataOps drive analytic value from data, especially during times of uncertainty due to COVID-19 coronavirus pandemic data can help

We have six proactive actions supply chain leaders can take to address the above problem areas both in the immediate as well as the long term to refortify their supply chains.

While we might know what our customers are buying and how they’re buying it, without connecting the dots to understand customers’ path to purchase, we can’t be sure how to engage them to purchase again.

As Chief Data Officer (CDO), it’s more important than ever to be an effective communicator with your C-level peers and provide information and context into what is happening to the business; at both the micro and macro levels.

Effective approach for data analysts in managed healthcare organizations and companies to master data from variety of data entities at scale.

The ultimate guide for Chief Data Officers (CDO) on building data-driven procurement organization, supporting accurate enterprise spend analysis projects

Financial organizations must make mastering customer reference data a priority in order to ensure the lines of defense for KYC and and Anti-Money Laundering (AML) can tackle dirty and duplicate data.

Manual metadata curation is inefficient and simply not sustainable for growing organizations using catalog software. Use Tamr metadata enrichment.

Explore how financial institutions battle money laundering with AML programs, despite the challenges they face due to manual processes and data quality issues.

With the intention of making data more FAIR (Findable, Accessible, Interoperable, Re-usable), organizations have invested a lot of time and money on software to identify and track datasets across existing data sources in the organization. Many software solutions promise a vision of systematically cataloging datasets in one place such that data consumers can always find and access the most trusted data available.

FracFocus is a free database that contains the hydraulic fracturing “recipes” for many of the wells, horizontals and verticals, drilled in the United States.  It comes in a .bak format, can be restored in MS SQL, and even has query instructions.

With multi-billion-dollar fines being imposed against institutions that have run afoul of KYC and AML regulations, it’s time for firms to aggressively adopt AI/ML solutions.

The FAIR data principles are finally emerging from the halls of academia to find their place in the commercial world. And not a moment too soon for life sciences businesses, as they begin to treat their R&D data as first-class assets after decades of proliferating and languishing data silos.

Data variety refers to discrepancies in the way data is collected across an organization–in different formats, across different business units, and with differing organizational structures.

Most legacy systems are not designed to integrate seamlessly with external sources, and traditional MDM systems are not scalable for the variety and volume of modern data. Tamr uses machine learning to improve your data quality and seamlessly connect with various internal and external sources, including search engines, to help you enrich and master your customer data.

If you are not on the IT side of the energy industry, you may not have heard of the concept of data mastering (often referred to as master data management, or MDM), but those of us who work in other departments have unknowingly been complaining about the lack of it for years.

One thing to consider when choosing a machine learning solution is to ensure that it will allow you to continue using your favorite reporting and visualization tools, now enhanced with mastered data and dozens of new data sources.