Tamr Insights

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.

Is it taking too long to onboard new customers due to risk, compliance and due- diligence screening? Are your sales reps not getting proper commissions for their sales?

Every manufacturer I have engaged with in the past 10+ years claims to be data-driven and almost all of them are investing in “Digital Transformation and Industry 4.0” initiatives. These initiatives have business-critical goals like better asset utilization, inventory reduction, improving quality and throughput or optimizing their supply chain.

In the age of open-source software, it’s tempting to solve your data problems by building your own tools. Here at Tamr our biggest competition is not other software companies but telecoms, multinationals and oil and gas giants who are building their software in-house. In this blog post, I’ll cover five reasons why buying software is a better strategy for your business.

While the people, processes and tools involved in data pipelining obviously differ, the building method and life-changing results are similar.

When someone thinks about using machine learning in any industry, forecasting and predictive analytics immediately come to mind. I have no doubt that many readers of this post already use some type of machine learning through their analytics teams or by consuming reports created using machine learning techniques.

It’s common to think of data mastering as the way we turn our disparate and dirty data sources into something clean that we can use to power data applications.

DataOps improves collaboration and iteration by bringing together disparate teams, business units and tools. Watch this video to learn more.

What do CDOs need to know when it comes to data mastering at scale? We've got three things to help you get started -- read them here.

How one organization transformed its R&D organization through data unification and data management, improving decision-making about new medicines.

Creating a data-driven culture is a crucial initiative for enterprises. Here are three steps that can help your organization get started.

How many organizations have implemented DataOps, and how successful have they been? Read the results of a survey on Current Trends in DataOps

What is data variety -- and why does it pose a unique challenge for the enterprise compared to volume and velocity of data?

Tamr is evolving our data unification platform to natively understand geospatial mapping data to help enterprises achieve analytical breakthroughs.

What if every oil and gas company could unify and track a wide array of machinery, vendors, and personnel to improve wells mastering?

Know your customers: In banking, this requires embracing the potential of big data analytics. Read how one multinational bank accomplished this here.

Real world data can be used as real world evidence (RWE) in various ways to improve patient outcomes and accelerate the development of medical products.

Learn how a probabilistic, machine learning approach to data unification helped a large, multinational bank unify data hundreds of data silos.