Data Fusion - from Elite Intelligence Units to HR

LiranGabbay, CEO & Co-Founder, Stealth mode startup

Data Fusion – from Elite Intelligence Units to HRLiranGabbay, CEO & Co-Founder, Stealth mode startup

A lot has been discussed about the new HR-analytics era, rapidly conquering almost every technological aspect of talent acquisition, sourcing and onboarding processes. The good and the bad news are simple – this trend, as being showed these days - is about to come to an end:

Goodbye data analytics, hello data fusion.

In the past 4 years, multiple HR-Tech startups have been trying to assist organizations to manage, analyze and prioritize huge amounts of data, mostly originated from their legacy ATS (Applicant Tracking System). These startups offered companies to reduce time and efforts as their main value proposition. However, during these years a few major changes occurred and are yet to be properly addressed: the variety of relevant data sources has been dramatically increased; apply-to-hire periods of time are becoming shorter and shorter due to an incredible competition over talent, and HR marketing activities are quickly becoming a must-have for hyper growth companies.

"The intelligence fusion area has started almost a decade ago, and like any other successful workforce trend – it’s now coming to take over the HR-Tech scene"

To tackle these major trends, HR-Tech analytical products will have to transition from being an intelligent integrator between the ATS/HRIS systems and the outside world, into developing an internal data fusion magnet; which acts as a powerful recruiting growth engine, empowered by a holistic fusion of ATS, HRIS, Marketing and Sales data.

Sounds revolutionary? Well, here are some potential use-cases to demonstrate my thesis:

Funnel prioritization is no longer valid when compared only to job requirements – by utilizing HRIS data, applications must be matched to the exact hiring team human capital (and its strengthens and weaknesses), and to common grounds of top-preforming employees within the company.

Existing and potential customers can easily produce an attractive talent pipeline if marketing and sales CRM DB’s would be fused into the existing ATS. From candidates’ point of view, joining a new company looks highly attractive after using its main product for years.

Employer branding campaigns on social media channels are still being analyzed only from the brand awareness perspective, while ignoring a huge talent pool

Internal referrals are widely known as a key sourcing tool – but companies missing crucial data which could pinpoint the leading employees who not only excel in their role, but also holds an attractive network of potential talent funnel to target.

With that being said, companies at any scale now understands that HR KPI’s must include not only a quantitative aspect, but also qualitive one:

They are all looking to create a complete feedback loop.

Simply put, retention time is a solid factor to look back and mark a successful recruiting process, but it’s not enough. 2022’s HR analytical startups will have to start using AI based engines to provide companies with the ability to measure not only how long do new employees last in their role, but also how well did they preform over the years. With this kind of data being indexed and analyzed, organizations would gain the power to identify significant contributing factors to try and duplicate a successful hiring. That long-term retrospective analysis process, agnostic to any human power changes among talent acquisition teams over the time, could be the next X-factor.

As a former analyst in the Israeli elite intelligence unit 8200, I find multiple tangent lines that are common for both HR-Tech evolution over the years, as well as for the intelligence domain evolvement - the transition from raw data to information management, and from that to actionable insights. The intelligence fusion area has started almost a decade ago, and like any other successful workforce trend – it’s now coming to take over the HR-Tech scene.

Winter is coming, do you have enough data sources to be well prepared?

Weekly Brief

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