Do you know that the data produced in 2025 is expected to reach 180 Zettabytes? Despite these enormous amounts of data, businesses struggle to utilize it efficiently to make sound decisions. For this reason, you need to know about efficient data mapping to enable your business to capitalize on the data it produces and processes.
Though data mapping is acknowledged, businesses struggle to implement it correctly. As a result, the process consumes resources without a tangible return on investment.
If done incorrectly, data mapping can result in non-compliance with various privacy laws, such as the California Consumer Privacy Act of 2018 {CCPA}.
An inefficient data mapping process can also cause numerous challenges, one of them being data silos. Having a data silo means that one department has information that is inaccessible to other employees. Another problem with inefficient data mapping is increased costs because data is more complex and time-consuming.
We have created this practical guide to help avoid problems associated with inefficient data mapping.
This article provides a comprehensive data mapping document for the year 2024 based on the emerging data landscape. The upcoming sections explore practical aspects of data mapping, such as initial planning, data profiling, maintenance, and tools to use.
Key Takeaways
- Data mapping improves data quality
- Regularly validate data map documents to ensure they are up to date
- Data mapping should involve stakeholders
Understanding the Importance of Data Mapping Documents
Data mapping documents are vital because they provide a holistic understanding of a business’s entire data landscape. The documents use a visual and detailed overview of data sources and how information moves from one destination to another.
There are various benefits of having data mapping documents. One is enhanced data visibility, in the sense that business managers understand the type of information they have and how it flows within the business.
The benefit of this is that it is easy to identify redundancies or inefficiencies in the data process. Other benefits of data mapping documents are:
- Improved decision-making: A comprehensive visual of how data moves within the business makes it easy for managers to make informed decisions. As a result, accurate and strategic choices are made.
- Efficient data integration: Data mapping documents make it easy to integrate different data sources into one system, making it easy to process information. The benefit of this is that the data mapping framework created makes it easy to adapt to changes and incorporate new technologies.
- Improves data quality: Data mapping documents improve data quality because the mapping process identifies errors and inconsistencies.
Lastly, structured data mapping contributes to regulatory compliance. Data mapping documents help ensure compliance with various data protection and privacy regulations, such as the General Data Protection Regulation.
Examples of how data mapping ensures regulatory compliance
An example is when a business uses data mapping to identify sensitive personal information that needs to be protected.
Data mapping can expose systems or processes that handle personal data. As a result, the business will implement appropriate safeguards, such as the use of access controls and encryption. This ensures compliance with laws such as the General Data Protection Regulation.
Regulatory compliance is also achieved when you create a data lineage. A data lineage shows how data moves from one source to another. The data lineage comes in handy when the business is subjected to regular audits. With a data lineage, there is transparency, which speeds up the auditing process.
Data mapping ensures regulatory compliance, especially where consent is a must. This is because the data mapped will tell you if the personal information being processed has consent or not.
Lastly, data mapping makes it easy to facilitate data subject rights, such as the right to have your personal information corrected or deleted. This is because when a request has been made for data to be deleted, the document will specify where this information is being stored and processed.
The benefits of having a robust data mapping framework
A data mapping framework is a set of guidelines that have been put in place to guide how to create, maintain, and manage data mapping documents. The framework serves as a foundation for achieving specific data-related goals, such as ensuring compliance and improving data visibility.
One advantage of having a robust data mapping framework is the ability to make informed decisions. This is because you have a clear understanding of the relationship between different data sets and what each piece of information does.
Another benefit is that the framework is vital in establishing and enforcing good data governance. This is because it provides a foundation for establishing accountability, access controls, and data ownership.
Components of an Effective Data Mapping Document
There are several components of an effective data mapping document. One key element that every data mounting document must have is the source of data.
The source of data is crucial because it provides a reference point for understanding the type of data being processed.
Also, a data source makes it easy for you to assess data quality by assessing its accuracy and completeness.
