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Today, talent data is a key asset for all employers. Your strategy, decision making and the engagement and productivity of your workforce depend on it. This is why most organizations work so hard on collecting and storing it. They are always trying to improve the quality and effectiveness of their talent data.
Yet, if your talent data exists in various systems, it probably also has different values that mean the same thing, but have different labels. This is where data mapping comes in. Read on to find out why you need to use data-mapping to better manage and take advantage of your talent data.
By now, you know why Talent data is essential. You know that it is imperative to the success of your talent strategy. This is because it improves many areas of your talent cycle and informs you about your workforce.
Complications can arise when your talent data is disparately managed across various systems. You create talent data in your applicant tracking system, your talent management systems, HRIS, and many others. This means you have lots of data to use, but they're all in different locations and in varying forms.
As a result, it will be difficult to have a single view. Without this single view of the data, your organization will fail to leverage the real benefits of talent data. For instance, analysis of data will be difficult to do. As such, you need to integrate systems and transform or migrate data. Here's where you use data mapping.
In short, data mapping is the exercise of creating relationships between distinct models of data from different sources. In other words, it is the practice of drawing out data fields from various sources. Then, connecting them to their associated targets.
For example, in your ATS or CRM a job might be called "Lead Financial Accountant", but in your HRMS it's called "Accountant 4". You used a different label in the ATS to translate it to market language, but you need to make sure your analyses know they are the same thing. So you map it.
As your volumes of data increase, you’ll start using machine learning (including anomaly detection) to manage and monitor your data and your data mappings inside the data lake. This means long term maintenance is much less than you are used to!
Data mapping plays a crucial role in data integration tasks. The success of integrations, however, rests on the fact that the data model of the source and target are the same, or they at least know what the equivalent across the systems is. Unfortunately, it is not common for different systems to have the same models. Here you have two options.
The option we recommend is that you manage your data in a data lake. This makes data mapping more powerful and flexible, and also makes it easier. It means data is already curated before it goes into a new system or a warehouse.
Your second option is to hard code mappings in your integrations. That means predefining every single one and needing to recode if you have any changes at all. Ouch.
Since your data comes in all shapes and sizes, it will often need to go through data transformation. At SwoopTalent (as with most data lakes), we do transformations differently. We believe that you should keep the original versions of all your data.
Any transformations and normalization are additions to your dataset. This allows you to have both the source and target available. It also means you can adjust, update and fix any mappings at any time in the future.
When you're migrating data, you'll have to build mappings between the source of the data and its target. Historically it has been standard to convert the data and only keep the original in audit trails. With the use of data lakes, though, that is no longer necessary. You can easily store both the original and converted versions. So you aren’t forever trapped (or penalized) by choices you make during your project.
At SwoopTalent, we can assist you in ensuring that your systems understand each other. With data mapping, transformations, and automation, we provide you with the tools needed. You will have the power to manage data and processes with ease.
Data mapping is a way of showing what values mean in different contexts (apples and apples, even if one system calls them pink ladies and granny smiths).
New technologies make data mapping much less onerous and risky than it was in the past. For example, new methods allow you to keep data in its original form. As such, you can make corrections and adapt as your circumstances change. In the past, this was not possible.
Make optimal use of your talent data by using leading tools and methods to help with data mapping. By doing this, you'll get the highest value from your data and the lowest cost for your projects. This is because you will have complete control of it, being able to move it where you need it and change it into any form.
As a result, your analysis will come easily, and you can draw insights without much effort. From this, as you can imagine, decision-making and strategy will be better than ever.
Don't let the challenges of talent data put you off from using it to improve your strategies. With SwoopTalent, you don't have to worry about the challenges at all. We take care of your talent data with our talent data platform and assist with any data mapping tasks you have too.
Together, your talent data and our data mapping capabilities will take you to new heights. Contact SwoopTalent for more information today!