Data mapping is the process of establishing a connection between different objects in an integration flow. It is a rudimentary and one of the initial stages of all data management processes. In data mapping, different unsorted data objects are connected by using specific pre-set procedures to categorize data into a distinct set.
But you need to follow certain sets of standards to link data. These standards are specified depending on the domain values of data models under consideration. You can use numerous data mapping tools and various models in this process based on your business needs and the particular use-case. In this article, we’ll discuss 5 data mapping best practices that you can follow for faster and accurate results.
Top 5 Data Mapping Best Practices
The following are the 5 best practices that can help you build your data mappings effectively.
1. Identify and Map Data
Many sophisticated data mapping tools allow you to map objects in a flow simply by dragging-and-dropping fields of one object to the corresponding fields of the other. These solutions are designed to work with large volumes of intricate data while complying with all General Data Protection Regulation (GDPR) guidelines.
2. Ensure Data Security
Advanced data mapping tools are enriched with many security features that allow you to secure your integration projects by allowing access to restricted users based on user roles. Simultaneously, these solutions also allow businesses to carry out a risk analysis of their data.
3. Automate the Process
To resolve inconsistencies in naming conventions, some tools offer automated data mapping capability. For instance, in Astera Centerprise, users can utilize this feature by creating a synonym file dictionary that includes current and alternate names of a header field in a table. On runtime, Centerprise automatically matches these dissimilar fields and extracts data from them.
The automated data mapping feature is especially helpful when dealing with large datasets, as it reduces the error probability during mappings.
4. Maintain Periodically
To ensure an effortlessly running system, you must frame and execute a consistent maintenance schedule. To address the distinctive challenges of a use case, you may have to update or modify a flow at any given point. These alterations can disrupt the mappings and by extension, your data flow. Therefore, it’s best to ensure the accuracy of your mappings before executing the process.
Features like Instant Data Preview offer transparency into mappings of any integration flow. This feature allows you to debug complex mappings in real-time and prevent errors before executing the flow.
5. Keep Records of Data Sharing
You need data maps to identify all the entities with whom you have shared the data. This helps during personal information tracking by utilizing requests and at the time of deletion. Take account of use-cases for each mapping, classify applications that use the maps, and document the source-to-target convention of mapping and how it is used in the workflow.
Proper planning and effort go a long way when it comes to data mapping. These five best practices will help you obtain optimal results from your integration projects without any imprecisions and faults. And the key to that is using a good data mapping tool that simplifies and optimizes your processes more efficiently.
Astera Centerprise is an enterprise-ready data mapping tool that offers the same level of usability and performance to both developers and end-users. It is equipped with an industrial-strength ETL engine, SmartMatch functionality, support for workflow automation, Instant Data Preview, out-of-the-box connectivity to a wide range of data sources, and a completely code-free environment.
Download a free 14-day trial and explore how you can create drag-and-drop data mappings to execute your integration flows.