Big Data Analytic buzzwords; are they a way to fit in?
Reading a recent article in Forbes – ‘6 predictions for Big Data Analytics and Cognitive computing in 2016’ by Gil Press, I found myself nodding in agreement with salient points about ‘stagnating data lakes’ and the ‘talent crunch’ which, as a recruitment partner to IBM and other big data analytics leaders, is one of my team’s biggest challenges. But I had to smile when I read prediction #5 that ‘we will have a new buzzword’.
I don’t know about you, but I have been a sucker for buzzwords since starting my headhunting career in the days of E-business and Y2K. Nowadays buzzwords have got a bit more whacky.. my 7 year old daughter was very excited to hear her Daddy discussing a Unicorn company in Tower 42 with a prospective candidate, but rather disappointed when I explained this had nothing to do with a quest to find mythical creatures…on second thoughts, maybe it does.
Buzzwords, whether we love or hate them, serve an important purpose. They allow us to fit in and establish some credibility with clients and colleagues. Any Enterprise IT sales exec knows the importance of talking the language of their customers. New buzzwords and acronyms also allow us to quickly label things, be it new Tech or new Job titles (DSI IOT is not a countdown conundrum it’s a sought after job).
But what happens when we label people? Scouring CVs or social media profiles for buzzwords is effectively treating a person as a collection of Data (data which they choose to make publicly available I might add). This is nothing new and is a simple process which can be performed by a relatively inexperienced recruiter or by basic search software.
The differentiator for Big Data Analytics or ‘People Analytics’ that HR and Talent Acquisition Directors could get excited about is that people’s digital behaviour can now be analysed. For example updating a Linkedin profile, commenting on or liking an article, making new connections to people or groups, or even the time of the daily commute (easy to calculate when permission is given to share location with phone apps) can all point to a passive candidate’s willingness to move. This ‘intent’ plus a few key data points such as experience working for a certain company with a specific job title can provide a digital footprint that, in theory, would allow those algorithms to target the right person and, more crucially, at the right time.
Sounds like I’ve just written myself and every other specialist recruitment agent out of a job. So why are our services in greater demand than ever before?
There is the obvious answer both in-house recruiters and agencies would agree on that, once you have engaged passive talent with the right skills and intent, navigating them successfully through a recruitment process is a whole different ball game and I have yet to see anything come close to automating that. We could summarise this as candidate experience.
Then there is the specialist industry knowledge which recruiters take many years to develop. For example understanding the cultural that existed at a specific company at specific time (e.g. Xerox in the 80s, Oracle in the 90s, PTC in the 00s) and knowing where the people who thrived in those environments will fit in now.
What about the A star candidates that data analytics will overlook because either they haven’t subscribed to sharing their data publicly or they don’t tick the desired buzzword boxes.
The best software sales person I’ve ever worked with has a very simple Linkedin profile with no buzzwords whatsoever. Her occasional and carefully considered moves have not been obvious either, going from a rather technical sale of infrastructure software at a large corporate to a business applications sales role at a pre-IPO company. She has thrived in both environments both financially and in terms of career development.
Of course no one would think this possible unless they had an actually had a conversation with her and identified that talent in the first place. Which brings me on to my next and final point….people buy from people.
There is an argument that Big Data Analytics could in the future be used to develop highly sophisticated solutions to develop specialist industry knowledge, address candidate experience and identify less obvious talent fitment (psychometric testing is having its day again in this regard).
If people just need a job then a transactional recruitment process is fine and Big Data Analytics is perfectly positioned to make the right matches quickly.
However these high worth individuals with in demand skills aren’t just thinking about a job, they are thinking about their career. Most of these people will value a long term relationship with a recruiter usually because they know there are no shortcuts to developing trust. More importantly though, it is because everybody wants to be understood for their uniqueness.
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