Your messiest, most overlooked data could hold your most valuable insights. With LLMs and ETL, even dirty data is a goldmine.

This battery recycling company is now cleaning up AI data centers Redwood Materials plans to repurpose EV batteries to build renewable-powered microgrids.

Governance, standardized data goals and an AI-powered approach to smoothly extract, clean, format and unify data can help a company create data with integrity and deliver accurate results in real ...

The CLEAN framework is a structured, five-step methodology for data cleaning: Conceptualize, Locate, Evaluate, Augment, and Note, aimed at addressing data issues systematically and transparently.

This project demonstrates data cleaning on the Nashville Housing dataset (2013–2016) using R and packages like tidyverse, lubridate, and janitor. Key steps included standardizing column names, ...

This CRM hygiene checklist, developed by the data experts at ZoomInfo, offers a step-by-step guide to creating a scalable, clean CRM that’s primed for the next phase of AI-enabled growth.

It's time for spring cleaning, including your enterprise data stores, says data expert Joey D'Antoni, who offers front-line data-hygiene advice straight from the IT trenches.

Struggling with messy data? See why Power Query is faster and easier than Python for cleaning and transforming data. Python vs Power Query ...