Data Mapping: Definition, Benefits and Best Examples

The general idea of data mapping is to process or match multiple data fields into a schema or a centralized database. Its purpose is to combine data sets and compress them as one.

Research shows that companies using data-driven approaches are 23 times more likely to acquire customers and 19 times more likely to achieve profitability.

An in-depth understanding and good customer relations allow data-driven companies to outperform their peers. It increases the company's value and reputation, which helps for the growth of the business.

What is Data Mapping?

Data mapping is a method where multiple data sources are compressed or integrated into one another. 

With data mapping, companies collect data similar in type, form, and content into a centralized database. This helps reduce redundancies and creates accurate analysis of the given sets of data. 

In other words, data mapping is a set of instructions on moving data from one database to another and using it as a tool throughout an organization.

Benefits of Data Mapping for Your Business

Data mapping reduces the risks of committing mistakes and spending time redoing things. 

While decreasing potential data errors, data mapping increases an organization’s ability to deliver precise data output. 

Here are the most important benefits of data mapping for your business.

Data Quality

Data mapping is crucial in identifying and rectifying data discrepancies or errors. When mapping data between systems, inconsistencies can be detected and corrected beforehand, ensuring data accuracy and reliability. 

Standardizing data into a common format improves its manageability, understandability, and quality. Data mapping also confirms adherence to specified formats or predefined value ranges to validate data integrity.

Data Integration

Automating data integration through data mapping establishes connections between different data sources, facilitating efficient data sharing across systems. For instance, integrating ERP with CRM provides insights into customer relationships and financial performance. Standardizing and integrating data from diverse sources creates a unified data view for informed decision-making.

Data Governance

Data mapping improves data data governance, facilitating comprehensive data understanding. It aids in cataloging data, safeguarding sensitive information, ensuring quality, and meeting compliance standards. Data mapping also aids governance goals by identifying and safeguarding sensitive data.

Data Security

Data mapping categorizes sensitive data, ensuring compliance with regulatory requirements regarding its storage and usage. It employs data encryption and access controls to safeguard data from unauthorized access or breaches. Real-time data tracking promptly identifies and mitigates potential security threats, further improving security.

Time and Cost Efficiency

Efficiently integrating data sources via data mapping minimizes the need for manual data entry and error correction, resulting in significant time and cost savings. Data mapping automates error detection to streamline compliance adherence and mitigate the risk of penalties for non-compliance.

Analytics

Data mapping consolidates data from diverse sources, standardizing formats, and providing a unified data view for analysis. This comprehensive data analysis drives accurate decision-making.

Compliance

Data mapping ensures data adherence to the required formats and structures for compliance purposes. It validates data accuracy, completeness, and other qualities, facilitating compliance with regulatory standards. Data mapping also enables tracking data changes, aiding in auditing and compliance reporting.

Five Steps of the Data Mapping Process

Considering all the benefits of data mapping, the next thing to learn is the step-by-step process. There is no one size for all. The pattern will depend on the output you want. The following are the general steps to consider when starting your data mapping process.

1. Identify all of your data sources

Identifying all the data sources is the first step to having a sound data mapping system. 

Define the types of data you want to collect and from which sources you want to obtain them. Properly performing this step makes it easier to organize the data afterward. 

These data sources can include relational databases, flat files, APIs, external systems, or data warehouses. 

This step also involves understanding the relationship between the data systems you choose.

2. Profile and make your data uniform

The next step is to profile the data correctly and uniformly. This step will show how data is categorized in tables, files, or formats. 

Other databases use data profiling to identify relationships between tables via primary keys or other links. 

This way, it becomes easier to track data discrepancies.

3. Test your process

Run a small-scale test on the drafted mapping system to check for errors. This test will ensure the system's quality and help detect early mistakes. 

For automated data mapping software, validation or checking might be much lighter since these have verifications and real-time alerts. 

For manual data mapping, having an experienced developer overseeing the whole process might help.

4. Complete the transition

After testing the system, data migration, integration, and transformation can now be complete. This is when the process of transferring data from one source to another begins. 

Depending on the program maker's design, it can be done as a one-time data migration or ongoing data integration.

5. Maintain and update

To secure the quality of the program, consistent maintenance is necessary. A few modifications are handy to prevent more significant losses in the future.

