Data Visibility is Essential for Regulatory Compliance

By Nagamani On Jul 5, 2023

Digital transformation isn’t stopping anytime soon, which means that businesses are collecting and processing more and more data. With that comes a responsibility to protect this data. Companies can only protect data if they know what data they handle, and where this data is. Data visibility is, therefore, an essential requirement for regulatory compliance.

Keep reading to get insight into the rising number of data protection laws around the globe, why data visibility matters for data protection, what companies can do to improve data visibility, and why a data governance framework supports ongoing compliance.

Data Protection Law Is on The Rise

A flood of data protection laws has emerged in recent years, which reflects the growing recognition of privacy as a fundamental human right.

The General Data Protection Regulation (GDPR) formally came into effect in the European Union in May 2018 – which has game-changing implications for companies around the world that serve EU markets.

The California Consumer Privacy Act (CCPA), and the set of laws that followed it, the California Privacy Rights Act (CPRA), are other major examples. In addition, standards like the Payment Card Industry Data Security Standard (PCI DSS) have been updated to address contemporary data security threats – including data privacy.

This trend of new and updated data protection laws, especially the Federal Trade Commission or FTC Safeguards Rule 2023, is expected to accelerate with more state laws in the US, more regulatory and enforcement action, and a more active enforcement environment in the EU.

China and India have also joined the global privacy movement, with China’s new data protection law – the Personal Information Protection Law (PIPL) posing significant risks for noncompliance. Signs are, therefore, that in 2023 the volume of data protection law is only going to grow.

You Can’t Protect What You Don’t Know Exists

Data privacy laws are designed to safeguard data, particularly personal information – but it’s impossible to apply regulations when companies don’t know where data is.

However, many companies grapple with imperfect visibility into their data. Data is often dispersed across various systems, departments, and even geographical locations, making it difficult to have a comprehensive view. Unknown data can create security and compliance risks, including:

Uncontrolled data breaches: Without proper data visibility, companies won’t know when and if data is breached and can’t apply the necessary safeguards to protect sensitive data.
Compliance violations: Regulations like GDPR, CCPA, HIPAA, and many others require organizations to know where their data resides, who has access to it, and how it’s being used.
Difficulty in data management: Data visibility is vital for managing data life cycles, without it, organizations can’t effectively enforce data retention and deletion policies, leading to the unnecessary storage of data.
Insufficient auditing and reporting: Without visibility, organizations may struggle to demonstrate that they are managing and protecting their data in line with regulatory requirements.

The point is that compliance regimes are there to prevent customers in the case of a data breach – or indeed, to ensure breaches never happen in the first instance.

Automating Data Discovery for Compliance

The first step to automate the discovery process is through cataloging data. Companies can use data catalog tools that use machine learning to automatically locate, classify, and organize data spread across different systems and databases.

It makes it easy to understand what data is available, where it resides, and what the characteristics are of this data. As a next step, companies need to go through a data classification process, again relying on automated tools to identify and categorize data based on predefined classes. This helps in understanding what kind of data is held.

For example, automated tools can distinguish between personally identifiable information (PII) financial data, health data, and so forth. These different data types can then be matched to the compliance regimes that cover that data.

The process should also examine the data’s origin, usage, relationships, and quality. This can help in identifying where data comes from, how it’s processed, and where it’s used. Compliance often requires proof of data integrity and tracking the flow of data can provide this proof.

Using Governance to Consolidate Compliance Efforts

To ensure the data is reliable and accurate, automation in data quality management is needed. Automated tools can monitor, cleanse, and enhance data, thereby ensuring its quality. High data quality is essential in meeting compliance requirements as it helps in making accurate decisions and reduces the risk of non-compliance.

This process can be supported by a data governance framework. A data governance framework is a structure that assists an organization in assigning responsibilities, making decisions, and taking action on data covered by compliance law.

With a data governance framework, companies can develop a rigid practice of identifying important data across the organization, ensuring it is of high quality, and improving its value to the business. It also ensures that companies can consistently enforce their compliance obligations. There are three key components to a data governance framework:

Funding and management support: The framework must be backed by management as an official company policy, and there needs to be sufficient funding behind it – though, given the consequences of poor compliance, funding should be relatively easy to justify.
User engagement: Those who consume the data must understand and cooperate with data governance rules, which means that companies need to work hard to train their employees to adhere to the data governance framework.
Data governance council: Data and the compliance law that covers it constantly shifts, so it’s best to have a formal body in the company responsible for defining the data governance framework, adjusting it when needed, and verifying its implementation.

The right mix between awareness of compliance, data discovery for visibility, and data governance to ensure ongoing compliance will help organizations stay ahead of the continuously changing compliance landscape.