Data mapping is an essential part of your organizational data flow management, and it’s used in many digital initiatives that companies undertake. It’s a part of your data modernization journey, software development and integration projects, and more. You will also use data mapping when setting up new business intelligence (BI) tools.
This article clarifies the data mapping definition and explains why it’s sometimes better to involve big data consultants in your data project instead of attempting data mapping with your in-house team.
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.
Without further ado, let us investigate the data mapping process.
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. It’s used to link information across multiple databases and data models.
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’s typically a part of the data journey within a larger project. Every time you need to change an existing data structure or establish a new one, you are highly likely to do data mapping as part of this process.
Data mapping is essential during the following initiatives:
- Data integration is about consolidating data from different sources. Normally, it is a recurring process. For instance, data integration tasks (or jobs) can be scheduled on a daily basis or can be triggered by an event.
- Data migration is the movement of data from one system to another. After migration is complete, the original data source is often subject to decommission. One example is moving data from a legacy system to a new system or archive.
- Data transformation involves converting data from one structure to another. This includes data cleansing, eliminating duplicates and nulls, etc. One example is transforming data from free text into a more structured format, such as a comma-separated values (CSV) file.
- Deploying reporting tools. Some ready-made reporting tools have a different terminology and a predefined data structure, and companies need to map their data to the reporting tool’s schema.
- Custom software development. Any new software will have a backend database or a storage unit that you will need to map to the existing data sources while integrating this software into your system.
And how exactly can data mapping help with the initiatives described above?
Each application in your IT infrastructure generates data, and these various data sources typically use unique structures or schemas.
Consider a scenario where a single data element from one structure corresponds to a combination of elements in another structure. For instance, a single ‘full_name’ field in one database might be equivalent to the combination of ‘given_name’ and ‘family_name’ fields in a different database.
Furthermore, there are situations where you may need to perform a mathematical calculation to align data fields. For example, to match the ‘expiration_date’ field in the destination structure, you might not find an exact counterpart in the source. Instead, you might calculate the ‘expiration_date’ by adding the ‘validity_period’ to the ‘production_date’.
By hiring data specialists, such as those from ITRex, you’re essentially bringing in experts who can align all the technology systems with different schemas. The result? Your operations run more smoothly, decisions are based on accurate and consistent information, and you avoid the costly misunderstandings that can occur when systems misinterpret each other’s data.
Data mapping techniques
In the table below, we have highlighted several methods for data mapping, as well as the benefits, drawbacks, and suitability of each approach.
| Manual data mapping | Semi-automated data mapping | Automated data mapping | |
| Description | In this approach, users match different data fields by hand without the assistance of any dedicated tools. | A hybrid approach that uses automated data matching with some manual intervention. A developer relies on an automated tool to create most of the mapping but still uses custom code and manual efforts to tackle the data fields that remain ambiguous for the tool. | The approach implies using an automated tool to create mappings. This approach doesn’t require coding knowledge, but users need to be familiar with the data mapping tool of their choice and understand the data they’re working with. Many automated data mapping tools, such as Intelligent Data Management Cloud and Tableau’s Prep Builder, have convenient drag-and-drop interfaces and extensive documentation to guide you through the data mapping process. |
| Benefits | You have full control over the process and the results. | This approach balances efficiency and flexibility. | Fast, scalable, and does not require coding skills. |
| Drawbacks | This process is time-consuming. And given the sheer volume of data in modern companies, manual data mapping is unlikely to be effective for large projects. | This data mapping process is time-consuming due to the manual efforts involved. Additionally, the use of data mapping tools will result in licensing fees. | Data mapping tools can be pricey. For example, the two tools mentioned above offer consumption-based pricing depending on the number of users and the license type. For instance, with the Tableau tools, you can purchase one viewer license for $15 but if you want to interact with the data and create your own dashboards, the price jumps to $75 per license. The users performing data mapping will likely need training, too. |
| Suitable for | Suitable for relatively small databases (metadata-wise) and one-time migration projects. | Repetitive mapping tasks on multiple databases, and handling custom (including legacy) data formats. | Large-scale data integration and software engineering projects, with multiple source systems involved and a large amount of metadata in the scope. |
Data mapping done right: an example from the ITRex portfolio
A digital health startup approached ITRex to extend the functionality of their 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 short answer is yes.
You can perform the data mapping process without any external consultation.
However, it requires a solid understanding of the business processes, the nature of the data collected, and how data mapping tools work (if you are planning on using any). So, you are likely to invest in specialized training and let your employees drop all the other tasks they have at the moment and focus exclusively on the mappings. And even then, the process is likely to take a long time.
What can inexperienced in-house staff miss?
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.
Data specialists, however, are a 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 means they can do the job quicker and also recommend improvements to your databases.
These changes can make everything run smoother and faster. For instance, your team might be familiar with how to map the data, but if the database responds slowly, it will slow down the related processes. Data experts consider the overall scope: they analyze your data and plan how it will integrate seamlessly into the organizational workflow. They can often anticipate possible issues and prevent them. This involves selecting the appropriate storage, ensuring that your data loads efficiently, designing indexes, and guaranteeing the optimal performance of your database in the future.
So, your data mapping initiatives will be more successful as a collaborative effort between 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 crucial data pieces that the client simply couldn’t find. They knew they stored it somewhere but couldn’t identify the storage unit. Our data experts used reverse engineering to understand the business logic and then applied formulas to calculate and aggregate the missing data.
Second, some data required for the reporting tool was simply not collected by the platform. We advised the client to make particular changes to their product to start gathering and cleaning up the missing information.
Data mapping steps
If you decided to do the mappings in house, here are five data mapping steps that will guide your team through the process:
- Step 1: Clearly define your target schema/outcome and identify what the target database will look like
- Step 2: Determine which data sources you want to use. This can include business operating systems, relational databases, data generated through APIs as CSV/JSON/XLSX files, and other formats. 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 that will help you get through the process.
- Standardize the naming conventions of the data fields and document them
- Consider using readily-available automated tools when possible and implementing scripts to minimize reliance on manual efforts, again, when possible
- Thoroughly document the data mapping process, procedures, and tool configurations (if these affect your data)
- Implement versioning of the mappings and all related artifacts so that you can roll back to previous versions if needed
- Classify data based on its sensitivity level and pay extra attention to protect sensitive data. Keep in mind that data mappings are created in order to be used in data processing, so marking certain fields as sensitive will help the development team process them safely in the future.
- Foster collaboration among data specialists, domain experts, analysts, and the legal team
ITRex as your data flow management partner
We are an experienced data management company that has helped many clients on their data journey. We will be happy to assist you with data processing, whether it’s a part of your digital transformation, data management initiatives, or integrating/building a new software product.
Also, drop us a line if you are not satisfied with your current reporting and analysis processes. We will help you transform your data to extract additional insights to support your business decision making.
Get in touch if you are looking for a reliable data management and modernization partner. We can also audit your data and restructure it to extract information for deeper business insights.
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