Utilizing Intelligent Data Discovery
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Utilizing Intelligent Data Discovery

By CIOReview | Monday, May 2, 2022
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Data lies at the center of the vast collaborative network that drives commerce and monetization and communication and business growth.

FREMONT, CA: Data is the force that drives the modern firm in today's digital economy. The massive collaborative network that operates commerce and monetization, as well as communication and business growth, is powered by data. The amount of data has virtually surged as the globe has increasingly gone online—and a remote-first workforce has dramatically hastened that movement. Given the old, manual tactics that many businesses still use, governing, safeguarding, managing, and simply comprehending all of that data has become a monumental undertaking. Even the growing momentum behind a new regulatory landscape can't keep up with the massive amounts of data created, gathered, exchanged, processed, used, and forgotten every hour of every day. As the amount of data that enters the digital ether expands, so does the importance of managing it. The faster the data is managed, the more effectively we will be able to harness and effectively use it to develop stronger AI, drive more informed business intelligence, and accelerate digital transformation.

Businesses must understand how to get the right data to the right person at the right time for the right reason. To do so, they'll need a clear picture of where data is stored, as well as which data is valid and which isn't. A practical and continually updated inventory of your most important data assets can provide the necessary level of visibility. With sophisticated data discovery, more value can be extracted from data with deeper data intelligence. Businesses may use advanced discovery to search across all of their data, whether in the data center or the cloud, from structured, unstructured, or semi-structured sources, and whether it's in motion or at rest. Organizations can search not only by metadata but also by sensitivity and risk after discovering and classifying all of their data. They will be able to obtain an integrated perspective of data quality, which will lead to increased confidence and data trust. They can also implement and operationalize a data-up governance and control architecture.

To reduce risk, businesses must first identify it—for all types of data across the organization. They must classify data by kind and sensitivity to assess its risk and take appropriate action, such as remediation, deletion, encryption, or archiving. Companies must also consider who has access to which data. When it comes to sensitive or confidential data, companies must assess whether the persons who have access to it have the appropriate privileges—or whether their access violates data sovereignty or transfer prohibitions.