What Is Data Mapping, and Why Is It Important
Data mapping is an essential component of your organisation’s data flow management, and it is used in many digital initiatives that businesses undertake. Whether modernising legacy systems, integrating corporate applications, or developing software solutions from scratch, you’ll have to perform data mapping. That’s not to mention implementing modern business intelligence (BI) and data analytics tools.
This article defines data mapping and explains why it is sometimes better to hire big data consultants for your data project rather than trying to do it yourself.
This article is part of our data series, where we highlight different aspects of enterprise data flow management and monitoring. Check out our blog for information on data masking, data governance, and unstructured data. We also explain the difference between a data warehouse, a data lake, and a data lakehouse, and offer a guide on how to prepare data for machine learning algorithms.
What is data mapping, and how does it work?
Essentially, data mapping is the process of matching data fields from one data source to data fields in another. This allows businesses to link information from multiple databases and data models, resulting in a 360-degree view of their operations.
As simple as it may appear, the process is fraught with complexities and pitfalls that, if overlooked, can jeopardize the success of your software development, migration, or integration initiative.
What is the purpose of data mapping?
Data mapping is rarely done on its own. It is usually carried out as part of a larger project’s data journey. When you need to change an existing data structure or create a new one, you are very likely to use data mapping.
Data mapping is essential during the following initiatives:
- Data integration. The activity involves consolidating information from different sources. Usually, it is a recurring process. For instance, data integration tasks (or jobs) can be scheduled daily or triggered by an event.
- Data migration. As the name implies, it is the process of moving information from one system to another. After migration is complete, the original data source is often subject to decommission. One example of data migration is moving data from a legacy system to a new system or archive.
- Data transformation. The task revolves around converting data from one structure to another. This includes cleaning up data, removing duplicates and nulls, and so on. The conversion of information from freeform text to a more structured format, such as a comma-separated values (CSV) file, is an example of data transformation.
- Deploying reporting tools. Data mapping is critical when implementing reporting tools, as their terminology and data structure may differ from those of your corporate software solutions. As a result, organisations need to map their data against the new reporting tool’s schema.
- Custom software engineering. If your company is developing a new software solution to expand its digital capabilities, connecting its back-end database or storage unit to existing data sources is critical.
And how can data mapping help with the initiatives mentioned above?
Every application in your IT infrastructure generates data, and each of these data sources has its structure or schema.
Consider the following scenario: a single data element from one structure corresponds to a combination of elements in another. A single ‘full_name’ field in one database may be equivalent to a combination of ‘given_name’ and ‘family_name’ fields in another database.
Furthermore, in some cases, you may need to perform a mathematical calculation to align the data. For example, when matching the ‘expiration_date’ field in the destination structure, you may not find an exact match in the source scheme. As a result, you will need to figure out the ‘expiration_date’ by adding the ‘validity_period’ to the ‘production_date.
Data mapping techniques
The table below lists several data mapping methods, along with their benefits, drawbacks, and suitability.
Data mapping done right: an example from the IT Rex portfolio
A digital health startup approached ITRex to extend the functionality of its mental health portal. The company wanted to integrate data from different EHR and EMR systems into their web portal database to give doctors access to patient information, such as demographics and medical history.
Essentially, this was a data integration project that required data mapping from the source systems (EHR and EMR systems) to the target system (the startup’s database).
As the first step, our data expert opted for Redox as a tool that can automatically integrate data from EHRs and EMRs of various clinics and deliver it as a JSON file containing one unified dataset through its API. Next, the data specialist manually mapped the data fields from the Redox API to the corresponding data fields in the client’s database. This was a challenge as much of the Redox data did not have a direct match in the portal’s database. For instance, some data fields that Redox delivered as a single entry corresponded to an aggregation of entries in the portal database. So, our expert had to parse the single entry and break it into multiple tokens.
Furthermore, some of the Redox data was not understandable and not relevant to this project. Our expert communicated back and forth with Redox engineers to clarify different aspects and coded the mapping rules into a script so that all information on new patients can be automatically positioned in the correct fields in the future.
Thanks to the exceptional technical knowledge of our data specialists and their meticulous attention to detail, the portal seamlessly integrates with various EHR and EMR systems used by mental health facilities across the USA. The solution provides a wealth of information on patients’ well-being, empowering physicians to make better-informed decisions.
Does it make sense to do data mapping without hiring data professionals?
The simple answer is yes.
You can complete the data mapping process without the need for external consultation.
However, it requires a thorough understanding of the business processes, the nature of the data collected, and how data mapping tools work (if you intend to use any). As a result, you are likely to invest in specialized training and allow your employees to drop all other tasks in order to focus solely on mappings. Even then, the process is expected to take a long time.
What can inexperienced in-house staff overlook?
Your internal team may understand the fundamentals of your data, its structure, and how it’s used in day-to-day operations, but they may not have a complete overview of the data flow or the specialized skills required for data mapping.
However, data specialists are an entirely different story. They’re not just familiar with data mapping; they’re pros at it, with a wide range of experience across various systems. Their expertise allows them to complete the task faster and recommend improvements to your databases.
These changes can make everything run smoother and faster. For example, your team may be familiar with how to map data, but if the database responds slowly, the associated processes will slow down. Data experts consider the big picture: they analyze your data and plan how it will fit seamlessly into the organizational workflow. They frequently anticipate and avoid potential problems. This includes choosing the right storage, making sure your data loads efficiently, designing indexes, and ensuring your database’s future performance.
As a result, data mapping initiatives will be more successful if they are carried out collaboratively by subject matter experts within your organization and external data specialists.
Here is one example of data experts coming to the rescue
One of our clients had an online collaborative platform to capture usage insights in software products, and they wanted to build a reporting tool to go along with it. The company felt confident enough to do the mapping internally. But when they submitted the results, some key aspects were missing.
First, there were some critical data points that the client simply could not locate. They knew they had it somewhere, but they could not pinpoint where. Our data experts used reverse engineering to understand the business logic before calculating and aggregating the missing data.
Second, the platform did not collect all of the data required for the reporting tool. We advised the client to make specific changes to their product to begin gathering and cleaning up the missing data.
Data mapping steps
If you have decided to do the mappings in-house, here are five data mapping steps to help your team get started:
Step 1: Clearly define your target schema/outcome and determine how the target database will look
Step 2: Choose the data sources you want to use. This can include business operating systems, relational databases, and API-generated data in CSV/JSON/XLSX files, among other formats. You should be able to clearly understand the structure of this data and the relationships between its fields.
Step 3: Identify data entries requiring transformation before mapping
Step 4: Formalize the transformation rules and the mapping logic
Step 5: Test your logic on a small data sample and make the necessary adjustments
Data mapping best practices
Whether you decide to work on data mapping alone or hire an expert, here are some best practices to guide you.
- Standardise and document the naming conventions for data fields
- Consider using readily available automated tools and implementing scripts to minimise reliance on manual efforts whenever possible
- Completely document the data mapping process, procedures, and tool configurations (if they affect your data)
- Implement versioning for the mappings and all related artefacts so that you can roll back to previous versions if needed
- Classify data according to its sensitivity level and take extra precautions to protect sensitive data. Remember that data mappings are created to be used in data processing, so marking specific fields as sensitive will help the development team process them safely in the future.
- Encourage collaboration between data specialists, domain experts, analysts, and the legal team