Other key elements that should be included in a data map document are:
1. Data elements
Data elements are pieces of information that are tracked as they move through different processes within the business. The elements can be simple pieces of information such as names and addresses of the consumer.
The elements can also include complex data structures such as product analogs and consumer records. Examples of common data elements are:
- Names: The name acts as a unique identifier to distinguish an element from another
- Data type: A data type specifies the format of the information at hand, and this can be in the form of a number, date, or text.
- Data quality checks: These define the procedures required to ensure the information is accurate and consistent.
- Target system: The target system details the type of platform on which an element will be stored.
2. Transformation Process
A good data map should describe how the business modifies and processes data. This process must be documented to ensure the data is usable and of high-quality.
When documenting the transformation process, document the rules and logic governing each transformation. The rules and logic should include mathematical formulas and conditional statements.
A transformation process shows transparency and aids in validating the accuracy of the entire process.
Another crucial piece of information that should be captured while describing the transformation process is information about the tools and technologies used.
Tools that could be used include scripting, custom-built applications, or ETL {extract, transform, load}tools.
3. Data destinations
Another key element to include in a data map document is information on data destinations. The data destination is the final location where a piece of information is stored and processed.
This section should include information on the databases or applications being used. It should also include the purpose and functionality of each application. This way, it becomes easy for stakeholders to understand the context in which the data is being processed.
4. Data relationships
It is important to include data relationships in the mapping document to ensure stakeholders understand how different data elements are interconnected.
Data relationships show how one data set influences another as it is processed and stored. There are several forms of data relationships. These are:
- Identification relationships: This shows how one set of information is linked with another data set.
- Cross-system relationship: This type of relationship shows how information is connected across different systems. An example is customer information that is held in an ERP system.
- Hierarchical relationships: This relationship can be organized using either a data flow diagram or an entity relationship diagram. An example is when a customer’s ID is used as a parent to make numerous orders.
There must be clarity, accuracy, and relevancy when demonstrating data relationships. A clear mapping document should be understandable, and this is achieved by using diagrams and charts.
To ensure accuracy, the mapping document must be regularly updated to reflect changes that occur within the data source.
5. Data lineage
Creating a data lineage is important because it helps you get better business insights. A data lineage uses visuals to illustrate how information flows through various data sources till it reaches its final destination. Some of the advantages of creating a data lineage are:
- It is easy to identify data sources
- Easy-to-understand transformations
- Enhances data migration
- Supports data governance
6. Security and privacy considerations
This element is important because it documents measures and controls that have been put in place to protect sensitive information.
When creating the mapping document, identify and label sensitive elements. Sensitive elements include the personal information of your consumers, health records, and financial data.
The sensitive elements must have access control with a description of who has permission to view and use the sensitive data. The mapping document must also include encryption protocols that are being implemented and can include things such as the use of encryption algorithms and passwords.
Lastly, a good data map document needs to be relevant to stakeholders. To do this, include elements such as the goals and objectives of the business.
To tailor the mapping document to the business’s specific needs, customize it for your intended audience. For example, if presenting it to executives, create a high-level overview of the data being presented. This makes it easy to create a data insight.
Other strategies that you can use to customize the mapping document are:
- Integrate the report with data governance
- Create a scalable document that can be easily changed
- Ensure the document is dynamic by allowing stakeholders to give feedback
Common Mistakes and How to Avoid Them
One common mistake made when creating a data mapping document is using incomplete data. One reason for this is the failure to add data elements. Omitting data elements such as names can create knowledge gaps and hinder integration efforts.
To avoid this mistake, have extensive stakeholder engagement. Other data mapping mistakes and their solutions are:
Failing to engage with stakeholders
Failing to engage with stakeholders can result in inaccuracies. This is because you will not get diverse perspectives on the kind of data being processed by various teams.
To solve this problem, collaborate with various data owners and end users to gather insights on the information they process.
Inconsistent formats and terminologies
Inconsistent formats and terminologies can make the data mapping document challenging to read and follow. As a result, this can hinder data collaboration.
