Most companies must manage massive amounts of data originating from countless sources. Enterprises often rely on several data storage systems, and each system may store that data in different ways and locations.
A truly Live Data Map should act as the strong foundation for your organization’s data privacy program and provide a much-needed framework for businesses seeking 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, and how does it serve within a privacy program framework?
This guide breaks down the purpose, processes, and tools you need to know.
What Is Data Mapping?
Data mapping is an essential, foundational process for organizations that desire a comprehensive and compliant privacy program. It involves identifying the type, location, and flow of personal data throughout an organization.
This information is crucial for:
- Assessing risks associated with the processing of personal data
- Identifying compliance obligations
- Implementing appropriate privacy controls
The resulting output of Data Mapping is called a RoPA (Record of Processing Activities) which is required by law as part of the European Union’s General Data Protection Regulation’s (GDPR) Article 30.
The data mapping process involves tracking, documenting, and inventorying the various data elements (data sources, data fields, data systems, data warehouses, etc.) a company controls and uses to collect data, along with all internal and external third-party systems that hold the collected data. It streamlines communication between disparate databases, particularly regarding sensitive, personally-identifying information.
Data maps can help bridge informational gaps and standardize enterprise data. A comprehensive data map can tell you where data is stored, how it’s stored, and, most importantly, how it all connects.
With this knowledge, you can bolster your enterprise-wide data practices allowing 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 to provide an essential overview of all the data inventory generated in and flowing through an organization. It’s a critical component for any modern company’s privacy program that aims to comply with regulations requiring you to know what data your organization collects and how you process and use data.
Data mapping is an essential compliance measure for companies beholden to data privacy laws, like the GDPR, CCPA, VCDPA, and CPRA. According to Rita Heimes, General Counsel and Chief Privacy Officer for 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.”
With a robust data map, you can provide answers to questions regarding:
- Source(s) of data ingestion (e.g. a marketing form)
- What data set you’re 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 hold plenty of sensitive data (personal information), and customer data is only a small piece of it.
More to the point, not every bit of data is equal. Some information proves more critical than others and requires more 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. 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.
Robust data mapping is an integral first step for several data-related use cases, including:
- Data Migration: A one-time transfer of data from a legacy system to a new source. Once moved, the original data source is retired.
- Data Integration: A continuous process of transferring data from one system to another, typically triggered by a specified event or as 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 or duplicates, removing nulls, enriching the data, or changing the data type.
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- 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.
- Data Audits: Maintain a detailed record of audit trails, logs, and records of processing activities (RoPAs) to identify errors and continuously optimize the data mapping process easily.
- Privacy Assessments: A data map provides a comprehensive inventory of collected and held data in order to properly conduct Privacy Impact Assessments (PIAs)/Data Protection Impact Assessments (DPIAs). These assessments help identify privacy requirements for a new product, project, or activity, and any changes to a company’s privacy practices, policies, or promises.
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The Data Mapping Process
What does the data mapping process look like? The specifics vary depending on the organization, its systems, the breadth and scope of its data, and whether it leverages automated tools.
The below process is a typical workflow for companies using a manual data mapping program. DataGrail can help automate these steps.
- Identifying Data Sources: Start with identifying all collected data and data sources. This is critical for preventing data loss, upholding data accuracy, and determining the correct data sets, data fields, data sources, and inputs involved in the process. Organizations constantly gather data from various sources like marketing forms or payment interfaces.
- Classifying Data: Organizations should use data classification to understand exactly where customer data and other sensitive information lives, who has access, and how it’s processed. This allows companies to improve their data management practices and apply appropriate safeguards and data access controls.
- Mapping Data Flows: Identify and map data flows and match different source data fields to their destination fields so there’s alignment between the two. If doing this manually, maintaining logs and monitoring the process helps prevent errors or data bottlenecks.
- Analyzing Risks: Once data flows are mapped, it’s important to review and analyze risks related to the collected data and deploy safe data management practices. Review different sources of data to ensure that all third-party app data integrations are able to keep sensitive data safe.
- Ongoing Maintenance, Updates, and Documentation: Data maps are dynamic, not static. They require constant maintenance, updates, and changes when new data sources are added or changed. Data mapping results in an accurate RoPA, which is the documentation required by GDPR’s Article 30. RoPAs should stay up to date with data maps as they evolve.
