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 that use data mapping techniques are growing at an average of 30% yearly.
An in-depth understanding and good customer relations allows 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.
Why is Data Mapping Important for Your Business Growth
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 three reasons why data mapping is essential for your business.
Data Consistency
Data consistency in data mapping is the act of delivering the data in a precise manner. Data must be transferred or integrated from one source to another while maintaining consistency.
Proper data mapping allows organizations to align data fields between systems. This results in accuracy and synchronicity. At the same time, they avoid duplicate files or incomplete reports.
Data Governance
Data governance is a set of policies that ensures data reliability. It involves general management of the company's data assets to ensure precision, consistency, and accuracy.
It provides a framework and guidelines for correctly mapping and managing the given data through its cycles.
For example, organizations may implement naming conventions and establish data stewards (people who have exclusive access to certain data types).
An excellent data governance system enables the organization to repeat a good cycle or process. It also ensures they adhere to data privacy regulations while ensuring proper handling of data.
Data Integration
Data integration synchronizes data from multiple sources into a single source.
With that in mind, data mapping helps establish the relationships between data and other elements to secure the integrity of the process and the accuracy of data compilation.
Data integration’s primary task is to collect disparate and transform data accordingly. It also provides timely analysis and reports to support decision-making situations within the organization.
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 get them. Doing this step properly makes it easier to organize 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.
By doing so, 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 quality of the system and help detect early mistakes.
For automated data mapping software, the validation or checking might be a lot more light 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.
It can be done as a one-time data migration or ongoing data integration, depending on the design of the program maker.
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, and it will require updates as new resources, inputs, and other data come into the program.
How to Create a Data Mapping Template [Example]
Let’s have an example to understand the data mapping concept. Here is a sample of a database of a supermarket organized in columns.
| Dairy Products | Dry Goods | Frozen Goods |
| Name | Name | Name |
| Product Code | Product Code | Product Code |
| Expiration | Expiration | Expiration |
| Brand | Brand | Brand |
| Weight | Category |
Each column has both similar and different entries.
Each has a name and product code, while the frozen goods have categories, dry goods have weight, and dairy products have none of both.
Compressing all these databases simplifies as one can provide a holistic view of the company's overall 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 and understand the rules while defining the critical relationships between elements.
Semi-Automated Data Mapping (Schema Mapping)
Semi-automated data mapping is the hybrid approach where automated systems are handled by developers.
In this approach, identifying data elements, schema matching, and other basic mapping rules are automated.
At the same time, developers handle the more complex part of the mapping system. It also uses graphical representations for linking data or creates 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 come from data inconsistency. There can be inconsistency in data formats, naming conventions, and other data types. Such discrepancies can lead to challenges in establishing mapping rules. This will lead to errors and data quality issues.
Volume of Data
The volume of data input becomes a challenge when operating on a larger scale. In addition, dealing with diverse data types and sources may add complexity to the overall 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 various 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 various 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 various 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 in matching the right source to the right target. This results in easy access for the professionals to share vital patient information and other necessary health care data. In addition, 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 are a success, check out Kohezion today.
