Written by Andy Palmer
This week, Forrester Research, Inc. named Tamr a Strong Performer in “The Forrester Wave™: Data Quality Solutions, Q4 2015” report. The report evaluated 13 vendors based on 30 criteria, including current offering, strategy and market presence. In addition to receiving the highest scores in the validation, cleansing and standardization, as well as the cloud, criteria, Tamr scored a 4.00 out of a possible 5.00 in data governance and stewardship and in data link, match and survivorship. Tamr also received the highest score possible (5.00) for product strategy.
We believe that for a young company like Tamr to be named a Strong Performer in “The Forrester Wave™: Data Quality Solutions, Q4 2015” report, it takes unnatural amounts of work by a team that, like ours, is fully on the same page.
It also takes a problem worth solving with so much energy. That’s what the challenge of Data Quality in the Big Data Era has become. As Forrester’s research posited, “the data quality solutions market is growing because more enterprise architecture professionals see data quality as a way to address their top challenges.”
We’ve seen exactly this dynamic in our market among enterprises attempting to meet the rapidly elevating expectations for analytics. They’re actively looking for innovative data quality approaches that can dramatically accelerate data validation, cleaning and usage of all available internal and external data sources. Which makes total sense. With all of the data now available to enterprises, at some point you’ll want all it in a clear, clean, comprehensible format to feed your advanced analytics.
But there’s a catch. With so much diverse data and disparate sources, it’s nearly impossible to sort through the information from the top down with a specific goal in mind. We believe, instead, you need to start with the data as it is and work from the bottom up: integrate it, uncover new insights along the way, locate more data and sources that support or add to what you have discovered, make prepared data available to many analytics users (to optimize cumulative value) … and then repeat.
Tamr’s “virtuous cycle” approach, we believe, is exactly what Forrester — and our customers — pointed to in the report:
Tamr turns its back on convention and lets the data speak for itself, and customers give it high marks for this. Sophisticated machine learning algorithms go beyond pattern recognition and pattern matching seen in data quality to date. The tool analyzes data and data feeds and prepares the data to fit a defined data model for any domain … Tamr scales to ingest a large number of simultaneous feeds and matching (tens of thousands).
We believe that Tamr’s human-guided, machine learning approach is core to why Forrester found us to be a Strong Performer. The next generation of data quality systems will need machine learning-driven automation for scale. But they’ll also need human involvement for accuracy — a result of real users engaging with and improving the data. If users run into odd patterns or duplicates while analyzing data, the system must give them the ability to submit instant notice that gets fed back into the process. The virtuous cycle, in practice.
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