The Ultimate Guide to Data Mapping: Basics, Terms, Best Practices, and More

Data mapping allows organizations to stay aware of every byte in their data estate, meet compliance requirements, and implement strong security measures. Data mapping aims to create uniformity across data sets, which can be used for data migration, storage, and protection.While data mapping has been common in data management for years, it’s become increasingly critical for businesses to remain secure, compliant, and ready to make the most of their data. Additionally, the massive increase in the volume of data and its value has made effective management critical.However, gaining full control and understanding of a company’s data has become increasingly challenging — resulting in non-compliance, lacking security, and minimal data-driven insights. One study found that roughly 80% of respondents cited a lack of data cataloging as a top challenge in data management. Data discovery, classification, and mapping must all be in play for ongoing data cataloging. So, how can you improve your understanding of your data estate, better protect it, and remain compliant? Data mapping has become increasingly necessary — we’ll be exploring what this process is and how to put it to work for your business.

What is Data Mapping?

A high-level overview of data mapping is combining fields from many datasets into a centralized, consistently structured repository. Once complete, data from various sources share the same schema. A few reasons modern businesses prioritize data mapping is to enable data security, compliance, or migration.Data comes from various sources and is typically captured in different ways. However, data must be homogenized for security, compliance, and advanced use cases like AI-driven insights.The overall process of data mapping isn’t new; many businesses and industries have made it a standard practice. However, the growing data sources and increase in raw amounts has made data mapping more important — and more difficult — than ever before. Leveraging the right platforms and processes is necessary for effective data mapping.

How Does Data Mapping Relate to Security and Compliance?

Data mapping often associates data with security controls and data compliance requirements.  Data can be effectively secured based on existing security processes that leverage data classification. Ongoing discovery and classification allow data to share the same categorization, such as confidential, and can then be mapped to security protocols. Regarding data compliance, some regulations directly advise data mapping that complies with privacy and security requirements, while others will still benefit from it. A few examples include:

• California Consumer Privacy Act (CCPA) requires organizations to implement different security measures for data storage based on the type category of personal information. Data mapping allows organizations to implement consistent security controls on all customer data.
• General Data Protection Regulation (GDPR), another common data privacy and security regulation, emphasizes data minimization and accuracy. Implementing data mapping helps organizations stay on top of every byte, correctly classify it, and effectively secure data.

Data Compliance vs Data Security Compliance

Before diving deeper into data mapping, let’s quickly differentiate between data compliance and security compliance.These terms are related but not interchangeable. Data compliance is an overarching term that encompasses technical and non-technical practices for meeting regulatory requirements. Data security compliance is a sub-category under compliance that focuses specifically on securing data. Let’s look at a bakery as an example to break down these two concepts. Data compliance would be the equivalent of the health and safety standards the bakery needs to follow. This broad set of rules may include the workspace cleanliness, proper food storage, employee hygiene, and more. Meanwhile, data security compliance would be more focused on customer information collected. Data security compliance would focus on ensuring the cash register is locked, financial records are secure, and sensitive customer data is protected. It’s crucial to stay aware of these differences when exploring data mapping, as these processes will vary in execution based on the specific focus.

The Key Phases of Data Mapping

Data mapping ensures consistent data integrity and quality across all sources, enabling consistent data protection, compliance, and the ability to leverage AI use cases.How can your organization start with data mapping to benefit from it? Data mapping has several phases that ensure effective mapping for security, compliance, and overall management, which are:

1. Identify and define relevant data: Define the data that needs to be classified, moved between databases, or included in data compliance practices. For example, if you’re mapping data for HIPAA compliance, relevant data would be all patient information and records.
2. Map fields: The actual mapping process will depend on your chosen tool, but overall, this process is the more time-intensive and essential step in overall data mapping. Mapping fields calls for associating a field in one database with a field in another, making them equivalent to each other.
3. Test and deploy: Field mapping should be configured and then tested in small batches, possibly using test data, to make sure all identified data is accurately mapped to another database, security processes, or any other possible use cases for data mapping. Deploy in stages to avoid errors or issues, as you can review results and implement any necessary changes.
4. Maintain and update: Once fully deployed, continually maintain data maps so that newly captured data is correctly associated with controls, categories, or another database. Implement any necessary changes that will likely emerge over time, such as a compliance requirement calling for increased protections.

