Data Mapping 101: A How-to Guide

The more you foray into data privacy compliance, the clearer it becomes that data mapping is an absolute necessity. But how do you “do” data mapping? What is a data map? What goes into a data mapping exercise?

In this blog, we’ll clear up some of the confusion surrounding data mapping for data privacy purposes, answering questions like what it is, why it matters, best practices, and various data mapping techniques. 

What Is Data Mapping? 

As it turns out, data mapping can mean several different things, depending on the context. 

In this article, we’ll focus on the term as it relates to data privacy, not the technical process of mapping fields between databases—that’s a different thing altogether.

In data management, data mapping typically involves aligning data fields between different systems or databases to ensure consistency and accuracy.

In data privacy, however, data mapping is creating a detailed map that visualizes how personal information (PI) is stored, processed, and transferred across your organization. This map is essential for understanding and managing the data flows within your systems.

When data mapping, you’ll produce a data inventory and an actual data map.

Data Inventory

This is a comprehensive list of all the personal information your organization collects, stores, and processes. It includes details such as data types, sources, storage locations, and processing purposes, all of which are vital for effective data integration. 

The data inventory is the backbone of your data mapping efforts, providing the raw information needed to manage PI effectively.

Data Map

Building on the data inventory, the data map visually represents how personal information flows through your organization’s systems. It makes tracking and managing data easier as it moves between departments, across borders, or to third-party vendors.

By creating and maintaining both a data inventory and a data map, your organization can gain a clear, comprehensive view of its data landscape.

The Role of Data Mapping in Data Privacy Compliance

Data mapping is crucial in ensuring compliance with data privacy regulations, particularly GDPR. Even though data privacy regulations don’t explicitly require a data map, a data map serves as an indispensable foundation for your compliance program. Upon it, you build everything else. 

Under GDPR, Article 30 requires organizations to maintain detailed Records of Processing Activities (RoPAs). 

These records must document everything from the types of personal data being processed to the purposes of processing and the parties involved. Data mapping is instrumental in creating and maintaining these RoPAs. 

Data mapping visually represents how personal data flows through your organization, making tracking and documenting all processing activities easier. This ensures your organization meets GDPR's stringent requirements.

But, GDPR isn't the only regulation that benefits from data mapping.

In the U.S., laws like CCPA and HIPAA also impose strict data management obligations. For instance, CCPA requires businesses to disclose how they collect and use personal data, while HIPAA protects health information.

Internationally, other privacy laws impose similar requirements. 

Data mapping helps organizations comply with these diverse regulations by providing a clear, comprehensive view of data flows, making it easier to meet legal obligations across different jurisdictions.

Benefits of Data Mapping

Companies use data mapping to: 

• Make it easier to respond to but also to respond to data subject access requests (DSARs). 
• Inform data protection impact assessments (DPIAs). 
• Create records of processing activities. 
• Minimize the collection, storage, and use of PI. 
• Monitor vendors more effectively 
• Cut down on non-compliant cross-border data transfers 
• And more. 

Let us take a closer look at how these data sets benefit your organization.

Faster DSAR Response 

DSARs require careful attention to ensure that data from one source is accurately represented in your response. Most laws require you to respond to DSARs within 30 or 45 days, depending on the data model your organization uses. Meeting those deadlines, especially as your DSARs begin to scale, can be costly in terms of budget and the opportunity cost of your team’s time.  

According to research by the International Association of Privacy Professionals (IAPP), 47% of respondents said that when it came to fulfilling a data subject request, finding a person’s data within their organization was really difficult. 

Unsurprisingly, it’s a lot harder to find a data subject’s PI if you don’t know where your organization stores personal data. 

Having a data map in place means you can respond to DSARs faster and with more confidence that you’ve actually fulfilled the data subject’s request. 

Better Informed DPIAs

DPIA requirements are generally open-ended and vary slightly from law to law, but the GDPR lays out the following requirements when conducting a DPIA: 

• A description of the envisaged processing operations and the purposes of the data processing. 
• An assessment of the necessity and proportionality of the processing. 
• An assessment of the risks to the rights and freedoms of data subjects. 
• The measures taken to address the risks and demonstrate compliance with the GDPR. 

Indirectly, a data map supports many of these requirements. For one, you can’t effectively assess risk if you don’t know what will happen to PI once your organization processes it. 

You may also already collect and/or process the required data elsewhere in your organization, in which case the correct action wouldn’t necessarily be to re-collect or re-process it. 

Easier RoPA Creation and Maintenance

Consider the process of creating a RoPA. 

The GDPR requires both processors and controllers to create and maintain a RoPA If the GDPR covers your company, you must document:  

• Why you're processing personal data. 
• What measures you take to protect the data. 
• The recipients to whom you’ll send data. 
• Transfers of data to third countries. 
• The length of time before you delete the data. 
• And more. 

If you don’t know where you’re collecting, storing, sending, and processing data, you cannot meet this legal requirement. What’s more, you’ll need to maintain and update your data map in order to maintain and update your RoPA. 

