Retail data powers Consensus. The company built a platform for retailers that simplifies the complex process of “activated selling,” or selling subscription-based products—for example, selling an iPhone with an AT&T data plan or a new television with a Netflix subscription. In either example, the Connected Commerce platform guides retail employees through the required steps of selling bundled products to consumers, offering up the right data about each product and its offerings for more effective selling.

One of the biggest challenges facing Consensus’ retail customers is the fraudulent activity that occurs at a higher frequency—and a higher cost—in subscription-based agreements involving expensive devices. For every one device lost to a fraudulent credit card, retailers must sell five more in order to recoup that loss. To mitigate the effects of fraud, Consensus Corporation built a fraud detection model as part of its Revenue Cloud on the Connected Commerce platform in order to alert retailers of potential fraud activity before it occurs. This fraud model requires huge volumes of disparate data and routine updates in order to ensure its accuracy. While trying to prepare its data for machine learning (a process called data wrangling), the company ran into severe delays.

Consensus sought out a data preparation solution that could efficiently join disparate data sources together, handle the messiness of its data with ease, reduce reliance on developers and data analysts, and scale data discovery. At the same time, Consensus had recently adopted AWS ML Competency Partner DataRobot’s machine learning automation platform to quickly build optimal models for fraud detection, so it needed a data preparation platform that could easily integrate with DataRobot’s platform.

After experimenting with the free desktop version of Wrangler, a product developed by AWS ML Competency Partner Trifacta, Consensus chose to implement and run the Trifacta Wrangler Pro solution on AWS. By using this solution, Consensus is able to wrangle large amounts of data much faster than before. It has decreased its data discovery time from two to three days to under one day and reduced time spent preparing data from eight hours to under one hour.


Trifacta, an AWS ML Competency Partner, makes data wrangling a faster and more intuitive process. Trifacta considers its product to be an intelligent tool that gets better with use. The company continues to improve its product based on the user experience.

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