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Data Preparation Leads to Better Product Forecasting

Forecast modeling algorithms need more than clean, well structured data. Accurate forecast modeling depends on a wide variety of data formats and data sets whose quality may be questionable.

How can data scientists spend less time on the janitorial work—structuring, cleaning, and combining disparate data sets—and more time finding and tuning the best models?

This guide outlines how to use Trifacta Wrangler to prepare data for modeling in Amazon SageMaker.

Download this guide to:

  • Learn how to transform raw data into a refined asset that’s ready for modeling in four easy steps
  • See these four steps in action when a retailer implements a predictive supply chain model based on extreme weather conditions
  • Find out how data preparation leads to better product forecasting with Trifacta Wrangler and Amazon SageMaker

""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.""