Data Management

What Is Data Mapping – How To Do Data Mapping + Examples

The average company is now dealing with large amounts of complicated data systems. With siloed data in many places, linking and managing this data into a manageable centralized database is a priority for many businesses.

The amount of data sources that the average company is using is rapidly increasing. Data comes in many different forms and types, and it can be extremely complicated to ensure that data is structured universally.

That’s where companies are increasingly looking at data mapping. To take control of their internal and external data and find a solution that can organize, structure, and create a unified central data location.


What is data mapping?

Data mapping is the process of matching fields from multiple datasets into a schema, or centralized database. Data mapping is required to migrate data, ingest, and process data and manage data. Ultimately the goal of data mapping is to homogenize multiple data sets into a single one.

Data mapping means that different data sets, with varying ways of defining similar points, can be combined in a way that makes it accurate and usable at the end destination.

Data mapping is a standard business practice. However, as the amounts of data and the complexity of systems that use the data has increased, the process of data mapping has become more complicated and requires automated and powerful tools.


An example of data mapping

To help to understand what data mapping is and how it works, we are going to look at an example of multiple databases where data mapping is helpful. The data we are looking at is related to footballers, and the information is organized into columns and fields and has a different way of organizing the data

(click to enlarge).

Each of these databases has similar and different entries. For example, all of them have an id. The payers and managers have a wage entry, and teams are the only ones that have a field for stadium.

Merging all of these databases into a single entry means that you can query a single database to retrieve information on each. For businesses, this is invaluable as it provides a holistic view of the companies data assets.

Bring databases together requires a map of the fields that clarify and match fields that should intersect. It sets rules on how to hand data from each input, what type it is, and what should happen in the case of duplicates, or other issues.

Here’s our example again, but in with our map connecting the correct fields to produce a single database.

In this example, we have added some smart conversions as are possible in the Wult platform. We have set the currency on the output wage field to convert values from different currencies. We have an inferred field – the platform automatically finds the league and uses this to create a new field with the value. Along with this, a country field is added.

To summarize, data mapping is a set of instructions that allow for multiple datasets to be combined, or allow for a dataset to be integrated into another. This example is more simple, but the process can become exceedingly complicated based on the following factors:

  • The number of datasets that are being combined
  • The amount of data
  • The frequency that the data should be mapped
  • The number of schemas that are involved in the mapping process
  • The hierarchy of the data being combined


Why is data mapping essential?

Data mapping is essential for any company that processes data. It’s mainly used to integrate data, build data warehouses, transform data, or migrate data from one place to another. The process of matching data to a schema is a fundamental part of the flow of data through any organization.

Data mapping is the key to good data management. Unmapped or poorly mapped data will cause issues as data flows to different endpoints within an organization. Mapping is the first step to getting the most out of your data when it reaches integrations, transformations, and when it is stored for future use.

An organization that uses data makes use of data mapping at three main stages of the data flow. These are data integration and data transformation. Let’s take a brief look at data mapping in each of those contexts.


Data integration

Integrating data into a workflow or a data warehouse requires data mapping. In many situations, the data that is being integrated will be in a different form to the data that is being stored in the warehouse (or elsewhere in the workflow).

For a data warehouse, the primary mapping process involves identifying the incoming data, and it’s attributed and matching this to the warehouse schema. Specifically, the process will include looking for areas where the datasets overlap and defining the rules that will govern the mapping process. For example, if both databases have similar information, which one should be used.

Solutions like Wult make ingesting data simple and pain-free in these situations. With unlimited integration sources, you can build a centralized data warehouse that is accurately mapped, clean, and usable from minute one.


Data transformation

Data transformation is all about taking data in a specific format and converting it into a different format or structure. This step can be a crucial stage to prepare information that is ready to ingest into a warehouse or integrate into an application.

Data mapping is vital in this process as it is used to define the connections between data and helps to determine the relationship between datasets.


How to do data mapping effectively

Getting started with data mapping can be a daunting task. However, implementing a robust solution early on in the data lifecycle can save you vast amounts of time in the future and ensure that your data is robust and reliable.

These steps will help you to understand what you need to do before, during, and after initiating your data mapping solution.

Define the data that will be moving. This means that you should look at the tables, fields, and the format of these. Think about the frequency that data will need to be mapped.

Map the data. This stage requires you to map fields in the source data to fields at the destination.

Define any transformation that you’ll need. For example, this could be rules or governance procedures that deal with clashes in data or duplicates.

Test the mapping process. Start with a small amount of data and test to see if the data mapping works as expected.

Once you are happy that everything is working correctly, you can start your workflow or deploy your mapping system. If you are using a platform such as Wult, you can see in real-time where errors occur and attain full visibility at before and after points.

Maintain and update the mapping process. This will require input as new data sources are added with new fields.


Data mapping techniques

So you have been through the process, and you know what you need to do. But how do you select the right tool for data mapping? What options are there, and what techniques can you use to build a robust data mapping solution?


Manual data mapping

This is the first solution to create a data mapping tool for your business. This requires developers to code the connections that match the source data to the final database. For one-off injections of data or custom data types, this could be a viable solution.

However, the scale of most datasets and the speed needed to adapt to how these change in today’s data landscape mean that a manual process can struggle to deal with complicated mapping processes. In these cases, businesses will need to move to an automated solution.


Fully automated mapping

Fully automated data mapping tools allow businesses to seamlessly add new data and match it to their current schemas. Most tools make this p[process available in a UI so that users can visualize and understand the stages that data flows through and map fields at each stage.

Some allow inputs from thousands of different sources, and the mapping process lets users bring data in an agnostic way to their databases and solutions.

The benefits of a fully automated solution are that it provides an interface that means nontechnical employees can monitor and set up data mapping. As well as this, users can check and visualize how their data is being mapped, identify errors quickly, and improve the process simply.