Data Privacy

Data Mapping 101: Purpose, Process, & Tools

DataGrail, July 15, 2022

Practically every company must manage massive amounts of data, originating from countless sources. Most enterprises rely on several systems to store their data and each system stores that data in different ways and locations. 

To properly ensure all of the relevant information reaches its intended destination, you need a roadmap. That’s where Live Data Mapping comes in. 

Data mapping provides that much-needed framework for businesses that seek to analyze and act upon their data, migrate data between systems, or transition from legacy systems to newer modalities.

But what is a data map exactly, and how does it serve within the privacy framework? 

This guide breaks down the purpose, processes, and tools you need to know.  

What is Data Mapping?

Data mapping is the process of tracing, documenting, and mastering the links between various data points in different databases. It streamlines communication between disparate databases, particularly regarding sensitive, personally-identifying information. 

Data maps seek to bridge the informational gap, standardizing all enterprise data by connecting data fields from one source to data fields in another source, then centralizing it all in a single place. It tells you where data is stored, how it’s stored, and, most importantly, how it all connects. 

Equipped with this knowledge, you can bolster your enterprise-wide data practices, thus enabling you to: 

  • Record information accurately
  • Prevent errors and redundancies
  • Secure your sensitive data
  • Prepare for data reporting 

What Is The Purpose of Data Mapping? 

At its core, the purpose of data mapping is interoperability-enabling disparate computer systems and software to exchange data and then make use of that information. This is a critical component of any company’s modern privacy program, which requires that you know what data your organization collects and how it’s processed. 

Data mapping is an essential compliance measure for companies beholden to data privacy laws, such as the GDPR, CCPA, VCDPA, and CPRA. According to Rita Heimes, General Counsel and DPO at the IAPP: “It is quite difficult, for example, to prepare a privacy statement or an internal privacy policy without understanding what data is collected, how it is processed, and with whom it is shared.” 

But with a robust data map, you can answer questions on topics, such as:

  • Source(s) of data ingestion (e.g. a marketing form);
  • What data set you are collecting (e.g. name, phone, and email);
  • The purpose of the data (e.g. send relevant communication over email);
  • The handling of the data (e.g. store the information in Oracle Marketing Cloud and sync the consumer to Salesforce);
  • The retention timeline of the data (e.g. if the individual doesn’t purchase after 6 months, delete this information).

Why Does Data Mapping Matter? 

Businesses produce plenty of sensitive data (personal information), and customer data is only a small piece of it. 

More to the point, every bit of data isn’t equal. Some bits of information prove more important than others and require protection. Some data is shared, repeated, used, or stored in multiple locations. Different parts of organizations will use the same bits of information in different ways.

If data is mapped improperly, it could be corrupted or lost en route to its destination. And of course, to be useful, the data map must be more than a static snapshot of a point in time-it needs to be actively maintained as your organization grows and evolves.

As a result, robust data mapping is an integral first step for several different data-related management processes, including:

  • Data migration – A one-time transfer of data from a legacy system to a new source. Once moved, the original source is retired.
  • Data integration – A continuous process of transferring data from one system to another, typically, this is triggered by a specified event or part of a scheduled timeline.  
  • Data transformation – A process of converting data from a source format to the new destination’s format. Examples of this include deleting redundancies, removing nulls, enriching the data, or changing the data type. 
  • Data warehousing – Pools all the data into a singular source for analysis, queries, or reports. Data in a data warehouse has already undergone the three processes above. 

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The Data Mapping Process

What does the data mapping process look like? 

The specifics will vary depending on the organization, its systems, and the breadth and scope of its data. That said, data mapping will typically follow these overarching steps:  

  1. Define the data – Start with identifying which data needs to be moved as well as data that doesn’t need to move. From there, define the data relationships and their significance then set prioritizations for data sets. This is critical for ensuring that no data is lost and that the data’s accuracy is upheld. 
  2. Map the data – Your next task is to identify data flow and match source fields to their destination fields so that there’s alignment between the two. For this, maintaining logs and monitoring the process helps you prevent errors or data bottlenecks. 
  3. Transform the data – If necessary, you may need to convert the data from the previous format to the destination format. Doing so allows it to be properly stored and then leveraged at a later stage. 
  4. Test the process – You can run a system test using sample data to see whether the process works and is error-free. After, you can adjust accordingly. The three primary forms of tests are:
    • Visual
    • Manual
    • Automated
  5. Deploy the data management process – After the tests have confirmed that the data transformation is operational, you can schedule the migration or integration.
  6. Maintain and update – As mentioned, data maps aren’t static, they’re dynamic. So, they will require constant maintenance, updates, and changes when new data sources are added or changed.

