What is Data Discovery?

Over the past two decades, data has become an incredibly valuable asset. For most companies, it’s the key to better understanding and engaging with customers, making informed business decisions, and improving products and services. But at a time when so much data is being created — as much as 3.5 quintillion bytes per day, according to some estimates — most companies need help successfully discovering and managing it. That’s particularly true for sensitive data that could expose them to risk.

Data discovery aims to help mitigate some of the risks associated with sensitive data use. In this article, we’ll explore data discovery and how it can enable holistic data security.

What Is Data Discovery?

Fundamentally, data discovery is the process of identifying and analyzing data from a variety of sources to uncover trends and patterns within that data, often with the help of visual tools. The goal is to help non-technical business leaders identify the insights hidden in the vast amounts of data they have access to so that they can make more informed decisions. In doing so, data discovery can help democratize data by ensuring that all users have access to insights without having to rely on IT to get them.

While data discovery can play an important role across the entire business, it’s a particularly important part of data security. In that capacity, data discovery refers to the practice of locating and identifying sensitive information in order to prevent unauthorized parties from accessing it. This can include personally identifiable information (PII) such as social security numbers, email addresses, passwords, names, addresses, and birth dates, to cite just a few examples. Put another way, data discovery within a security context is the process of identifying all of the sensitive data as it enters a data platform so that it can be tagged and classified for policy enforcement.


The Drivers of Data Discovery

Why is data discovery so important for data security? Quite simply, it allows organizations to know exactly where all of their sensitive data is located so that they can better manage it and minimize the chances of data breaches. This allows organizations to confidently use their data to personalize services and derive more accurate insights.

Data discovery is also key for achieving compliance with relevant data regulations, including the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Privacy Rights Act (CPRA). That, in turn, can play a big role in helping companies manage their reputations and build trust. Plus, data teams can use data discovery to consolidate where their sensitive data lives, thus reducing their footprint and storage spend.

It’s worth noting that data discovery isn’t a nice-to-have when it comes to identifying sensitive data. At a time when more data products are coming on the market and the number of data users and platforms is growing exponentially, it’s absolutely essential. This is even more apparent when you consider that as sensitive data is increasingly being used for analytics, it is also being carefully regulated by a growing array of rules and legislation, like those cited above.

Inside Data Discovery Tools

Data discovery has historically been a manual process, and one that could easily result in hundreds of hours of work each year for data engineers. With the rise of effective sensitive data discovery tools, however, data teams can now automatically identify all of the sensitive data across the organization. The best data discovery tools will automatically scan cloud data sources, detect sensitive data, and generate standard tagging across multiple compute platforms, thus eliminating manual, error-prone processes while providing universal cloud data access control and visibility into sensitive data.

Automated discovery typically involves five steps:

  1. Scanning. Data discovery tools scan for sensitive information that needs to be protected against unauthorized exposure. They allow you to automatically discover all of the sensitive data that may reside in millions of different fields across systems and departments. This saves teams from time-intensive manual effort that would otherwise be required to do so.
  2. Classify. Once your sensitive data is identified, it must be classified based on a variety of prebuilt or domain-specific custom classifiers that note data type, level of sensitivity, and more.
  3. Tag. Next, your sensitive data can be tagged within the platform so that it is recognized system-wide as sensitive, which simplifies proper policy enforcement. Leading tools allow different teams to inspect tags through workflows that certify that the data has been properly identified and tagged.
  4. Profile. After that, it’s important to assess your sensitive data footprint by profiling registered tags for elements such as PII, protected health information (PHI), or other sensitive data.
  5. Catalog Integration. Finally, your data discovery tool should author policies that reference existing metadata in Alation, Collibra, Snowflake, and other data catalogs, and it should do so without managing policy metadata in multiple places.

Ultimately, effective data discovery software that enables automated data discovery and classification gives enterprises clear visibility across their ecosystem as new data sources are added to different components. The software should also reduce costs and time-to-data, help minimize the risk of data breaches and leaks, and simplify compliance with relevant data compliance laws and regulations.

Leveraging Data Discovery to Reach Your Goals

Data discovery is important because it can significantly reduce the amount of manual work required of data teams, such as the inspection of sensitive information, which saves time and eliminates the potential for human error. It also allows organizations to more readily enforce data security policies and procedures once the information is identified.

For governance, risk, and compliance stakeholders, effective data discovery limits regulatory and legal exposure by allowing organizations to understand what data they possess, where it’s housed, and who has access to it. This makes it easier to protect that data while ensuring data practices and operations remain compliant with regulations. That, in turn, helps to avoid the costly reputational issues and monetary penalties that can result from data breaches or non-compliance. Finally, sensitive data discovery tools allow data teams to keep up with the rapidly changing world of data, giving real-time insights into data that is added or created within an organization’s systems.

Start Benefiting from Data Discovery

Data discovery is an essential component of data security. With the help of the right tools, organizations can automatically identify and tag all of their existing sensitive data, as well as new sensitive data as it enters their data platforms. Therefore, organizations can proactively ensure their data security and personalize their data services, all while maintaining regulatory compliance.

How does data discovery relate to data classification? Find out in our guide, What is Data Classification?