Even exempt organizations need to be data mapping: Here's why

Data mapping is required for some organizations, but not all. As a result, some companies not subject to legal requirements may wonder: Does it make sense to invest time, energy and resources into data mapping, when all of that could be funneled into tasks that seem more pressing? 

Or, perhaps, tasks that result in a more public-facing payoff, such as launching a new or improved product — something investors may find more interesting — are more worthwhile?

But data mapping is a smart choice for any organization looking to maximize efficiency, reduce overhead costs, and ensure compliance. 

It is also a reliable method for creating and maintaining trust with consumers and investors who benefit from getting a visual picture of the data lifecycle of a particular organization.

What is data mapping?

Data mapping is the process of visualizing the lifecycle of data across an organization. It includes granular accounting of individual sources and suppliers of data, usage and use limitations, access and controls, and an accounting of all inter- and intraorganizational sharing and transfers. 

For interorganizational sharing and transfers, thorough data mapping documents all storage and filing mechanisms, details on company-wide, localized large language models used for business purposes, and apps — including those used on personal devices for work products. 

Intracompany data sharing and transfers must be documented, including disclosures of all service providers, data processors, and any other third-parties, especially those responsible for third-party sharing of data across geographic borders.

When is data mapping required?

Data mapping is a core component of compliance under multiple legal regimes. Article 30 of the EU General Data Protection Regulation explicitly requires businesses to maintain records of processing activities. Data mapping helps produce RoPAs. Additionally, a growing number of U.S. state laws, such as the California Consumer Privacy Act, and federal regulations, like the Health Insurance Portability and Accountability Act, require some form of data mapping or recordation process.

But data mapping is helpful for maintaining compliance with applicable laws outside of the requirement for formal documentation and record keeping. These include fulfilling data subject rights — a task made much faster when an organization can quickly find the lineage of the data at issue — and creating data privacy impact assessments or privacy impact assessments, which are constructed using the questions data mapping presents. A successful DPIA or PIA requires taking a granular accounting of how an organization is currently using data and how that same company may limit the collection or use without losing data utility. 

Though some organizations are not formally required to implement data mapping, the process remains beneficial and clarifying for any organization looking to maximize efficiency, streamline data flows, and — most importantly to C-suite executives and investors — expedite growth at scale. From this vantage point, data mapping becomes crucial for any organization that stores, shares and sells personal data and wants to do so safely, accurately and responsibly. 

Additionally, the data mapping process reveals how fully an organization would comply with current laws — for example, if it is considering expanding in the EU — and how prepared the company is for potential regulatory changes — in particular, new U.S. state-level legislation. 

Data mapping is a smart, proactive way to ensure organizations of any size and in every jurisdiction can stay ahead of competitors and adapt to any new rules and regulations with confidence and agility.  

Data mapping reduces overhead costs

Producing an effective data map can be both expensive and time consuming — which is the reason most organizations avoid the process, if they can. But targeted data mapping can save an organization money in three major ways.

Streamline data workflow. Good data mapping is a lot like cleaning out a hallway coat closet. Extraneous items are donated or disposed of, and more space is available for newer, higher-functioning outerwear. When organizations undertake data mapping, data collection and storage practices can be refined and updated to comply with current standards and regulations.  

This ensures organizations are primed to comply with data minimization requirements and can respond quickly and effectively to data subject requests for information or for deletion — in-part or in-whole. 

Mitigate current risk and prepare for future potential risk. This process is critical in helping compliance teams isolate current risk and identify future risk. Additionally, the visual nature of data mapping makes it easier to explain to C-suite executives where an organization may be exposed to security threats or legal liability under applicable regimes — and why it makes sense to initiate compliance measures to bridge the gap. 

Communication between compliance teams and C-suite executives is fundamental to robust AI governance and is best accomplished when compliance teams can match AI and privacy-related metrics to C-suite priorities. 

Evaluate human bottlenecks and improve overall workflow — upskill workers when appropriate. Data mapping may highlight areas within an organization where workflow could be streamlined with the addition of agentic AI or relatively inexpensive, traditional AI agents. 

Existing employees, already familiar with the organization and its mission, could then fill alternate roles in the company, thus adding to an organization's overall employee base without the cost of a new team of human employees. 

To automate, or not to automate?

Today, data mapping can be done through autonomous procedures. While autonomous processes can make data mapping faster and more cost-effective, they should not be solely relied upon by large organizations managing extensive datasets across multiple platforms or by those responsible for context-specific data.

Certain nuances are likely to be missed by today's fleet of autonomous data mapping vendors. Take caution whenever outsourcing work related to documenting the data lifecycle and always ensure a manual review when working with complex datasets, nuanced environments, or multitiered companies. 

While automated tools can be helpful in certain circumstances, others, such as nuanced contexts and companies processing large sets of complex data, should implement a manual review of any automated data mapping processes. 

Even when automated tools are not deployed, continue to implement regular, human-in-the-loop review mechanisms and procedures to check for accuracy and robustness, and avoid any errors or pitfalls. 

Maria Cannon, AIGP, is an associate at AMBART LAW PLLC. 

This article is only for informational purposes and does not contain legal advice.