As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies
May 2025 | Source: News-Medical
Organizations in today’s data-driven environment can be working with data sources from various outdated systems, cloud systems, or even from the third-party application; and with that, data integration is happening continuously. One important step in leveraging successful integration is ensuring that data dictionary mapping is taking place. Data dictionary mapping is the alignment of elements of data from multiple collecting and receiving systems to ensure the data will be consistent, accurate, and flowing through various platform types.
In this article, we will discuss what data dictionary mapping is, why it’s important, and how to execute proper data dictionary mapping to accomplish seamless data integration.
Data dictionary mapping is the process of matching fields or data elements from the data dictionary of one system to the data dictionary of another system. A data dictionary is essentially a catalog or repository of metadata that defines and describes data elements (the metadata includes the data element’s name, type, length, allowed values, and relationship to other data). [1]
For example, a system might store a customer’s birthdate as DOB, while another system might use Birth_Date. Correctly mapping the data elements ensures that when integrated, or when migrated data between systems, the data is understood and interpreted by the systems in a consistent manner.
Inconsistent and/or erroneous data mapping can lead to incorrect data transformation, loss of data or other misinterpretations having high risk operational and compliance implications. [2]
Before you get going, you will want to be clear on the goals of the data dictionary mapping exercise:
What systems are being integrated? What data domain (for example, customer, finance, HR) is in scope? Are you migrating, synchronizing, or standardizing data? Knowing the ultimate purpose of the project will help you design your data dictionary mapping service around explicit business requirements.
Every source system that you are integrating should have a published data dictionary. In the event there is no data dictionary for the source, you will need to either build one through data profiling or ask the system administrator to help you. [4]
What you will want to get:
Before you can start to map, proper preparation requires that you normalize and clean your metadata. This normalization and cleaning may entail many different actions, such as:
This is the meat of the data dictionary mapping exercise. Determine how fields relate to each other, and there are common types of mappings:
You will want to use mapping tables or spreadsheets to document each match, including transformation rules and notes as needed.[3]
Manual mapping is very error-prone because of human experiences, especially in large and complicated datasets. You might want to consider using a dedicated data dictionary mapping tool or using a professional data dictionary mapping service.
If you do choose to outsource to a data dictionary mapping service, you want to ensure the vendor has industry standards and compliance experience in your context.
It is important to tester everything (even if you don’t) before deploying! Test for:
Ask business analysts or appropriate subject matter experts to check that mapped data is contextually correct. [2]
The documentation you provide should capture the following:
Documentation provides both the clarity and transparency you need when applying data mappings to your systems, it will also make future audits or updates simpler. [4]
Data dictionaries and mappings are not static. Data mappings should change as systems evolve. Form a governance model to regularly review and update data mappings.[5]
Determine triggers such as:
Despite the significance of data dictionary mapping there are many obstacles faced by organizations when considering data dictionary mapping.
Using experienced data dictionary mapping providers can assist with many of the problems mentioned above by providing tools; to assist as well as frameworks and domain knowledge related to dictionary mapping. [1] [3]
Data dictionary mapping is an important first step to successful data integration. Whether you are merging, building a data warehouse or performing live data synchronization, without the appropriate plans mapped, your chances of being successful are slim.
Utilizing a structured approach, and experts on data dictionary mapping, organizations can mitigate significant integration risk, improve data quality, and support better decision-making.