Stronger Data Minimization and Purpose Limitation Practices

The less data your company holds on customers, the less it has to protect, and the less liability you will have should there be a privacy incident. 

Data mapping gives you the big picture of data collection and processing at your organization, enabling you to reduce redundant, irrelevant, unnecessary, and out-of-date data through effective data integration.  

Better Insight Into Vendor Risk

Depending on a company’s size, dozens or hundreds of vendors could be processing your consumers’ personal data. Ultimately, it’s your responsibility to vet how vendors treat (and pass on) the data you've collected to protect your consumers.  

Privacy professionals are well aware of the risks that third parties pose when it comes to data privacy compliance—that’s why vendor risk assessments exist.  

One of the major challenges with vendor monitoring, however, is knowing all the vendors in use at your organization. 

Today, it’s relatively easy for one department to begin a relationship with a third party that involves the transfer of PI. There might not even be money exchanged; the third party could provide its services as a loss leader or explicitly for consumer PI. 

Data mapping lets you discover where data flows to different vendors and what data is being transferred. That means you can prioritize vendor risk assessments based on the nature of the transfers, the sensitivity and volume of the transferred data, and the privacy reputation of the vendors. 

Stronger Cross-Border Data Flow Compliance

If data crosses borders, it's essential to know where it's going, what laws are at play in both the sending and receiving jurisdiction, and what mechanisms you use to ensure the transfer remains compliant. 

For example, the GDPR only permits the transfer of EU residents’ data to a country outside the EU under certain circumstances. This includes an adequacy decision (i.e., EU authorities have decided that the receiving country has adequate protections in place), standard contractual clauses, binding corporate rules, and a few other niche mechanisms. 

For transfers between the EU and U.S., the Data Privacy Framework earned a recent adequacy decision (though it remains on shaky legal ground).  

Without a comprehensive data map, your organization could easily unwittingly transfer data to other jurisdictions. Vendors may operate in other countries, or you may accidentally transfer data that should stay in one jurisdiction to an office in another. 

Challenges in Data Mapping

While data mapping is essential for ensuring data privacy and regulatory compliance, it comes with its own set of challenges, particularly in large organizations with complex data ecosystems.

Identifying All Data Sources

One of the primary challenges in data mapping is identifying every source of data within an organization. 

Data is often dispersed across multiple systems, departments, and even geographies in large enterprises. Shadow IT, where departments independently use unsanctioned software, can further complicate the identification process. 

Without a comprehensive view of all data sources, your data map may be incomplete, which could lead to potential compliance risks. 

To overcome this, organizations need robust discovery tools and cross-departmental cooperation to ensure all data sources are accounted for.

Determining Data Sensitivity

Another significant challenge is accurately determining the sensitivity of different data types. 

Not all data is created equal—some may be personal and subject to strict regulations, while other data might be less sensitive. 

Misclassifying data can lead to either over-protection, which is resource-intensive, or under-protection, which can result in compliance violations. 

Organizations need to establish clear criteria for data classification and ensure that these criteria are consistently applied across the board.

Ensuring Data Security During Mapping

The data mapping process itself involves handling sensitive information, making data security a top priority. If the mapping process is not adequately secured, it could expose the organization to data breaches. 

This is particularly important as data maps often contain detailed information about data flows and storage, which could be valuable to malicious actors. 

To mitigate this risk, organizations should enforce strict access controls, encrypt data during the mapping process, and ensure that all data mapping tools meet high security standards.

Cross-Departmental Collaboration

Effective data mapping requires input from multiple departments, including IT, legal, HR, and marketing. However, achieving consistent communication and collaboration across these departments can take time and effort. 

Different departments may have varying levels of awareness or understanding of data privacy requirements, leading to inconsistencies in the data map. 

To address this, organizations should foster a culture of collaboration, provide training on the importance of data mapping, and designate a project manager or privacy professional to coordinate efforts and ensure consistency.

Advanced Strategies for Effective Data Mapping

As organizations mature in their data mapping practices, they encounter more complex issues that require advanced strategies.

Manual vs. Automated Data Mapping

One of the key decisions in data mapping is whether to use a manual or automated approach. Manual data mapping involves collecting and documenting data flows by hand, which can be time-consuming and prone to human error, but offers complete control over the process.

It is often more feasible for smaller organizations with limited data flows. On the other hand, automated data mapping uses software tools to scan systems and automatically generate data maps.

While faster and generally more accurate, automation can be resource-intensive and may require significant upfront investment. Automated tools are particularly beneficial for larger organizations with complex data environments, but they may still need manual oversight to ensure accuracy and context-specific adjustments.

Data Mapping for Emerging Regulations

The regulatory landscape for data privacy is constantly evolving, with new laws and amendments being introduced in various jurisdictions.

For example, while GDPR set a global standard, other regions like California (CCPA) and Brazil (LGPD) have introduced their own data protection regulations. Each regulation may have unique requirements, such as different definitions of personal data or specific rights for data subjects.

