Poor data quality is consequential now more than ever businesses access more and more data and manipulate it to their advantage. Users also have a larger list of options to choose from in case one house fails them.
Impacts of poor data quality
Organizations may not be able to avoid bad data altogether. The best to go about this is by halting the trustworthy data before it gets into the back-end system. This is called prevention, and it may mean having a team of IT professionals to assess raw before systems can base actions on it. Correction should come only when unreliable data isn’t detected at the point of entry. If you are quick about it, you can cleanse your systems without incurring any extensive losses, but still, it will be costlier doing it than preventing data flaws from finding their way into your systems in the first place.
Cleansing and duplicating data at the latter stages of the data management process can be ten times costlier than preventing it. Failing to do anything about the poor data quality in your systems, on the other hand, can set you back as much as 100 times the cost of preventing it at the point of entry.
Monitoring the data in your web, internal, and cloud storage systems in real-time are too much work to do. Humans will get tired of the monotony that comes with it. So, we need data integrated and scrutinized using proper AI and machine learning tools. Integration makes data easier to monitor, and with the help of whistle-blowers, you can be alerted about potential flaws in your data even without conducting manual checks.
Try an AI-augmented data quality platform in the market, that can helps detect and address data quality issues without the need for much human effort. The tool can be calibrated to scan all your business data sources and datasets at different stages of their movement through your systems. Since it is an AI-based platform, it will discover patterns and, if possible, tune itself to curb data quality issues of the type it has come across before.