What Is Data Discovery?

In the context of data risk management, data discovery technology is the fastest way to:

• Find, identify, and classify personal and sensitive data
• Determine its compliance with internal policies and external regulations (e.g., as data retention policies, legal holds, and data protection requirements), and
• Calculate data risks across complex, enterprise data landscapes

Data discovery software automatically connects to and continuously scans all data sources across an enterprise infrastructure, including (but not limited to) servers, databases, data warehouses, share files, cloud storage, and SaaS applications. Its intelligent sampling algorithm reduces the burden on IT infrastructure and accelerates the process, so users learn what data is stored where in minutes or hours–rather than days. Machine learning and content analysis allow the software to identify personally identifiable information (PII) and other sensitive data, so organizations have a complete, accurate, up-to-date data map at all times.

Why Is Data Discovery Important?

Organizations today, especially enterprise-scale ones, possess more data, of more types, about more subjects, coming from more sources, than ever before. They collect data about their customers, who they are, where they live, demographic information, financial information, their purchase history and patterns, and more. They produce data as part of their workflows–or producing and processing data may be the very nature of their business.

The data will likely be spread across multiple systems and locations. Data may reside on laptops, smartphones, large on-premises servers, and in a variety of cloud platforms, public, private, and hybrid. They may store years (or even decades) worth of internal communications on their email servers, but it’s likely that they continually add and remove additional communication and collaboration solutions, all of which store data.

This data is subject to a number of regulatory and governance requirements:

• Civil Litigation: E-Discovery requirements require organizations to identify, preserve, and collect all data relevant to a given legal matter.
• Privacy Regulations: Organizations must be able to find, produce, transfer, correct, and delete all data associated with an individual to comply.
• Data Breach Response: Cybersecurity regulations demand that organizations identify and notify both authorities and data subjects when PII has been compromised.

In addition, organizations can achieve significant business benefits by understanding what data they hold, where they hold it, why and how it was collected, and what retention or disposition obligations are associated with it.

• Actionable Insights: Business intelligence and analytics software can leverage data to facilitate understanding and decision-making.
• Proactive and Predictive Decision-Making: Identify trends and anomalies in data, allowing organizations to stay ahead of the curve.
• Process Optimization: Organizations that understand their data landscape are better positioned to automate and streamline workflows.

How Is Data Discovery Different from a Data Inventory?

While both data discovery and data inventory contribute to effective data management, they serve different purposes. Data inventories are traditionally created by reaching out across the entire organization and having key stakeholders identify what data they hold, where they hold it, and how it is used. But today, such a method of building a data inventory is fraught with risk. Traditional, manually compiled data inventories capture a snapshot in time--one that may be out of date by the time the project is completed. It may or may not be operationally linked to technology that automates and executes requests to search, preserve, delete, correct, analyze, or produce data. 

Data discovery software, on the other hand, is automated and continuously scans organizations’ data sources to maintain an accurate, up-to-the-minute picture of what data they hold in a constantly changing data landscape. Its AI and machine learning capabilities allow it to identify and classify known PII and learn new types of PII and sensitive data. Its results can be readily linked and fed into operational data retention, compliance, and legal hold technology.
 

If you're interested in learning how Exterro Data Discovery can help you lay the foundation for your successful data privacy compliance program, get a demo today!