Organizations must adapt their data mapping practices to stay compliant with these emerging regulations, which may involve revising data maps, updating RoPAs, and ensuring that data flows comply with multiple regulatory frameworks simultaneously.

Data Mapping in Complex Environments

Data mapping becomes particularly challenging in complex environments, such as multinational corporations or organizations with legacy systems. Multinational corporations must manage data flows across different legal jurisdictions, each with its own set of privacy laws.

This requires careful mapping of cross-border data transfers and ensuring compliance with various international regulations. Legacy systems, on the other hand, often lack the documentation or compatibility needed for modern data mapping tools, making it difficult to accurately map data flows.

Solutions may include integrating legacy systems with modern platforms, conducting thorough audits to understand data flows, and gradually modernizing IT infrastructure to improve visibility.

Best Practices for the Data Mapping Process

Unsurprisingly, there are many different approaches to mapping your data, each of which will have its own set of benefits and challenges. Nevertheless, there are some common best practices you should keep in mind when exploring data mapping options. 

Make It One of Your Privacy Professional’s Core Responsibilities 

Like compliance itself, data mapping is an ongoing process, not a one-and-done task. That means it isn’t appropriate to assign to, say, your IT personnel, who have a slew of other responsibilities to attend to and will, therefore, be more inclined to treat it as a special project.  

Data mapping is best handled by a dedicated privacy professional whose sole responsibility is compliance activities like data mapping. 

Prioritize Mapping Systems With the Riskiest Data 

If you’re aware of systems that collect, process, and/or store sensitive data or particularly large quantities of data, that’s where you should begin your data mapping work. 

Odds are, there will be downstream flows that need to be accounted for, opportunities to reduce unnecessary data collection, or additional security measures you can employ.

Regularly Review and Update Your Data Map

Data environments are dynamic, and your data map can quickly become outdated if not regularly maintained. 

Regularly review and update your data map to reflect any changes in your data processing activities, systems, or regulatory requirements. This practice ensures that your data map remains accurate and effective for compliance and risk management.

Involve Multiple Departments in the Mapping Process

Effective data mapping requires collaboration across multiple departments, including IT, legal, HR, and marketing. Engaging these stakeholders ensures that all data sources and flows are accurately captured, making the data map more comprehensive and reliable. 

Cross-departmental input helps create a complete picture of your organization’s data landscape and supports effective data transformation.

Acknowledge Unknown Unknowns 

You may not know exactly what privacy risks exist in your organization’s various systems, but you at least know where to look to find out—right? 

In reality, you’ll rarely have a complete picture of all the systems and PI collection points at play in your organization. 

It’s important to acknowledge this reality and make plans to discover where unknown stores of PI may exist. 

Use Data Mapping to Support Data Minimization and Purpose Limitation

Use data mapping as an opportunity to identify and eliminate unnecessary data collection and processing activities. This aligns with data minimization and purpose limitation principles, reducing your organization’s risk and ensuring compliance with data protection laws. 

By minimizing the data you hold, you also reduce the potential impact of any data breaches.

Avoid the Data Science Bottleneck 

One approach to mapping your organization’s PI landscape is to leverage business intelligence and data science resources. 

However, there’s a major drawback to this approach; if your organization has these resources in place, it’s generally because they’re needed for a multitude of tasks. 

Data privacy compliance, unfortunately, will likely fall low on the list of data science priorities. 

Even when privacy-focused data mapping becomes necessary, the data science team likely will not have the same understanding of requirements as a privacy professional.

Document the Data Mapping Process

Document every step of the data mapping process, including the rationale behind decisions and any assumptions made. 

Thorough documentation not only aids in compliance but also provides a clear reference for future updates and audits. This level of detail can be invaluable during regulatory reviews or internal audits.

Integrate Data Mapping Into Your Broader Data Management Strategy

By aligning data mapping with your organization’s overall data governance efforts, you can ensure that data protection is consistent, scalable, and aligned with your business objectives. 

This holistic approach reinforces your organization’s commitment to robust data privacy practices.

Provide Ongoing Training and Raise Awareness

Ensure that your team is well-trained and aware of the importance of data mapping and its role in data privacy compliance. Regular training sessions can help maintain maintain data quality and data integrity across the organization. 

Awareness initiatives can also foster a culture of privacy, where all employees understand the significance of data mapping in protecting personal information.

Use an Automated, Privacy-Focused Data Mapping Tool  

It’s best to secure a privacy-focused, automated data mapping tool that your privacy professionals can use without relying on external teams and processes. 

If they don’t want to wait on data science resources, of course, your privacy team could just open up a spreadsheet and get to mapping—but this approach is prohibitively tedious. By the time you finish mapping your data with a spreadsheet, it’ll already be out of date! 

Consider automated data mapping tools instead. These make it easy to find, record, and work with PI, data warehouses, and data flows across multiple systems. Osano Data Mapping is a great example of such a tool. 

Book a demo to learn how mapping your data with the Osano platform can set your organization’s privacy program up for long-term, effective compliance.