How is AI automating Data Preparation?

Automating repetitive data preparation tasks

What is Data Preparation

Let’s consider an example: You are analyzing customer behavior to build personalized recommendations. This requires ingesting data from multiple sources, including CRM systems, marketing campaigns, product usage logs, subscriptions, and entitlements. Before diving into the analysis, the data must be prepared — a process often referred to as data wrangling — which typically involves three key steps:

1. Data Cleaning: Dealing with missing, inconsistent, or incorrect data often consumes a significant portion of time. For instance, standardizing the email and timestamp format, removing duplicate CRM entries, etc.
2. Schema Mapping: Combining data from different sources frequently requires schema reconciliation. For instance, match customer_id between CRM and transactions; using email to map marketing data to CRM.
3. Transformation Needs: Raw data must often be reshaped, aggregated, or reformatted to meet analytical needs. For instance, creating a aggregate order_total by customer_id to calculate total spend; summarizing marketing data (e.g., clicks and conversions) by email.

The percentage of time spent on data preparation varies based on the complexity of the data, the tools available, and the organization’s data maturity.