One way to deal with this problem is to set up guidelines, consistent terminologies, and formats that should be used when creating the document.
Failing to document transformations
Failing to document transformations can result in the use of inaccurate data, which can result in non-compliance.
To deal with this challenge, document each transformation, guided by business rules and logic.
Neglecting data quality concerns
The accuracy of the data map document will depend on how reliable your data is. The more inaccurate the data is, the more unreliable the mapping document will be.
To address data quality concerns, incorporate data quality metrics and work with stakeholders.
Mapping documents are prone to errors and inconsistencies. Therefore, care should be taken to ensure that errors are minimized. One way of avoiding errors and ensuring accuracy is by having a clear objective.
By having a clear objective, you can define the purpose of the data mapping exercise and the scope. This way, the exercise will align with specific goals.
Other ways to minimize errors and ensure accuracy are:
- Regularly validating the data: The data should be cross-referenced with actual sources. Regularly validating it ensures that the correct data reflects what is on the mapping document.
- Have version controls: Version controls are essential because they enable you to keep track of changes or updates happening to the data on the mapping document. Version controls are also essential because they maintain an accurate and auditable record.
- Create documentation guidelines: Before starting the exercise, create a guideline for the procedure. Deal with issues such as the format to use and data to analyze.
- Facilitate data governance integration: Data governance integration is crucial because it ensures the data mapping exercise aligns with organizational policies and standards.
How to create data mapping documents as dynamic and adaptable tools
A good data mapping document should be dynamic and adaptable to change. A static document is prone to errors because it does not factor in changes made to a data set.
How do you make the document dynamic?
For starters, design it to be agile and adaptable. The best way to do this is to make room for accommodating new data sources and data structure updates.
Another way to make the mapping document dynamic is to create feedback loops that create room for feedback gathered from users and stakeholders. Also, have a document change management policy. The policy should act as a guideline on how changes are proposed, reviewed, and incorporated.
Tools and Resources for Data Mapping
There are several tools and software that can be used for data mapping. One such tool is Microsoft Visio. This tool helps you visualize data using professional diagrams.
Another tool that can be used for data mapping is IBM InforSphere. This is a data integration platform that makes it easy for businesses to understand how their data is being used and transformed. The tools also help businesses analyze and monitor data quality.
Apart from commercial systems, businesses can also use open-source data mapping tools such as Talend. Talend can detect data quality, integrate with other data sources, and identify errors.
The only way to achieve efficiency in the data mapping process is to use technology. Data mapping tools use AI capabilities to automate repetitive tasks. As a result, data mappers can focus on strategic tasks such as resolving data quality concerns.
Also, modern data mapping tools enhance efficiency by integrating with cloud-based platforms such as AWS that allow stakeholders to collaborate in real time.
To stay updated with changing data mapping practices, read learning resources such as Captain Compliance and data management blogs such as Datacamp.
We also recommend that you join LinkedIn groups that focus on data management and attend data management webinars.
Closing
Data mapping is a critical process for every business, especially if you are worried about compliance. If you use accurate data, the data mapping document will be consistent and reliable.
However, a simple mistake, such as failing to use consistent terminologies, can result in non-compliance. Captain Compliance has a wealth of experience that you should use to create a reliable data mapping document. We stay proactive, informed, and determined to ensure our clients implement compliant data mapping practices.
Give us a call for a free consultative session, and let us help you identify data relationships and create accurate data maps.
FAQS
1. What is data mapping under GDPR?
Data mapping under the GDPR helps businesses achieve compliance by enabling them to understand and document the type of data they process and store.
2. Is data mapping a GDPR requirement?
No, data mapping is not a GDPR requirement. However, the process makes it easy to determine how compliant your business is.
3. What tools can I use for data mapping under GDPR?
Popular tools that can be used for data mapping under the GDPR include IBM InfoSphere and Microsoft Visio.
4. Is data mapping necessary?
Yes, data mapping is essential because the increased transparency helps to ensure GDPR compliance.
