What is data mapping?

‍Data mapping is an effort to map data locations and flows across the business data ecosystem, internal and external systems, to understand where data is located and how it's moving. A proper data mapping approach enables your team to identify the relationship between data models and structures to facilitate smooth transformation, migration, warehousing, and integration processes as well as complete data privacy compliance.

Why data mapping matters

Understanding what data mapping is empowers your team to manage information with greater consistency and accuracy. Through the best practices of data mapping, you can ensure that the information remains accurate as it travels from point to point, from sender to recipient.

Data mapping also helps your team align data management practices with the latest industry compliance and regulations. The data mapping process offers a transparent roadmap of data movements, optimizing privacy risk assessments, database governance, and applying the necessary safeguards for safeguarding sensitive and personalized information.

Your organization can glean powerful insights through data mapping from the available information in multiple data systems and sources. Data mapping empowers your team to streamline analytics and benefit from accurate data-driven decisions regardless of their destination.

What are the consequences without data mapping?

As organizations expand their data networks, it becomes increasingly important to ensure accurate and reliable data. A single data discrepancy could result in poor business decisions, legal complications, and financial mistakes.

Poorly managed data compromises the integrity of your organization, resulting in costly project delays and reputational damage. Research shows that 77% of organizations report data quality issues, with 91% attributing its direct impact on their company's performance.

Data mapping prevents these issues by ensuring that data fields tally with each transfer and transformation layer and stays consistent in every source. You can achieve a seamless data mapping process by applying specialized tools that automate complex data mapping according to your processing needs.

Data mapping process

It is important to identify the types of data mapping involved, such as mapping on the cloud versus on-premise practices. Your team must ensure that the transferred data remains accurate after the mapping, with data correlations kept intact and data presented in the relevant format.

How is data mapping done?

The following steps can help your team achieve the best data mapping practices:

1. Conduct a system discovery scan: A thorough system discovery scan helps you assess the data within your relational and non-relational databases, classifying data sets for streamlined management. You could apply advanced technology like responsive machine learning to drive a predictive assessment and management of your ongoing data processes.
2. Classify the information: Deep discovery scans inform your team about the sensitive and personal information in internal and external systems, each requiring added care and attention. Once you have identified these data sources, you can update user preference and privacy management with real-time accuracy.
3. Test the mapped logic: Before completing a mapping process, it is strategic to test a sample of your data logic to ensure that it is progressing in the right direction. Consider applying automated tools to verify that your software runs smoothly as intended. The measure helps you minimize the work required in the event of a mistake or error. These include white box testing, where you can add, update, or delete data that would execute a system trigger.
4. Deploy the mapping process: Once your sampled data logic passes its respective tests, you can go ahead with the rest of the process by leveraging a reliable data mapping tool.
5. Maintain and regularly update your mapping processes: Maintaining and updating your data is equally as important as establishing the flow. It is crucial to ensure that you continue following the best data mapping techniques with the addition and modification of data sources and destinations.

Data mapping GDPR

The General Data Protection Regulation (GDPR) is a stringent data privacy and security law that applies to organizations within the EU and companies that target or collect the data of EU residents. Data mapping helps your company fulfill GDPR criteria by providing a clear and detailed view of your data movements, driving the following processes:

• Implementing data protection measures: The transparent layout of data movement allows your team to determine the potential privacy risks involved in your databases. As such, you can implement the right protective measures, such as setting up security controls, qualifying data transfers, and managing user access privileges.
• Checking against consent and agreements: A portion of GDPR revolves around user consent and agreement conditions. Data mapping helps your team zoom in on the legal aspects of each user interaction and the purpose of data processing interactions. By mapping the data, teams can identify the legal procedures required to gather privileged information, such as seeking explicit consent.
• Minimizing Data: The GDPR follows the principle of minimization or data avoidance, where data processors should limit data use to those "adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed." Data mapping makes it easier to monitor and shortlist data points to fulfill the principle of minimization.
• Supporting data protection impact assessments (DPIAs): Data mapping provides the key insights (i.e., data flow descriptions/diagrams and potential privacy risk factors) for creating the DPIAs that identify and mitigate data protection-related risks. The GDPR declares DPIAs mandatory for new high-risk processing projects.
• Identifying data sources and channels: An organized data mapping process enables your team to identify the exact source of confidential and personalized information. By doing so, you can determine if the targeted data falls under the jurisdiction of the GDPR and ensure necessary compliance.

Mapping tools examples

Data mapping tools automate data collection from multiple sources before formatting and organizing them according to the requirements of a target database. Some of the most popular techniques include:

• Conditional mapping: The process applies data mappings based on corresponding user-defined conditions.
• Constant mapping: Imputing fixed or constant values to a targeted data field regardless of source information.
• Lookup mapping: A process that maps lookup values to source data.

When it comes to managing a large-scale organization or enterprise, you'll need a trusted solution that you can rely on to run smoothly across data sources and web services, provide real-time mappings, and resolve discrepancies without fuss. There are lots of options but these are some of the best data mapping tools available. 

Ketch data mapping and discovery solution

Ketch is a leading platform among modern data mapping tools, offering comprehensive features for identifying sensitive personal data across your data ecosystem. Our advanced solution keeps you informed of real-time permissions for data use so that your data mapping processes always align with the latest policies of compliance standards like the GDPR. Ketch guides you through a scalable and comprehensive approach to data mapping, reliably serving privacy use cases like data subject rights fulfillment and risk assessments.

Leveraging the Ketch data mapping and discovery platform also enables you to:

• Replace manual survey-based data discovery with a reliable automated process.
• Boost privacy team productivity by eliminating compliance access requirements.
• Apply robust machine-learning technology for constant visibility of your data from a centralized interface.
• Keep your team in the know to drive the best decisions without sieving through copious information.

Request a demo with Ketch to optimize your data mapping capabilities and eliminate the complexities of privacy management.

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Keep reading! Related articles and pages:

• Sensitive personal data
• Data discovery: the foundation of your data privacy program
• Ketch Data Mapping and Discovery

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