The headhunting business has changed dramatically over the past couple of decades. The view was that the all-powerful and much sought after head hunters’ ‘black book’ – essentially a list of their best contacts – defined the industry. People wanted to be both in it and know who else was there.

And then in 2003 (yes, it was that long ago) Reid Hoffan came along and threw open those pages for everyone to see. For the first time the world could take a peek at some of those coveted details. LinkedIn had been born and those that wanted to could now see inside the book.

At first, the executive search market resisted its power: it would not hit ‘their’ market, it was for the ‘aspirational set’ not the senior directors that they typically dealt with. We all know what happened. LinkedIn now has more than 300 million users, there are now over one billion LinkedIn endorsements, more than 40 per cent of LinkedIn visits are now via mobile, and there are well over two million LinkedIn groups.

Yet, interestingly, despite these statistics, LinkedIn’s detractors continue to argue and find fault: LinkedIn has to be verified, LinkedIn costs, LinkedIn only covers a certain percentage of the market, lots of senior people do not want to be bombarded by another way of being contacted, LinkedIn lets you see who it wants you to see and not always who you are after. And so on. 

The debate will no doubt continue but we cannot limit the changing forces surrounding the executive search industry to just LinkedIn. We have to see the bigger picture.

LinkedIn uses semi–structured data that users give away without a second thought: where they live, the industry they work in, geographies, endorsements, skills, when they changed jobs or cities, it enables them to see trends on a vast scale and target data/jobs accordingly just like Amazon pushes suggestions for your next purchase.

Machine learning

We have also seen the encroaching impact of cognitive analytics which uses the human brain as its model; how we process information, deduce and most importantly learn is starting to take a lead role in these conversations.

Machine learning (also known as data mining, pattern recognition and predictive analytics), natural language processing (used already with Siri and Cortana) which enables the parsing, the understanding of unstructured data (think handwriting, voice commands/ transcripts, mobile data points, email, tweets, blogs, posts, images etc…) and a whole lot more capability in terms of storage and processing. What this might mean takes us way beyond LinkedIn’s email of jobs to you that seem to match your profile.

For example, take a look at the rise of wearable technology  and we start to see another set of data points that can feed into the senior appointment process. Your health data may not be only useful to your insurance company; your actual employer may find this of value. It could be just the point of data that it uses in its human resources assessment such as analytics that highlight talent pipeline issues, diversity mapping and performance management. GPS is already being used in the supply chain so there is natural progression in monitoring employees at all times: we can monitor what they are working on, who they are talking to and messaging and match this against set KPIs, to improve training.

The data that we need to ensure that you can pass your due diligence checks could alter massively. Where appropriate, there is already in place various background checks e.g. criminality, financial probity, your internet footprint, etc… but move beyond the blogs and the tweets and think about how comfortable you would be for your search history to be fed into the interview process too? Imagine when we can combine this health data, your search history along with various language/ profile analysis tools and apparently ‘know’ the candidate both inside and out.

The privacy backlash

Never mind the privacy backlash that might ensue, as big data starts to affect everything we do, we need to shout again that what we offer is more than dehumanised data points no matter how many patterns are recognised. Don’t get me wrong, Amazon, Netflix et al can tell you what most people with similar profiles bought but most people with similar profiles are not me and the nudge towards certain products and films, although sometimes useful, can actually result in me compromising on my original search. My options actually feel limited rather than expanded upon because I am being pushed to follow the pattern rather than towards a fuller search and sometimes even to question what I seek.

The impact of big data

LinkedIn disrupted the black book and at that point it became paramount that we spell out to our clients that it is not profile of individuals that they are buying, but that we work hard to understand and follow their market so we can offer the best advice. We know our candidates not just the data on their CV. And now in the face of the next stages of big data, we must again reaffirm that we work hard as a team together because – to use the old cliché – people will always be people.We will obviously take full advantage of what technology can offer us but it is our role to challenge the assumptions so we can avoid the obvious, move beyond the patterns and provide an independent voice, a reflective space and most importantly a creative leap so only the most well considered outcome is delivered.

Meg Myers

Meg is responsible for reviewing, developing and implementing an overall knowledge management strategy for the company and the management of the various information teams at Odgers Berndtson. Meg j...



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