This statistic highlights that data quality isn’t just an issue for technical users and stakeholders,
but rather should be treated as an enterprise-wide concern because of its far-reaching business
implications. To be clear, data quality doesn’t need to be 100% perfect in order to move forward
and start getting value from advanced analytics and AI initiatives. This mindset can lead to years
and millions of dollars lost since data is only contextual to its use — so the onus is on organizations
to make data fit for purpose as they embark on each use case.