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IDC has been using the phrase “data intelligence software” to describe a category of capabilities that provide intelligence about data, and the term “data intelligence” has caught on in the industry. But not all definitions of data intelligence are equal. Let’s take a closer look at how IDC defines the term, and some permutations that have emerged.

Data governance has taken on a new urgency in the digital economy for two reasons: 1) Increasingly, business decisions are influenced or made outright by automated systems driven by analytics, algorithms, and artificial intelligence that require trustworthy, high-quality data for good results; 2) Regulatory scrutiny of data integrity and access controls is now more frequently required to meet security, privacy, and ethics requirements.

Data governance is no longer optional for enterprise organizations. Aside from complying with new regulations, such as the General Data Protection Regulation (GDPR), organizations are finally realizing the value of data as an asset that needs to be protected, managed and maintained to increase asset value. But just because businesses understand the value of data governance, doesn’t mean that enterprises are confident in their abilities to execute on it.

Data governance is no longer optional: regulations such as GDPR will start to be enforced; and organizations are finally realizing the value of data as an asset that needs to be protected, managed and maintained to increase asset value. Because data is a digital asset, and has mostly been managed within the realm of IT, organizations are quick to look at technology, expecting to find data governance software and solutions; but technology is only part of the solution.