Data Mapping: A Complete Guide to Process, Benefits and Best Practices

Modern companies gather data from nearly any possible source - websites, mobile apps, CRM platforms, accounting programs, storage databases, spreadsheet files, cloud solutions, and a whole lot more systems. However, the main issue is that these sources sometimes do organize your data very differently.

Example: one software could keep a customer name under the field Customer_Name, whereas another one might refer to this as Full_Name. Some might store dates with DD/MM/YYYY format, whereas others are expecting YYYY-MM-DD.

This is basically why data mapping is required. It defines data relationships between the systems so that while data travels from one source to another it can still be translated without losing its meaning.

What Is Data Mapping?

Data mapping means linking data elements from a source data set with those in the output data set to form a mapping between them.

That is a bit like telling the data where it needs to go! Imagine that a business is updating its customer database from an aging CRM system into a newer one. The old CRM could have fields like:

These are the data fields that might be involved:

For the information to be transferred properly, each source field should first be matched up with the correct destination field.

This pairing sets the stage for the mapping of data. Inaccurate mapping might result in data being wrongly inserted in different fields, having its formatting changed, being duplicated, and/or being totally lost during a move.

Why Data Mapping Matters?

Most organizations no longer depend solely on one system.

Salespeople may keep their contacts in a CRM, finance might use an ERP product, marketing people could run an automation software, and executives might base everything on several dash boards.

The point is, so that these various apps can inter operate and share the required content among themselves, accurate transference of data is essential.

With accurate data mappings, an organization ensures not only of good data quality but also prevents mistakes resulting from manual data entry.

This becomes vital especially when a company is handling:

Let's say you need to bring into a new system 100,000 customer records which have already been collected. Manually correcting data that were wrongly mapped is going to cost you time and energy. If the mapping is done correctly before any kind of data migration, you will save yourself a few headaches.

Understanding the Data Mapping Workflow

It is true that each project brings with it a distinctive set of challenges, however, mapping projects are largely carried out according to a similar process flow.

1. Determine the Source Data

The initial thing to do is to figure out where the data is located now.

The source may be a database, spreadsheets (Excel), CRM, API, ERP system, business-related system, or even flat files like JSON, XML.

Teams should familiarize themselves with the structure, format, and quality of data before creating the mappings.

2. Determine the Target Structure

A close inspection of the destination system must be conducted next.

Knowledge of its fields, formats, validation rules, required values, and relationships is crucial.

This is very helpful especially in schema mapping where the two systems might be organizing similar information completely through different structure.

3. Align the Source and Target Fields

Now, field mapping.

One to one connection are established from the field in the source system to the right field in the destination system.

For example:

Some straightforward matching of items can be quite a breeze while others may involve hundreds if not thousands of relationships depending on the complexity of the system.

4. Set Transformation Rules

Data does not always flow seamlessly from one system to another. A data manipulation operation will also be necessary in many cases.

For example, one database can store the value:

whereas the target system needs:

Transformation rules help keep the data as the target system expects the format.

5. Test and Validate

Test mappings through sample data prior to transferring the whole databases.

Testing helps discover if some fields are missing, formats are wrong, values are duplicated, transformations are invalid, and unexpected relations are found.

Skipping validation might lead you from having only a little mapping error to turning it into a big data headache.

Where Is Data Mapping Used?

Data integration is perhaps the best example of data mapping.

When two business applications need to share information, mapping makes sure that the two systems get the information meaning right.

Another major use case is data migration. The companies that are changing an old software or moving databases to cloud computing need very good data mapping so that their information ends up exactly where it should.

The first step of ETL includes a data mapping process.

ETL is an abbreviation of the term Extract, Transform, and Load. Data is extracted from the source, modified to the right format, and finally transferred to another system.

Mapping rules are basically a set of instructions defining how that data is supposed to flow throughout the pipeline.

Manual and Automated Data Mapping

Mapping can be done by hand, for example, writing the code, or using data mapping tools.

With small simple datasets, manual mapping is quite feasible. Nevertheless, it gets difficult fast when you have hundreds of fields or several systems involved.

Platforms that are current can detect schemas automatically, recommend field matches, apply transformation rules, and verify data to some extent.

Automation might save a lot of time, but human supervision still plays a big role.

In some cases, a system can detect that two fields have the same technical structure, but it cannot see the difference that is very important from the business perspective.

The ideal setup is a blend of machine processing, straightforward business rules, and a human touch for validation.

Data Mapping: What Are the Best Practices

Nice data mapping is not simply about making a connection between two field names.

A group of individuals should write down detailed mapping rules that are essential to a system, so if another person wants, he / she can easily figure out why such relationships have been defined.

At the same time, they should check if the data is of the correct type, describe how missing or empty values will be processed, test transformation rules, and also pay attention when new fields are added or old ones are removed in source and destination schemas, which would affect mappings.

Most importantly, you should consider mappings just like documents - things that will need to be modified from time to time.

Software programs keep changing. Database schema are upgraded. New fields are introduced. Some fields are retired.

Periodically updating data mappings helps avoid scenarios where the system keeps producing wrong data without giving any indication to the users that there is an error.

Conclusion

In today's organisations, the flow of information is almost continuous.

What makes this whole effort challenging is more than just moving data from one system to another. A huge part is making sure that the information's interpretation, or, in other terms, the semantics, has been kept the same after the transfer and that the data can be effectively used by the target system.

You guessed it, data mapping has been providing that!

Whether it is your company's application's integration, database migration, analytics pipeline's development, or a legacy modernization, if the right design of the data mapping has been made, it is the data that would be connected with various databases through such mapping.

The more you focus on analyzing source data, formulating the transformation rules, executing the mappings, and developing the documents the more you will be able to avoid costly problems later.

As a matter of fact, the quality of mapping is what enables a business to have confidence in the data stream of their systems at large and data confidence eventually translates into efficient processes, insightful reporting, and sound decision-making.