Data preparation is critical for many industries, retail is one of them. Communicating specialized requirements and delegating tasks to data engineers can lead to unexpected outcomes and delays. Often the better approach for retailers is to explore, cleanse, and manipulate data on their own.

From optimizing prices and tracking inventory to forecasting demands, many areas of the retail industry depend on clean and standardized datasets to generate results. Domain experts in these areas know best how they want to fine-tune their data but might not always have the right programming skills for the job. The ideal option for them is to use a no-code solution with a visual, interactive means of working with data.

In this document, we will delve into the process that a category manager from a retail company follows in Astera Dataprep, a no-code data preparation tool, to prepare raw orders data for analyzing the performance of a new category.

We’ll explore how a retailer prepares sales data by:

• Addressing missing values
• Fixing inconsistencies
• Enriching with additional information
• Removing unwanted information