Data mapping is a continuous process that will require updates as new resources, inputs, and other data enter the program.

How to Create a Data Mapping Template [Example]

Let’s use an example to understand the data mapping concept. Here is a sample of a supermarket database organized in columns.

Each has a name and product code, while frozen goods have categories, dry goods have weight, and dairy products have neither. 

Compressing all these databases simplifies as one can provide a holistic view of the company's data management. This method requires a clear map to match all the identical data fields. 

If we apply the idea of data mapping, here’s how it would look based on the given model earlier.

In this given example, all the similar data are connected.

The process of data mapping can be based on the number of datasets to be combined, the amount of data, the number of schemas involved, and the data hierarchy. 

There are several data mapping techniques to consider when drafting a mapping system.  They can be based on the circumstances or total cost of the platform. 

The level of technical knowledge may also vary when it comes to the selection of data mapping techniques. 

Manual Data Mapping

This technique is an excellent data mapping tool for one-time data inputs or customized information types. 

Although some workforce is required, the company can fully control the data map, which can be used to the organization's advantage. 

Manual data mapping involves individuals such as data analysts or engineers. Their role is to oversee data structures, understand the rules, and define the critical relationships between elements.

Semi-Automated Data Mapping (Schema Mapping)

Semi-automated data mapping is the hybrid approach where developers handle automated systems. 

This approach identifies and automates data elements, schema matching, and other basic mapping rules. 

At the same time, developers handle the more complex part of the mapping system. It also uses graphical representations to link data or create schema maps in a visual interface.

Automated Data Mapping

In automated data mapping, software tools with algorithms are the key process drivers. This is ideal for non-technical people since it does not require high technical knowledge. Most programs use a graphical user interface to be user-friendly and for a more smooth deployment.

Dealing with Challenges in Database Mapping

Issues can arise during the data mapping process. Here are some of the challenges that you can expect.

Data Consistency

Most errors result from data inconsistency, which can include inconsistencies in data formats, naming conventions, and other data types. Such discrepancies can lead to challenges in establishing mapping rules, which in turn leads to errors and data quality issues.

Volume of Data

The volume of data input becomes a challenge when operating on a larger scale. Dealing with diverse data types and sources may also complicate the process. Regardless of the approach or technique used, potential performance issues may arise.

Overall Complexity

Data mapping is quite complex as it consists of data structures in different formats that may be unfamiliar, especially to non-technical persons. 

Understanding such information may require additional effort from the persons in charge.

Schema Evolution

A data mapping system requires regular updates. This will lead to schema evolution over time. The sources and target destination may change from time to time. For long-term projects, good management is necessary to keep track of all the changes in the structure.

Real-World Applications of Data Mapping

Data mapping can be utilized in different contexts and across different industries. This includes the retail, healthcare, and finance industry. 

Using the data mapping system in these sectors enables organizations to derive meaningful insights.

Finance Sector

Data mapping is essential to accurate financial information management. 

Organizations use data mapping techniques to understand transaction records, income statements, balance sheets, and investment records. 

A structured system can help identify essential data relationships, ensuring consistency, transparency, and compliance with the standards.

Retail Industry

Data mapping fills the gap between online and offline shopping experiences in the retail industry. It pulls data from different sources to draft a full view of the customer profile. 

Sources may come from websites, social media, and e-commerce platforms. Having a complete customer profile allows retailers to draft marketing campaigns accordingly.

Healthcare Systems

Data mapping helps achieve a good circulation of electronic health records. It is used to match the right source to the right target, which results in easy access for professionals to share vital patient information and other necessary healthcare data. The drafted data is used for data migration and integration.

Data Mapping Made Simple with Kohezion

Proper data mapping prevents errors and improves data consistency, governance, and integration. You can implement data mapping projects through manual, semi-automated, or automated processes. You also have to be aware of possible challenges to successful data mapping. If you need a tool to ensure your data mapping processes succeed, check out Kohezion today.

Conclusion

Data mapping is a vital process that ensures accurate and consistent data transfer, transformation, and integration across systems. Careful planning and using the right tools can help organizations maintain data integrity and quality, facilitating seamless operations and insightful analytics. Whether migrating to new systems or integrating multiple data sources, data mapping is crucial in the modern data-driven world.

If you have any questions or need further assistance, feel free to contact us at Kohezion. We're here to help!