Many companies struggle with data quality more than they admit and poor data can impact AI systems

When the algorithm feeds on training data to discover patterns, data quality challenges develop at the beginning of the process. For instance, when given access to unfiltered social media data, an AI system may detect abuse, racist statements, and misogynistic remarks, as demonstrated by Microsoft's AI bot. Recently, it was also thought that incomplete data was to blame for AI's failure to identify people with dark skin.

What Connection Does This Have to Data Quality?

Poor results are caused by a lack of data governance, a lack of knowledge of data quality, and isolated data views (where such a gender difference may have been seen).

Now What?

Businesses get terrified of recruiting when they understand they have an issue with data quality. To quickly identify, clean up data, and handle issues, consultants, engineers, and analysts are recruited on a whim. Unfortunately, months go by without any improvement and the issues persist despite spending millions on the employees. An immediate solution to a data quality issue is seldom ever useful.

Real change begins at the ground level.

Creating Awareness and Acknowledging Data Quality Issues

Start by developing a culture of data literacy to assess the quality of your data. Powerful industry voice Bill Schmarzo advises employing design thinking to foster a culture where everyone can grasp an organization's data objectives and issues and contribute to them.

Data and data quality are no longer only the responsibility of IT or data teams in today's business environment. Business users need to be aware of concerns like inconsistent and duplicate data as well as filthy data problems.

Making data quality training an organizational endeavor and equipping teams to identify bad data qualities are hence the first crucial steps to take.