Due to the variety of possible use cases, you can see how the stages of data mapping can vary. However, the above overview is widely applicable and helps lay the groundwork for your specific processes.

Best Practices for Data Mapping

Data mapping is valuable and necessary for many modern practices, ranging from migration to data security. So, how do you effectively implement data mapping in your organization? Fortunately, you don’t have to start from scratch — the following best practices will help you establish and refine your data mapping practices.

Choose the Right Data Mapping Techniques

Data mapping involves three overall techniques that handle the nuts and bolts of the mapping process. The technique you choose will significantly inform your future processes and their results. These overall techniques are:

1. Automated: Choosing automated data mapping depends on finding the right software that’s capable of matching your current data structures to defined schemas or databases. This class of software leverages machine learning to monitor and understand your entire data estate, mapping data as necessary.
2. Semi-automated: A semi-automated technique involves team members reviewing the results of an automated system and making any necessary changes before approving them. This approach can be ideal for companies with limited budgets and simpler datasets.
3. Manual: Relying on manual processes for data mapping is generally unsustainable for organizations of all sizes, given the modern data landscape. Finding and mapping every bit of data in the organization involves having teams continuously working in an endless queue, which can also be error-prone.

Automated and semi-automated techniques should be sought rather than relying on manual processes. Even small businesses will likely struggle and introduce human errors to data mapping, making investing in the right platform critical for compliance and security.

Ensure You Can Map All Relevant Data Types

Most organizations capture and store a wide range of data types and formats. Automated tools must be ready to find, evaluate, and map your entire IT ecosystem’s data, as overlooking any data types can be detrimental. For example, many solutions overlook mainframe data, harming your mainframe security posture. If you use mainframes, make sure your chosen platform can discover and classify mainframe data.Fortunately, other data types are easier for platforms to discover and work with. Common data types you’ll likely need to map include the following:

• Images, including PNG, TIFF, and JPG
• Text, including TXT, XML, and HTML
• Audio, including WAV, MP3, and AIFF
• Containers, including ZIP, TAR, and RAR
• Databases, including XML and CSV

Before you sign up with a new data mapping platform, be aware of the data you need to be capable of working with. If you use any less common data type, finding a platform that can map them becomes even more important.

Prioritize Protected and Sensitive Data

Data compliance and data security compliance focus on specific categories of data, so this data should be prioritized. Specific regulations typically dictate specific categories and which data should fall into those categories. For example, the medical regulation HIPAA states that patient records, doctor’s notes, and test results should be classified as sensitive. As a doctor’s office starts adopting data mapping processes, it should begin by establishing these categories and how the mapping solution works with them.Ultimately, starting with the most important data allows you to reap the rewards of data mapping quickly, as you can effectively protect high-priority categories. From there, these processes can expand to other categories to flesh out the data mapping and compliance processes.

Data Mapping Begins with Data Discovery and Classification

Mapping data across data sets for migration, better security, or ongoing compliance is mission-critical for many organizations across all industries. Once fully implemented, the entire data estate can be understood, protected, and comply with applicable regulations.However, implementing mapping processes from scratch requires time and capital, which can be significant depending on your existing data management processes. The first step in data mapping is understanding your data, requiring knowing every byte in your data estate.Data discovery from Inventa allows your organization to continually stay aware of new data, automatically classifying it once discovered. Classification allows data to be protected, mapped and managed according to your needs and regulatory requirements.Is it time to step up your data management practices to avoid non-compliance, data breaches, and missing out on advanced insights? Contact us today to learn more about our platform and how it can form the backbone of ongoing compliance and security.