Data Cleansing Made Simple (Webinar Recap)

Dirty data is unfortunately, a reality that anyone managing customer data has to face at some point. At Hull, we come across dozens of different scenarios where data has to be cleansed, like duplicate records, field values that aren't standardized, incomplete records, and typos and misspellings.

Fortunately, cleaning data doesn't have to be complicated with the right tools on hand.

Before we dive into what those tools are, cleansing data is often performed via two methods:

  1. Programmatically, with code-based, automated rules that run on a regular basis
  2. Manually, with tools built directly into a software application's UI

While programmatic (automated) data cleansing can catch and clean 90% of bad data, there are always edge cases and scenarios that need to be reviewed by humans before the data is changed or deleted. In these scenarios, manual data cleansing is necessary!

Hull Demos

In the webinar, Hull Head of Product, Tim Liu, shares demos of data cleansing for three familiar scenarios:

  1. Standardizing field values
  2. Backfilling tools with historical data
  3. Identifying duplicate records for further review

Your Data, Your Way

Fine-tune your customer data management skills by catching all of the replays from the "Your Data, Your Way" webinar series.

Watch the replays
Angela Sun

Angela brings over a decade of B2B technology marketing experience to her role as the Director of Marketing at Hull. Prior to Hull, she spent 5 years at utility data aggregator, Urjanet, where she held various roles in demand generation, marketing operations, and product marketing.