Best Practices for Data Mapping 

Your organization can address the challenges present in data mapping through a few best practices.

  • Provide a top-down approach:  For data mapping to be effective, leadership must buy-in as part of your privacy by design program. If your executives view this work as unimportant, employees will naturally deprioritize it. Setting a top-down example ensures that the entire organization takes the process seriously. 
  • Prioritize sensitive data:  Carefully consider what personal data requires higher levels of protection. While some of this information is obvious, like data that directly leads to an individual, some of it may be more organization-specific.
  • Leverage software:  Integrate data map maintenance into software development processes and ongoing changes driven by functions that interact with an individual (i.e. marketing, e-commerce, and human resources).
  • Invest in a privacy solution:  Ideally, you need a privacy solution that features data mapping at its core (find out which solutions will fit your company best with this Privacy Management Solution Buyer’s Guide).

Data Map Regulatory Requirements

The EU’s privacy law, the General Data Protection Regulation (GDPR), directly addresses data mapping in Article 30, stating in part:

  • Each controller and, where applicable, the controller’s representative shall maintain a record of processing activities under its responsibility.
  • Each processor and, where applicable, the processor’s representative shall maintain a record of all categories of processing activities carried out on behalf of a controller.
  • The controller or the processor and, where applicable, the controller’s or the processor’s representative shall make the record available to the supervisory authority on requests.

But it’s not just the GDPR your organization must prepare for. The CCPA, CPRA, VCDPA, and forthcoming regulations have similar requirements surrounding data mapping. Practically every modern privacy law released over the last few years has required that businesses be able to respond to consumer requests.

The easiest way to comply with any of these laws is by creating an up-to-date data map.

Finding the Right Data Mapping Tool

While the word “map” might imply a visual representation of data systems in use at your company, a data map is often just a table of information. While manual data mapping is possible, the sheer volume of data makes such an undertaking inefficient and unreliable at best.

There are two major challenges of data mapping: 

  1. Complexity:  As the amount of data and sources increases, the mapping process becomes exponentially more complex and time-consuming, especially using manual-based processes. Although a data map can be built in a spreadsheet, it will grow increasingly impractical and untenable for larger organizations.
  2. Evolving nature – Data maps are living documents that thrive under a close eye and regular maintenance. Organizations must budget for the necessary software and labor. Human maintenance increases the risk of inaccuracies or oversights—while software can free employees for higher-leverage work.

Because of these significant challenges, modern enterprises are increasingly eschewing outmoded methods in favor of automated data mapping tools, which enable them to optimize the entire process. 

What Should You Look for in a Data Mapping Tool? 

Data mapping tools benefit your organization by increasing data transparency, affording you greater visibility and control, and empowering you to drive the most value from your data analytics. 

The end result: better organizational insights and increased regulatory compliance. 

But how do you find the right tools for your organization? 

Features you should look for include: 

  • A live data map – An easy visual representation can illuminate your data’s comings and goings. It provides a blueprint of where data lives within your organization.
  • Request manager – Anytime a data subject request (DSR) arrives, the best systems will instantly identify where that data lives across the entire business, populates the details, and then fulfills the DSR.
  • Data format support – Since you may have various data formats and systems housing the data, you need a system that can support a wide array of formats.
  • Automated scheduling – You want your system to be set-and-forget wherever possible. Automation saves time and reduces human errors.
  • Audit trails – The system should maintain a detailed record of audit trails and logs so that you can easily identify errors and continuously optimize your data mapping process. 

Data Mapping: Business Intelligence with DataGrail

Your data map provides an overview of all the data inventory generated in and flowing through your organization. With that overview in hand, you can then understand your obligations under compliance regulations. Just as importantly, with data mapping, you know which sensitive data requires higher levels of protection.

But for that, you need the right tools. 

DataGrail is the key to modern data mapping. The DataGrail platform provides a single place to manage your privacy program.

Learn how your business can build an effective data map that protects privacy and adheres to ever-changing compliance regulations. Request a demo to learn how your business can get started with a simplified automated mapping approach today!  


IAPP. Top 10 operational responses to the GDPR – Part 1: Data inventory and mapping.

GDPR Hub. Article 30 GDPR.