Learning a new language leaves room for countless misunderstandings, like the Spanish student who thinks “Yo soy embarazada” means “I’m embarrassed.” (It actually means, “I’m pregnant.”) Every new language has its own rules, and even within a language, each dialect has its own shades of meaning that make translation difficult.
But believe it or not, this isn’t a problem that’s uniquely human. The digital world has a rich variety of languages that can lead to misunderstandings, especially when data needs to cross borders from one application to another.
Fortunately, there’s a process known as “data mapping” that functions like a translator to ensure your databases can more efficiently communicate with each other.
Data mapping 101: What is data mapping?
Data mapping is the process through which you take one set of data (known as the “source”) and assign or “map” its destination (known as the “target”). The goal is to make your organization’s data more structured, cohesive, and accessible to your team or customers.
For example, imagine you’re collecting customer data from desktop, mobile, and your servers. This data can be used for many different purposes, such as paid advertising, email marketing, push notifications, and more.
The only problem? Many of these platforms speak a unique language when it comes to data. This can make it difficult to use any piece of information to its full potential. But data mapping acts as a translator to bridge that gap, so your data can be seamlessly migrated, integrated, or transformed from its source to a destination.
Again, data mapping is simply a means to an end that helps your data communicate. Even when you use a centralized system, like a data lake or data warehouse, these global data structures still have unique languages that need to be understood if you want to export data from or import data into that system.
How data mapping fits into your broader data strategy framework
Data mapping has many use cases, and it doesn’t have an inherent end goal. Instead, data mapping is the first step to running a variety of data-related processes, including:
Data Integration: Bringing all your data into a centralized location and normalizing two different sets of data into a single stream. Think about a marketing and sales team combining their lists of leads with contact information. Data integration would take both data sets, remove duplicate information, and format the data in a cohesive way.
Data Migration: Moving data from one location (storage type, format, or IT system) to a similar but structurally different location. One of the most common types of data migration for modern businesses involves moving their data from an on-premises data center to a cloud platform (like AWS or Azure).
Data Transformation: Translating unstructured (or misstructured) data from one format to another. The most common example of this would be converting data from an XML to a CSV file.
3 data mapping techniques
Automated data mapping requires specialized software that will take new data and match it to your existing structure or schema. These tools often rely on machine learning to consistently improve/monitor your data models. There are many advantages to automating data mapping, including:
Pulling data seamlessly from hundreds or thousands of inputs
Allowing non-technical staff to run complex data processes with a user-friendly UI
Seeing your data flow represented with engaging visuals
Receiving notifications when issues arise
Troubleshooting those issues for targeted repairs
While some companies are hesitant to invest in this kind of software, the right data mapping tool will save you countless hours in labor, meetings, training sessions, developers, and more. It’s why companies like Retool used Segment to automate their data management as they scaled.
Not only did Retool massively grow and switch data warehouses, but they also went from 7 to 100 employees, implemented a new marketing and CRM system, and changed business intelligence (BI) tools. They were able to accomplish all of that and more by changing 5–10 lines of code with Segment.
Semi-automated data mapping
Semi-automated data mapping (also known as “schema mapping”) is a hybrid process between fully automated and manual data mapping. Developers work with software that specifically creates connections between different sources and their targets.
Once the process has been mapped, someone from your team will manually check the system and make any necessary changes. This is a good strategy when working with small amounts of data for basic integrations, migrations, or transformations, especially for smaller teams on a limited budget.
Manual data mapping requires a developer who can code rules to transfer or inject data from one source field to another. It’s becoming increasingly harder to create a reliable data management strategy without the support of automated software due to the sheer amount of data available to modern businesses. Instead, manual data mapping is a good solution for a one-time process (like data warehousing, for example) when the database isn’t too large.
The data mapping process in 5 steps
1. Identify all data fields that must be mapped
The first step in data mapping is to determine which data needs to be moved or restructured. Unfortunately, there’s not a “one-size-fits-all” recipe. Everything will depend on what you want to accomplish with your data mapping:
Integration: Look at each of your data sources to see how much information needs to be combined, how many sources they come from, and how often your integrations will take place. Large and frequent integrations are an indication that you need an automated tool. One-time projects with limited data can likely be done manually.
Migration: Look at the source data and define what you need in the target location. Again, the amount of data you’re working with will dictate the approach you take: the more data involved, the more helpful an automated software will be in the migration.
Transformation: Look at your data source and identify which format you want for your target destination. Most modern transformations will need automated tooling, but smaller projects can possibly be performed manually.
2. Standardize naming conventions across sources
Identify the format of the data in each of your data sources and define a format/structure for the target data.
For example, imagine you were integrating data from your marketing team’s email list into your sales team’s contact list. Marketing records the date as MM/DD/YYYY, but sales records it as DD/MM/YY.
You would need to determine the format you want this data to have when it reaches its target (in this case, the sales team’s list).
3. Create data transformation rules and schema logic
This step will heavily depend on how you’re mapping your data:
Automated: Drag-and-drop UIs do all the heavy lifting for you. With the right data mapper, even non-technical employees can map out complex data in minutes with no coding required.
Semi-automated: Use your software to create connections between your data sources and their target destination. Then have an experienced developer or data scientist manually check that these connections are working correctly.
Manual: Hire an experienced software engineer to hard code rules or schemas that map your data sources to their targets.
4. Test your logic
Move a small sample of the data that you’ve mapped and manually check for any errors. This is to ensure your data quality remains as high as possible. If you’re using automated data mapping software, then validation is simple since these tools often have built-in verifications and real-time alerts. Still, you may want to manually check a small batch of data in your migration, integration, or transformation to ensure your software is working as promised.
If you’re mapping data manually, you’ll want a highly experienced developer or data scientist to ensure everything is working correctly.
5. Complete the migration, integration, or transformation
When you’ve tested your logic, you can complete your migration, integration, or transformation. The difficulty of the overall process will depend on what end result you’re trying to achieve and what tools you’re using to accomplish that goal.
Automate and simplify data collection, migration, and integration with Segment
With small amounts of data that require a one-time migration, integration, or transformation, you can likely map out your data manually. But for larger, more complex projects, you’ll want to manage your workflow with the help of a customer data management platform like Segment.
In fact, Segment is how the company Smarttbot was able to capture 20% of its annual sales in one week while saving an entire week’s worth of work in the process. They did so by using Segment to build a best-in-class stack through a single API. This unlocked their ability to collect and connect data to growth-focused tools like Amplitude, Facebook Ads, Zendesk, and many others.
Learn more about how Segment can automate your data management strategy to save you time and money as you build smarter data-driven strategies.