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How Consensus, a Target subsidiary, simplified data wrangling for machine learning
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Data-as-a-Service
- Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries
- Retail
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Fraud Detection
Services
- Data Science Services
- System Integration
The Challenge
Consensus Corporation, a subsidiary of Target, simplifies the complex process of selling connected devices. However, a major risk for retailers selling expensive devices and services is fraudulent customer activity. To address this risk, Consensus adopted fraud prevention as one of its core services. Through its automated machine learning-powered online engine, Consensus can alert its retailer clients to high-risk consumers before they purchase expensive devices. To identify potential fraud, Consensus built an advanced data model that leverages huge volumes of disparate data and undergoes routine updates. In order to be able to constantly refine its predictive models and alert their retailer clients faster to potential fraud, Consensus sought out technologies that would allow it to prepare this data faster for use in its machine learning models. The painstaking process of re-engineering SQL scripts took Consensus up to six weeks (on average) to update its fraud detection machine learning model. In addition, the data preparation process required sophisticated knowledge of data science techniques, leaving the company’s product and business intelligence teams unable to perform data preparation tasks on their own.
About The Customer
Consensus Corporation is a subsidiary of Target that simplifies the complex process of selling connected devices. It connects retailers with manufacturers and network operators using a single platform to enable what it calls multiplex selling: bundling technology and service purchases together, such as a smartphone and a data plan, or a smart television and a subscription to a video streaming service. Consensus enables retail stores nationwide to sell these types of connected devices and services through one unified online platform. However, a major risk for retailers selling expensive devices and services is fraudulent customer activity. To address this risk, Consensus adopted fraud prevention as one of its core services.
The Solution
Consensus identified the need for better data preparation and rapid model prototyping and evaluation that did not require advanced data science knowledge. At first, the company attempted to work with several third-party data preparation solutions, but found them either expensive, unwieldy, or complicated to deploy. Finally, Harrison Lynch, Senior Director of Product Development for Consensus, discovered the DataRobot automated machine learning platform and free Trifacta Wrangler solution. Trifacta’s user-friendly UI and DataRobot’s ability to rapidly build and deploy machine learning models helped differentiate both technologies from the competition. Consensus then selected DataRobot and Trifacta Wrangler Pro on AWS to get more power and more connectivity to wrangle even more data and quickly create machine learning models. Trifacta Wrangler Pro & DataRobot are AWS–deployed solutions that seamlessly access data stored on AWS, including Amazon S3 and Amazon Redshift. In addition, Trifacta Wrangler Pro leverages Amazon EMR (Elastic MapReduce) to process the data.
Operational Impact
Quantitative Benefit
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