Developments in AI are a high priority for businesses and governments globally. Yet, a fundamental aspect of AI remains neglected: poor data quality.
AI algorithms rely on reliable data to generate optimal results – if the data is biased, incomplete, insufficient, and inaccurate, it leads to devastating consequences.
AI systems that identify patient diseasesare an excellent example of how poor data quality can lead to adverse outcomes. When ingested with insufficient data, these systems produce false diagnoses and inaccurate predictions resulting in misdiagnoses and delayed treatments. For example, a study conducted at the University of Cambridge of over 400 tools used for diagnosing Covid-19 found reports generated by AI entirely unusable, caused by flawed datasets.
In other words, your AI initiatives will have devastating real-world consequences if your data isn’t good enough.
There is quite a debate on what ‘good enough’ data means. Some say good enough data doesn’t exist. Others say the need for good data causes analysis paralysis – while HBR outrightly states your machine learning tools are useless if your information is terrible.
At WinPure, we define good enough data as “complete, accurate, valid data that can be confidently used for business processes with acceptable risks, the level of which is subjected to individual objectives and circumstances of a business.’
Most companies struggle with data quality and governance more than they admit. Add to the tension; they are overwhelmed and under immense pressure to deploy AI initiatives to stay competitive. Sadly, this means problems like dirty data are not even part of boardroom discussions until it causes a project to fail.
Data quality issues arise at the start of the process when the algorithm feeds on training data to learn patterns. For example, if an AI algorithm is provided with unfiltered social media data, it picks up abuses, racist comments, and misogynist remarks, as seen with Microsoft’s AI bot. Recently, AI’s inability to detect dark-skinned persons was also believed as due to partial data.
How is this related to data quality?
The absence of data governance, the lack of data quality awareness, and isolated data views (where such a gender disparity may have been noticed) lead to poor outcomes.
When businesses realize they’ve got a data quality problem, they panic about hiring. Consultants, engineers, and analysts are blindly hired to diagnose, clean up data and resolve issues ASAP. Unfortunately, months pass before any progress is made, and despite spending millions on the workforce, the problems don’t seem to disappear. A knee-jerk approach to a data quality problem is hardly helpful.
Here are three crucial steps to take if you want your AI/ML project to move in the right direction.
