Plotting the Course on Data Mapping
There’s no question that data is a massive driver of business. Data can tell you all about your customer base, how effective your financial strategy is, and even help forecast your future successes. But data can come in quite a few different formats, and keeping all of those data points organized and ready for analysis can be a daunting task. That’s where data mapping comes in.
Data mapping is the process by which two or more data sets in different formats are organized into one coherent system. It can take all of your Excel spreadsheets, your CRM data, and all that legacy data you have stored in a previous system and standardize it. This can be done manually, but it’s far quicker and more reliable if you let the computers handle it. Here are the basic steps that you should take when utilizing data mapping for your business.
Start With a Plan
The first step is to decide what you want your finished product to look like. This is where you will build your machine learning infrastructure which is the system by which you’ll organize your data. At this point, you will choose if you want to map your data by hand, use a semi-automated system, or opt for full automation. Each option has its benefits and drawbacks, so explore them carefully to make the best decision for your team. When it comes to saving time and effort, a semi-automated or fully automated system may be your new best friend.
Identify Your Source Data
Next, identify your existing data. This is also called your source data. It is all of the data that your business holds that you want to include in the final format, which is also called your target data. List everything out, including where the data is currently stored. Your machine learning program will start pulling from these places in the next step.
Plot The Connections
Your third step is to take a look at the fields in your source data and determine how they fit with the fields in your target data. For example, if your source data lists a customer’s name as “NAME” in one system and “CUSTNAME” in another, you’ll want to make sure those both end up mapped to the same “CustomerName” field in your target data. Utilizing a data mapping template will help your team visualize all of these connections.
This step is where the biggest differences between the three mapping methods become apparent. If you’re using hand mapping, your team will list out all of the connections and manually make them. A semi-automated system will select the connections on its own, and your team will then review those suggestions to make sure they’re correct. A fully automated system goes a step further by removing the quality control checks. These systems typically use drag-and-drop interfaces to let users create their own maps at the start.
Reap The Rewards
Once you’ve planned out your process, collected your source data, and determined which method to use to map it to your target data, it’s time to integrate. If you’re using a semi-automated or fully automated system, that also means that it’s time to let the machines do what they do best. Machine learning models are built into these systems. As you set up the maps in the previous step, you were training those models on how to handle the real source data. With all of that in place, you just have to flip the switch.
Utilizing data mapping to integrate your source data with your target data will streamline your business’s workflows and can dramatically increase your efficiency. It can also give your predictive modeling (such as financial forecasting) more power through the use of legacy data. Machine learning technology advances have turned this process from a long, slow, and tedious effort into one that can be managed quickly, simply, and effectively. Why not take advantage of this and let the machines do the work for you?
