What is Data Mapping? (Defined & Explained)

What is Data Mapping?

Data mapping is an essential, foundational process for organizations that desire a comprehensive and compliant privacy program. The process of data mapping involves tracking, documenting, and integrating 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.

Effective data management starts with data mapping to provide a holistic view of personal data and business information collected, held, and processed across an organization. The data mapping process can be extremely complex and painstaking, especially when mapping across different sources relies on error-prone manual tasks.

Data maps seek to bridge informational gaps, standardize all enterprise data by connecting data fields from one source to data fields in another source, and centralize it all in a single place. It tells you where data is stored, how it’s stored, and, most importantly, how it all connects. Effective data mapping should provide an organization with the ability to provide the following information about any data element they hold at any time:

• What is being processed?
• How is it classified?
• What is the format?
• Where is it going?
• Why is it going there?
• Where did it come from?
• What is the lawful basis for processing it?

Data Mapping: Key Concepts & Definitions

• What are the types of data involved in data mapping?
  • A data map should track employee, business, and customer data. Examples of some personal data that should be tracked include: 
    • Names
    • Addresses
    • Phone numbers
    • Email addresses
    • Credit card/banking information
    • Order history
    • Browsing history
• What are some common data sources and formats? 
  • Organizations are constantly gathering data from various sources like marketing forms or payment interfaces. Common data formats and sources include XML, CSV, spreadsheets, Excel, JSON, SQL Server, and more.
• Records of Processing Activities (RoPAs) 
  • GDPR’s Article 30 stipulates that organizations processing or controlling personal data must maintain a thorough, written log of their data processing activities. RoPAs are obligatory internal accountability documents that help an organization map its general personal data processing practices. 
• Data models
  • A model organizing data elements and standardizing their relations to one another and the properties of other entities.
• Data structures
  • A common term for structures used to organize, manage, and store data.
• Data transformation or data conversion
  • 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.
• Metadata
  • The “data about data,” metadata provides context for the data being mapped, making it easier to map and integrate with other data sources and workflows.

• Data quality and validation.
  • 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).
• How does data mapping help improve and maintain data privacy and regulatory compliance with GDPR, CCPA, and other laws?
  • In today’s digital world, organizations are constantly gathering sensitive data, which can quickly become overwhelming and difficult to track and organize. Providing stakeholders with choice and transparency when it comes to data privacy is quickly becoming the norm, and regulations like GDPR, CCPA, VCDPA, and others are proving this. To ensure simple compliance with these regulations, it’s vital for organizations to implement data mapping to understand: 
    • Source(s) of data ingestion (e.g. a marketing form)
    • What data set they’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)
  • Understanding the above helps organizations quickly and accurately respond and complete privacy requests and assessments.

Data Mapping Techniques

• Manual Data Mapping
  • Manual data mapping is performed entirely by humans which is time-consuming, resource draining, and prone to human error. One of the biggest issues with manual data mapping is the lack of ongoing, real-time data mapping as an organization grows and changes. Complex data mapping processes make it difficult to run an entirely manual mapping program.
• 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. For example, our Live Data Map is supported by 2,000+ powerful integrations that make it easy to integrate new apps and systems and discover shadow IT.
  • Powerful automation also supports ongoing, real-time data mapping as an organization grows, shifts, and collects more data from new and existing sources.
• Semi-automated Data Mapping
  • Semi-automated data mapping processes combine elements from both automated and manual data mapping activities.

Data Mapping Software

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 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.
• 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 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 helps automate these steps.

1. Define the Data: Start with identifying which data needs to move and which doesn’t. From there, define the data relationships and their significance, then set prioritizations for data sets. This is critical for preventing data loss, upholding data accuracy, and determining the correct data sets, data fields, and inputs involved in the process.
2. Map the Data: Identify data flows and match source data fields to their destination fields so there’s alignment between the two. For this, maintaining logs and monitoring the process helps prevent errors or data bottlenecks. 
3. Test the Process: Run a system test using sample data to see whether the process works and is error-free. Adjust accordingly. The three primary forms of tests are:
   • Visual
   • Manual
   • Automated
4. Deploy the Data Management Process: After the tests confirm the data transformation is operational, schedule the migration or integration.
5. Maintain and Update: As mentioned, data maps aren’t static, they’re dynamic. They require constant maintenance, updates, and changes when new data sources are added or changed.

Robust data mapping is an integral first step for several different 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.
• 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.

Who are the stakeholders involved in data mapping? It depends on the size and strength of an organization’s privacy program. If an organization is set on building and maintaining a labor-intensive manual data map, it will likely need to hire specific data mapper system operators, data systems administrators, and CRM specialists to deal with marketing-specific data, among other positions.

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).

The Bottom Line on Data Mapping

Data maps provide an essential overview of all the data inventory generated in and flowing through an organization. With an overview in hand, businesses can understand their regulatory compliance obligations and track sensitive information requiring higher levels of protection. 

But for that, companies need the right tools. 

DataGrail is the key to modern, automated data mapping. The DataGrail platform provides a single place to 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!