Data Quality Tool for Data Warehousing

Building a data mart or warehouse is way more than just moving all your information into a single database. Your data is likely coming from different systems.
Here’s a silent SaaS explainer on how our Machine Learning saves time. Enjoy!

Data Warehousing and Its Major Data Quality Challenge

The problem is – in most cases there are inconsistencies in the way product codes, company names, or loss causes etc. are named across each of those source systems.

What happens when records that reference “General Liability” enter your database but can’t harmonize with entries that reference “GL”?

Most likely your data warehousing needs involve a data quality tool able to perform consistent entity resolution. Preferably something you can integrate into your data architecture without a huge headache.

RecordLinker can live right next to your existing architecture as a pluggable tool!

A data mart or warehouse should help you work more efficiently and draw deeper, more accurate insights. Messy, fragmented records undermine all of that.

The best way forward is to map all your reference data to a canonical record set. RecordLinker helps you do just that, using a system that’s quick, user-friendly, and doesn’t derail your data management workflow.

Once we’ve sorted out relationships between records, you can easily feed them into your existing ETL tool or data mart.

Discover Our Data Management Capabilities

Visit these pages to understand what we do, how we do it, and whether it matches your current needs.

Problems We Solve

What can be achieved thanks to RecordLinker, general use cases.

Feature List

Our full list of capabilities, quality-of-life and collaboration features.

Scroll to Top