Welcome to Talent Tech Labs Lookback. We spend a lot of time exploring the Talent Acquisition Ecosystem and Marketplace and while every TTL Trends Report focuses on a different theme, we occasionally like to look back at some of our greatest hits.
In Q2 of 2015, we published a fully redesigned and reorganized ecosystem infographic for the talent acquisition landscape, to reflect the constant evolution of the solutions being developed.
Big data turned out to be more than just a trend… it now is being put to use through AI. Take a look at our past Trends Report V3 from 2015 on big data to see how far we’ve come!
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Since then, we’ve updated the ecosystem quarterly. It is filled with innovation — but it is also confusing and dynamic. With our third iteration, we included highly specific and easy-to-understand macro categories and sub-verticals — that can help you better focus your investments and energies on the innovations and technologies that are most interesting and critical to your organization.
This ecosystem enhanced the graphic in its look, feel and use, with a new color coding schema that grouped offerings into verticals, and by providing clearer, easier-to-digest information at a glance.
We continued to add and restructure to best present critical information about companies and innovations. This includes areas that were previously over-categorized. We removed the category of “college recruiting” instead of moving those companies into other segments. As some job search organizer companies evolved, we re-categorized several companies for this iteration. Finally, we reviewed and recategorized CRM and Job Marketing & Distribution companies to better clarify the distinctions within the vertical sector.
Q2 2015 Ecosystem Throwback:
Download the latest version of the Talent Acquisition Technology ecosystem here.
More Than a Trend — Big Data is Poised to Make a Bigger Impact
Big data has achieved celebrity status of late as a harbinger of a more empirically-optimized future. The applications within talent acquisition are profound, most specifically focusing on better and faster ways of aligning job opportunities and job seekers. The previous wave in talent acquisition analytics has been about broadening the data set for visualization.
Looking forward, the next phase will be focused on gaining depth of understanding. Expect the next generation of data software to enable businesspeople to focus on the business meaning resulting from data and statistics rather than requiring them to be statisticians themselves. We highlighted these four trends:
1. Taxonomic conformity.
In human capital data, there is no universal standard that all organizations adhere to for master data standards (the labels we place on our data). While data elements like location have a universal standard of nomenclature, elements like job titles, industry, and source type are often a mixed bag within a single organization, let alone across multiple businesses.
Emerging human capital data platforms are addressing the issues of conformity head-on, by looking within requisition data elements, including job descriptions and resumes, and using natural language processing to more granularly and precisely define a taxonomy as seen in the actual data, with even greater detail.
These 4 trends should be on your radar as we await the next wave of data analytics software and solutions for talent acquisition:
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2. Highly filterable aggregated market analytics.
A core but often misapplied diagnostic method is to benchmark one’s own performance against that of other organizations. Determining which organizations and records to include in your data set for a given analysis is actually a more complicated issue than performing the analysis itself. Should you only include companies of the same size? Okay, but size measured how—revenue, employees, total workforce? The tools of conformity described above offer tremendous opportunity here.
Now, we can rethink the idea of trying to find data points that feel like “needles in a haystack” and instead focus on a highly fluid set of data that can be as broad or specific as you like and only containing needles. This also includes the ability to use data across multiple organizations—so you can find all the relevant data to your organization, depending on which parts of the market are most relevant to you under which circumstances.
3. Total workforce management.
Given the fluidity with which managers turn to employee and non-employee worker categories these days, it would be incorrect to exclude either from the other’s analysis in some way. If your employee headcount is remaining steady due to a hiring freeze but your contingent population is rising, is it accurate to say the hiring freeze is working? Total Talent Management (TTM) is an emerging perspective that looks at the collective components of a workforce through a single lens.
Accordingly, data science tools are critical to comparing the “when” and “how” of workforce mix optimization, and can offer guidance to end users directly within the instance of their raising a requisition. While these techniques can be applied to any aspect of human capital analytics, in talent acquisition, predictive analytics has predominantly centered on matching algorithms, matching candidates to requisitions (for optimal placement), requisitions to requisitions (for conformity of grouping as a “market” inference) and job descriptions to job titles (for automated classification). There are many techniques in predictive analytics and none are easy or universally work best. Use with caution until the methods and interpretations are more mature than the hype.
4. Tying in performance.
Performance and/or quality of hire data (of candidates, workers, or suppliers) is still where things are most immature. There are companies with methods of looking at the volume of work in certain fields (like programming) but it is an imperfect and indirect measure of performance or quality of worker. Without this, while we can look at a how long or difficult it is to bring in talent we will never know which were worth the additional time, effort and expense. It’s a big gap and one that will likely smack against privacy sentiments.
SEEMS HARD, WHY BOTHER?
Data science applications are in their infancy but are making great strides. As with computer technology before it, the next wave of analytical innovations are going to start removing the technical burden on business people to more directly provide the answers they have been seeking.
We hope you’ve enjoyed this TTL Lookback of our views on Big Data. To see our most current trends report and research, or to view the ecosystem today, visit our Think Tank. You can also sign up for our email list to get Talent Acquisition updates, industry insights, and research to help you make the right decisions in 2018!
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