Data Discovery Best Practices for Enterprises

According to research done by IDC, the global datasphere could see an increase in the amount of data of over 500%—from 33 zettabytes in 2018 to 175 zettabytes of data by 2025. It is safe to say that the amount of data organizations are collecting and processing is growing at an exponential rate, and with privacy regulations stronger than ever, organizations need to find a way to govern this data before it becomes overwhelming.

Data is the largest and most valuable asset an organization has in this day and age, and privacy regulations are in place to make sure that organizations are responsible custodians. Privacy laws such as the CCPA and GDPR give consumers the ability to exercise rights on their data and help enforce these rights. One of the most popular rights is the consumer’s ability to make a data subject access request. 

What is a Data Subject Request?

Global privacy regulations such as the CCPA, GDPR and LGPD define a number of consumer data rights that they can exercise. To exercise those rights, however, consumers must follow specific processes and procedures; there are specific steps through which requests can be submitted and action can be taken on them. This process is known as submitting a data subject access request (DSAR). There are a number of rights that the consumer can exercise through submitting a DSAR. These rights include:

  1. Right to access
  2. Right to opt-in
  3. Right to opt-out
  4. Right to erasure
  5. Right to notice
  6. Right to rectification
  7. Right to restrict processing
  8. Right to data portability

An organization is obligated to fulfill these requests within a certain timeframe and failure to do so could result in heavy fines and penalties for the organization.

An organization potentially has millions of data records pertaining to consumers, and fulfilling these requests requires they discover and classify all that data in a way that simplifies the process. There are some data discovery and classification best practices that can help your organization navigate this exponential data growth.

5 Best Practices for Data Discovery and Classification

Data will keep growing, and organizations need to work on their processes to keep up with it. These five best practices will help your organization navigate its collected data.

Automate Your Processes

The first issue that organizations face while trying to discover or classify data is their reliance on manual methods. Manual methods are at greater risk of being inaccurate and inconsistent. The potential for human error in manual processes for data discovery and classification is extremely high and manual methods are also time-consuming.

Moving to automated processes for discovery and classification can speed up your operations and enable quicker results with higher degrees of accuracy.

Plan Your Data Discovery Journey

Don’t start your journey to implementing data discovery and classification practices without a set end goal in mind. One great place to start is CRM software that usually houses customers’ personal and sensitive data.

Once you have determined the plan of action, you then need to specify your objectives and look for solutions that can help you reach those objectives. Say, for example, your organization needed to comply with the GDPR; you would need a solution with built-in content and workflows for compliance with the GDPR.

Look Beyond the Horizon

Sensitive data can be found anywhere, and this data needs to be discovered and classified, even if it’s somewhere other than in conventional data sources. Sensitive data can be found on-premises, in the cloud, in shadow IT systems and devices and in testing and development systems. Look for a solution that can support you in this data discovery journey and help you find data wherever it may be.

Rinse and Repeat

Data is constantly evolving and constantly changing, which means organizations need to have a strong handle on this data. Automation can help keep track of this changing data and makes the data discovery and classification process scalable and repeatable

Take Action

One of the most important parts of holding consumer data is protecting it. Organizations are required to have sufficient security controls on their consumers’ information to protect sensitive data. An automated system can help you assess risks in your system, alert you of any anomalies and implement effective policies.

Organizations are migrating their data, including collection, storage, analysis and processing, to the cloud to increase productivity while grappling with cyberattacks and an increasing number of data regulatory requirements. To accomplish this successfully, organizations need a comprehensive, automated solution to help with their data discovery and classification and to achieve proper governance on this growing valuable asset. 

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Anas Baig

With a passion for working on disruptive products, Anas Baig is currently a Product Lead at SECURITI.ai. He holds a Computer Science Degree and did his Bachelors in Science from Iqra University. His interest includes Information Security, Networking, Privacy, and Data Protection.

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