Artificial intelligence (AI) continues its rise to prominence within the business world. The number of companies using AI today and the range of problems AI is being applied to are both increasing steadily. However, there is one issue that is plaguing AI just as much as it has plagued analytics of all kinds over the years—data quality.

Data Quality Is A Familiar Nemesis

Organizations put tremendous resources behind ensuring the quality of their data. This is necessary due to the broad range of ways that data quality can be compromised. Users might input data incorrectly, a system setting might lead to an incorrect code being assigned to certain actions, or a typo might end up in a script developed to facilitate data transformation. These are among the many potential sources of poor data quality.

Data Quality Is A Familiar Nemesis

ity is that the data quality issue will never be « solved » no matter how much an organization budgets or how sincere its intentions may be. This is because the business environment—and the systems supporting it—is always in flux. New quality issues can arise at any time from any number of directions, including immediately after we certify that the data is pristine at a given point in time.

AI Doesn’t Escape Data Quality Issues

As organizations delve into AI, data quality will be as big of an issue as ever. This is because any AI processes that use traditional data sources will be just as dependent on those sources being of high quality as any other analytic processes. However, AI is also making use of a wide range of new data sources and data types. The methods of the past that are typically borrowed for a new data source fall apart when entering the realm of new data types used by AI.