Good data quality enables organizations to confidently make decisions and to clearly understand why they are using their data and what that data is used for! In other words, it creates a common language.
Not surprisingly, bad data quality does the opposite (obviously).
Organizations plagued by bad data quality cannot know whether the data that they are using as a basis for their major decisions is the right data. This can and does lead to mistakes, legal liability, and wasted time and energy. To make things worse, they often know this, but are at a loss of what to actually do about it.
OK, now if you don't believe us yet, here is some data...
According to the most recent study in June 2022, by Great Expectations, a leading open-source data quality management platform, over 75% of the 500 data practitioners (engineers, analysts, and scientists) that they surveyed believed that they had serious data quality issues, and only about half believed that management was confident in their organization’s data quality (in contrast to the 11% who believed that they had no issues).
The upshot is that many senior managers KNOW that they are not relying on good data!
Now, what happens when a senior manager (knowingly or by avoidable mistake) relies on bad safety data, misleads investors in its 10-K (whoops), or provides the wrong data to a regulator or to opposing counsel? Or, on a day-to-day basis, what happens when management knows that the data that it is using creates wrong or unrealistic internal forecasts or KPIs.
Hint: not good things.
The good news is that there are certain information governance strategies that companies can take to improve their data quality. These include…
😀 creating a data governance framework that defines data quality standards, assigns data ownership and responsibility, and establishes procedures for data quality monitoring and reporting
😀regularly monitoring data metrics through, for example, data quality dashboards or scorecards that monitor and report on data completeness, accuracy, and consistency
😀using data profiling tools to identify data quality issues such as missing or inconsistent data
😀deploying master data management tools that create a single view of customer or stakeholder data
😀regularly cleansing data to identify and remove duplicate customer records, or data enrichment tools to add missing demographic data to organizational records
And, of course, backing up these processes with policies and procedures, training, and regular audit.