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Data quality and accuracy is an objective not only for businesses leading the way in machine learning and AI, but for anyone who deals with customer information or any kind of data.

Studies show that poor data quality can cost companies upwards of 20% of their revenue according to Gartner.

Data must be reliable and accurate, a responsibility that often falls on the IT department. However, most IT teams are not involved in the analyzing of the data, that is reserved for the business units.

In order for IT teams to keep their organizations organized, optimized, and efficient, data reliability needs to be an ongoing cadence and not something done all at once.

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