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Machine learning outcomes are only as good as the data they are built upon, but preparing data for machine learning is a time-consuming process. The work of preparing data (data wrangling) for analytics can consume over 80% of the project effort.

Data wrangling solutions running on Amazon Web Services (AWS) can help streamline machine learning applications so that your teams can focus on the work that really matters: creating accurate predictions that improve your products, services, and your organization’s efficiency .

Listen to our webinar recording to hear how Consensus, a Target-owned subsidiary, utilizes AWS and Trifacta to prepare data for use in fraud detection algorithms. You’ll learn how self-service automated data wrangling can save your organization time and money, and tips for getting started with Trifacta’s solution, built for AWS.

Viewers will learn:

  • Why automating your data wrangling tasks can lead to greater data accuracy and more meaningful insights.
  • How you can reduce your data preparation time by 60% and more with self-service data wrangling tools built for AWS.
  • How easy it is to get started with machine learning solutions for data wrangling on the cloud.

Who Should Listen:

Analytics leaders (VPs/Directors/Heads of Analytics, Data Strategy, Customer Insights, Consumer Insights, Big Data, and/or Digital Strategy), Enterprise Information Managers or Directors, data scientists, data analysts, and all data-driven professionals are encouraged to attend this webinar.

AWS Speaker: Pratap Ramamurthy, Partner Solutions Architect

Trifacta Speaker: David McNamara, Customer Success Manager

Customer Speaker: Harrison Lynch, Sr. Director of Product Development, Consensus Corporation

""Trifacta brought an entirely new level of productivity to the way our analyst and IT teams explore diverse data and define analytic requirements. Our users can intuitively and collaboratively prepare the growing variety of data that makes up PepsiCo’s analytic initiatives.""

""We were actually able to shave the amount of time it took to do the analysis by [a factor of] six. Rather than having to do a tremendous amount of analysis, we’re actually readily able to start getting incremental data products out quickly.""