A data lake is a repository that holds data in its rawest form, with little processing or analysis. For recruiters, it is a useful tool that can help manage and organize their talent data in one concentrated area so they can access as and where needed. It also helps organizations utilize AI and machine learning to optimize their recruiting capabilities. Before transferring your data to a lake, however, there are several questions you should consider. These questions involve examining the type of talent data you currently possess, what types of data you plan to have in your data lake, and if you have a platform provider who can help you get the most out of your talent data. Asking yourself these questions about the platform you've chosen for your data lake will make your management of it much more efficient post-implementation.
Build a comprehensive list of data sources you plan to input into the data lake. Include all in-house corporate systems, social media, and any other applicable websites and systems - even those resumes and spreadsheets lying around can be on the list. This is a necessity to ensure none of your data is lost in the transition. Incomplete data transfer can lead to confusion later when accessing talent information. That being said, one of the great things about a talent data lake is that you can change your mind later with no trouble! So, while it is best to add as much data as you can up front, there's no pressure - if your list of systems changes, or you discover new data sources, you can simply add them later.
What is the size of your talent base? Exactly how many internal candidates have you had in recent years? How many are in your CRM or RMS? What about candidates you identified for a past opportunity with whom you did not proceed but would like to track? Data lakes can handle vast data volumes and all kinds of data structures, so you can really cast a wide net here. No more reducing the scale of your project so there's less data!
Managing your data lake is easier when you fully comprehend the types of data you input at the start. Ensure you and your team know what data you're including as part of the implementation process. This ensures that no one accessing the data lake will have misguided expectations on what types of data for which to look later. If the data sets you're implementing are complex, you'll want to choose a platform that can use AI and machine learning to interpret them.
As time passes, some of your talent data will change and require updating. Internal and external candidates take new jobs and acquire new skill sets. The most effective recruiting systems are able to stay on top of their talent updates so they can surface the right talent for recruiters. This requires a data management program that can help you do that.
As you transition to a data lake, do any of your talent records include outdated information? Are you choosing a data lake platform that will require manual maintenance on your part or is the platform able to update this data quickly?
A good talent data management platform will accommodate data collected from a diverse set of systems and platforms. What systems can your platform support? Understand this at the outset of the implementation process to ensure your platform can manage all your data once the transition is complete. And be sure that it will adapt automatically as your data needs change.
The previous questions involve a considerable amount of deliberation prior to implementing your data lake. It leads you to another final question: wouldn't it be easier to partner with a proven provider of data management services who can help guide you through this entire process?
Teaming with SwoopTalent to input your talent data into a data lake frees up your time, energy and resources to focus on strategic planning and other tasks critical to your operation's health and success. We offer a diverse suite of talent data management solutions. For more on how we can help you fulfill all your talent data needs, contact us today!
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