SAS: 5 Common data quality project mistakes (and how to resolve them)

Over the course of the last eight years, I’ve interviewed countless data quality leaders and learned so much about the common mistakes and failures they’ve witnessed in past projects.

In this post I wanted to highlight five of the common issues and give some practical ideas for resolving them:

\#1: Not connecting data priorities to business priorities
One of the biggest data quality frustrations I’ve witnessed in the business community is a lack of focus on tangible business issues. Data

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact [email protected] or view our subscription options here:

You are currently unable to copy this content. Please contact [email protected] to find out more.

To continue reading...

You need to sign in to use this feature. If you don’t have a RiskTech Forum account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: