Data is an integral part of every business. It is how you make decisions and plans that shape the future of your organization and give it higher chances of success. But for data to give you valuable insights into your operations, you need to map it correctly, and that’s why the CPRA data mapping compliance code advises that all data mapping procedures need to be automated.
Hence, with the use of automated data mapping tools, you can scale your business with data-driven insights. You no longer have to deal with the stress of running multiple failed marketing campaigns to get to your customers. When data is mapped correctly, you can run campaigns based on their interests and behavioral patterns.
In this post, we’ll be explaining what data mapping is and the five best practices for effective execution.
What is Data Mapping?
Data mapping is the process of integrating data from multiple sources. It is a way through which sense is made out of multiple data sets by matching them into a schema for better presentation and analysis.
In simpler terms, data mapping is fitting different data into their required groups for easy translation. Data mapping ensures that data sets are matched to their destination such that they are accurate and useful at the planning or decision-making stage.
Data Mapping Techniques You Should Know
There are three main techniques of data mapping when you’re connecting different objects in an integration flow. They are as follows:
Manual Data Mapping:
Manual data mapping allows you to create, control, and customize your map from scratch. It is the first approach to developing a data mapping solution for your business. The manual technique requires the help of a developer(s) and other professional coders on board that will help with the coding and mapping of your data sources.
However, this technique is considered outdated and ineffective in modern businesses due to a large amount of data that’s now available for use. But it can still be a useful mapping tool for a one-time process when the database is small or not so complex.
Semi-automated Data Mapping:
The semi-automated data mapping technique is a combination of automated and manual processes in data connection. Some coding knowledge is required in this process for successful execution. This means that a developer is needed to work with software to create a connection between the different data sources and other members of the team crosschecks the connection and manually adjust any errors discovered in the process.
This technique is good if you’re running your business on a budget. It is also advisable to use this method when working with a small amount of data, and you need to perform some migrations, basic integrations, and transformation tasks.
Fully Automated Data Mapping:
Automated Data Mapping is a modern approach to data integration. All processes are carried out by software, which means you don’t need a coder, and you can use it to map larger databases. This technique, however, requires you to learn the dynamics of the software in use.
With the use of this software, all you need to do is drag and drop data to their respective schema. Unlike the manual and semi-automated mapping solutions, you can monitor and make mistakes more quickly.
The Best Mapping Practices
If you want to successfully migrate your data, here are the five best practices you should know:
To map your data accurately, the first thing you need to do is identify what data you want to include in your mapping process. Ensure that the relationships between each data and their significance are properly interpreted.
You also have to prioritize when identifying data, especially in cases when you need to map some data sets before others. Even when using the automated technique to map data, you need to ensure that no data is lost or inaccurate due to human error, so be sure to do a double check.
Perform Data Mapping:
Using one of the techniques explained above, you can map your data from its source to its relevant destination. Also, keep an eye out for errors to ensure total accuracy.
Ensure Data Security:
With the right mapping tool, you can protect your projects and only allow access to certain users. When choosing a data mapping software, be sure to look out for this feature to keep your projects secure.
Automate the Process:
To reduce error probability and prevent naming inconsistencies, automate the data mapping process.
Maintain and Update:
Your data flow needs periodic maintenance for optimum functionality. This is essential when newer data sources are added. If you want to improve your flow and keep it running, maintain and update your system.
For better insights into your business, data mapping needs to be done right. With the techniques and practices explained above, you’ll get your desired results in no time.