Data Mapping Techniques
- Direct Mapping: Mapping data fields directly from source to target without any transformation or modification
- Concatenation: Combining multiple data fields from the source into a single target field
- Lookup Tables: Replacing or mapping source data values to corresponding values in the target system using a lookup table
- Data Transformation: Manipulating or converting data during the mapping process, such as cleansing, aggregation, or calculations
- Conditional Mapping: Mapping between source and target fields based on specific conditions or rules
- Schema Mapping: Mapping the overall structure or schema of the source data to the target data structure
- Field-Level Mapping: Mapping individual data fields from the source to their corresponding fields in the target system
- Hierarchical Mapping: Mapping parent-child relationships and maintaining hierarchical structures in nested data formats
- Semi-Automated Data Mapping: Combining manual mapping with automated suggestions or recommendations from machine learning algorithms
- Template-Based Mapping: Using pre-defined templates or mappings for commonly encountered mapping scenarios
- Metadata-Driven Mapping: Utilizing metadata about the source and target data to guide the mapping process
- Automated Data Mapping: Automated data mapping processes — like DataGrail’s Live Data Map — immediately reduce risk, eliminate human error, and allow employees to focus their time and energy elsewhere
Data Mapping Best Practices
Organizations 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 the Privacy by Design program. If 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 tools at its core (find out which solutions will fit your company best with this Data Privacy Solution Buyer’s Guide).
Ensuring Data Quality and Addressing Challenges
Continual data mapping helps to improve and maintain data quality for your organization. This is vital for stakeholder verification and validation when fulfilling data subject requests (DSRs).
However, data mapping can present three major challenges:
- Complexity: As the amount of data and sources increase, the mapping process becomes exponentially more complex and time-consuming, especially using manual processes. Although a data map can be built in a spreadsheet, it will grow increasingly impractical and untenable for larger organizations dealing with complex data structures.
- Sheer Volume: The massive volume of data that businesses collect in an increasingly digital world can present a large issue for data mapping, especially if the mapping is manual. As the amount of data grows, so too does the risk of human error, likely resulting in duplicates, inconsistencies, and other data quality issues.
- Evolving Nature: Data maps should be living documents that thrive under a close eye and regular maintenance, and organizations must budget for the necessary software and labor. Manually maintaining a data map increases the risk of inaccuracies or oversights, but powerful software automation allows employees to focus on more engaging work requiring a human touch.
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 data processing activities (RoPA) 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.
It’s not just the GDPR your organization must prepare for. The CCPA, CPRA, VCDPA, and forthcoming regulations have and will continue to implement similar requirements surrounding data mapping. Practically every modern privacy law released over the last few years requires businesses to 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” may 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 at a smaller scale, the sheer volume of data makes such an undertaking inefficient and unreliable at best.
Because of these challenges, modern enterprises are increasingly eschewing outdated methods in favor of automated data mapping tools that enable them to optimize the entire process.
What Should You Look For in a Data Mapping Tool?
Automated data mapping tools like DataGrail’s Live Data Map can help improve mapping accuracy and efficiency and build a strong privacy program foundation built upon transparency. With an automated data mapping solution, it’s easier for companies to understand data sprawl across tech stacks, respond to DSRs, and complete privacy assessments.
Some data mapping software features to look for include:
- A Live Data Map: An easy, visual representation can illuminate complex mapping and data flows. A real-time data map provides a blueprint of where data lives within an organization and allows for increased analytics to make data-driven business decisions.
- Automated DSR Fulfillment: The best data maps help fulfill privacy requests by leveraging machine learning-based tools to instantly identify the relevant set of data and where that data lives across the entire business, populate the details, validate and verify the requester, and fulfill the DSR. DataGrail’s Request Manager integrates with our Live Data Map to do just that.
- Data Format Support: Organizations often have various data formats and systems housing their held source data and require a system that can support a wide array of different data formats. Common data formats and sources include XML, CSV, spreadsheets, Excel, JSON, SQL Server, and more.
- Automatic Updates: Data mapping programs should leverage automation whenever possible. This saves time and reduces human errors like duplication and misclassification to make sure that your data map is always updated.
- Audit Trails: The system should maintain a detailed record of audit trails, logs, and records of processing activities (RoPAs) to identify errors and continuously optimize the data mapping process easily.
- A Comprehensive Privacy Dashboard: Data mapping gathers important business information across all systems, and a comprehensive Privacy Dashboard like DataGrail’s helps provide privacy insights, analysis, and trends with powerful user interface workflows.
Data Mapping: Business Intelligence With DataGrail
Data maps provide an essential overview of all the data inventory generated in and flowing through your organization. With an overview in hand, your business can understand its regulatory compliance obligations and track sensitive information requiring higher levels of protection.
But for that, your company needs the right tools.
DataGrail is the key to modern, automated data mapping. The DataGrail platform provides a single place to streamline workflows, ensure data quality, and manage a comprehensive, powerful privacy program that outsmarts risk and builds transparency and brand loyalty.
Learn how your business can build an effective data map that protects privacy and adheres to ever-changing compliance regulations. Request a 1:1 demo to learn how your business can get started with a simplified automated mapping approach today.
Sources:
IAPP. Top 10 operational responses to the GDPR – Part 1: Data inventory and mapping. https://iapp.org/news/a/top-10-operational-responses-to-the-gdpr-data-inventory-and-mapping/
GDPR Hub. Article 30 GDPR. https://gdprhub.eu/Article_30_GDPR