Building the Digital Thread Across the Enterprise

Building the Digital Thread Across the Enterprise Today’s Chief Data Officer must ensure that hundreds of data sources across the enterprise are clean, consistent and being used properly to power transformational analytics. Large enterprises such as GE, Toyota Motors Europe and Thomson Reuters have adopted Tamr to solve the challenge of fragmented and unstandardized data coming from out of many different systems. Tamr’s solution enables teams to build a central, dynamic glossary and map in hundreds of data sets. Having all data sets harmonized accelerates data integration and is tightly coupled by the underlying data. Rather than a series of custom one-offs, teams use Tamr to build a strategic foundation while ensuring proper governance. Download whitepaper here.

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Tamr Platform Overview

Quickly Uncover Insight From All Enterprise Data Data is the most important asset an organization has at their disposal. It can be used to help make tactical decisions as well as guide more strategic, transformational ones. Although the importance of data to an enterprise is abundantly clear, most don’t leverage more than 10 – 20% of what they have in the decision-making process. This isn’t as puzzling as it seems. Current technologies and approaches for managing data are extremely manual, costing firms a significant amount of time and money. This leads to partial insights that are generated very slowly, which is not ideal for any organization. Companies simply don’t have the ability to generate insights faster while using more of…

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Tamr on Google Cloud Platform

Business analysts and application developers are under increasing pressure to generate clean, comprehensive datasets for analysis to drive business value. Without high quality, unified data, downstream analytics are ineffective and, in most cases, time is of the essence when generating insight. Markets and opportunities move very quickly and businesses need the capability to respond even faster with insights that matter. A failure to do so results, at a minimum, in slower growth and foregone profit. Unfortunately, the current operating environment of most enterprises isn’t architected to meet these requirements for a variety of reasons, including: + Amount of manual effort required to integrate sources: Integrating disparate data sources into a unified, clean dataset requires significant manual labor. Programmers are needed…

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Tamr for Procurement Analytics Solution Overview

Rapidly Deploy Procurement Analytics To Gain A Complete View Into Your Spend And Significantly Reduce Organizational Risk & Cost The production of a high-quality product at an attractive profit margin is the lifeblood of all businesses. Unfortunately, every organization must contend with difficulties in execution that threaten this model. More often than not — particularly in industries manufacturing complex products — ensuring sufficient availability of cost-effective, high quality product components is a major issue that companies struggle to solve. While these vital responsibilities typically fall on sourcing and procurement teams within organizations, ramifications are felt company-wide and on the balance sheet. Two of the most frequent results that stem from not properly managing the production of goods are a substantial…

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Procurement Whitepaper

As we move into a 2016, it’s a safe prediction that buzzwords will continue to dominate the business world. “Strategic sourcing” is one buzzword that has permeated the procurement function for the past 20 years, but is often hard to describe. Almost every large procurement organization claims to be strategic, but what actually makes an organization strategic? This whitepaper identifies some of the information critical to acting strategically in a sourcing function. Strategic Sourcing as a Way of Operating A common distinction between tactical and strategic sourcing is that tactical sourcing reacts to short-term business needs, while strategic sourcing systematically aligns sourcing practices with long-term business objectives. Strategic sourcing relies on a holistic view of the business along with processes that promote…

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Media Analytics

Data analytics is key to the success of any industry — media, entertainment and sports included. Developing a single, unified view of any given product or person (whether a movie or show, actor or director, musician or tour, team or athlete) allows media, entertainment and sports companies to maximize revenue in a variety of ways. They would be able to, for example, compare performance of these entities against many variables, including region, genre, dates/windows, etc. They may also be able to leverage data to access rich intelligence about competitive products and gain deep insight into financial optimization, marketing strategy and consumer preference. It is particularly difficult for companies within media, entertainment and sports to harness the abundance of data within…

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Tamr in Depth: Data, The Neglected M&A Asset

In a Harvard Business Review article a few years back, Clayton Christensen and his co-authors wrote — with an almost audible sigh — that “[c]ompanies spend more than $2 trillion on acquisitions every year. Yet study after study puts the failure rate of mergers and acquisitions somewhere between 70% and 90%.” “The success or failure of an acquisition,” they surmised, “lies in the nuts and bolts of integration.” Typically, post-merger integration guidelines — like Bain’s “10 steps to successful M&A integration” — focus on challenges in melding companies’ distinct management teams, organizations, cultures and processes. All critical, to be sure. But would anyone be surprised if a healthy chunk of the M&A failure rate relates to the “nuts and bolts”…

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Tamr In Depth: Hadoop and Data Lakes

  Hadoop’s distributed approach has driven down the cost of storage and processing of massive amounts of heterogeneous data from diverse structured and unstructured sources. To deal with the staggering volume and variety of data in their organizations, enterprises turn to Hadoop to help form a ‘Data Lake,’ allowing these companies to store all of their raw data in one place for future analysis. While compelling, this approach comes with an obvious problem: you end up accumulating vast amounts data and promising yourself you’ll get back to it — eventually. Pretty soon you’re overwhelmed with dark, murky data that you’ll need to invest a whole lot of money in just to see (much less use). Getting data into the lake…

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