The Next Big (Data) Thing: Awaiting the Robo-Revolution

As the quest for the shiniest silver bullets in recruitment continues the fields of artificial intelligence, machine learning and “Big Data” are proving to be a great hunting ground for salesmen and sensationalists alike. To the trained eye these fields are distinct and separate, yet when selling a solution that makes a claim in one of these areas there’s a wealth of content that shows us that our industry is capable of misunderstanding them all equally.  Whilst the misrepresentation of technology by those that are selling it is nothing new in the recruitment industry the coverall terms of A.I and “Big Data” have become imbued with a special power.  Part of this magical thinking has increasingly led to loftier and loftier claims for technology that, though still in their infancy, can be skewed for more clickable headlines.

In journalism there’s an eponymous “Law” for this kind of headline. “Betteridge’s Law of headlines” is an adage that states: “Any headline that ends in a question mark can be answered by the word no”.  It is intended to be humorous but seems to work in the overwhelming majority of examples. “Is This the True Face of Britain’s Young?” Sensible reader: No. “Have We Found the Cure for AIDS?” No; or you wouldn’t have put the question mark in. “Does This Map Provide the Key for Peace?” Probably not.  I propose a similar adage for content covering technology and its application to recruitment and HR.  Betteridge’s Law still applies in this area for example “Does AI Mean the end of Graduate Recruitment?“. Nope, it doesn’t.  What about those headlines that aren’t posed as questions? For those I propose Ward’s Law “The more often an article about recruitment uses terms associated with A.I., Machine Learning, and ‘Big Data’, the more likely the results of any study quoted will overstate the efficacy of the technology“. In other words, the more often recruitment is thought of as being “solved”, the further that will be from the truth.

Let’s look at an example.  Recently I saw this headline “Big Data research predicts which CV’s will be invited to interview by recruiters”, which sounds fantastic! It continues with “New research has discovered a way of telling which CV’s are most likely to be picked out from a large pile of job applications by recruiters.”  This type of press release has become formulaic. Characterised by a claim to the potency of the algorithm, some slightly spurious statistics that don’t entirely hold up to further research or misrepresent the original intent of the research, make some other claims where the algorithm hasn’t been tested but would be an amazing disruption “…make it possible to predict a candidate’s future performance simply by scanning their uploaded CV..”, and ending with a sooth-saying doomsday quote from someone in the industry – “In the future we’ll all be fed by tubes and robot overlords will tell us what jobs to do”.

In this example we are given the sample size a “staggering 441,769 CV’s” and the percentage accuracy of 70-80% when graded against human recruiters screening the same 441,769 CVs. That means that in 88,354 to 132,531 cases the algorithm disagreed with the human recruiters and rejected the candidate. That’s quite a number of false positives/negatives, even more so for any company that values a candidate’s experience and values how applicants might be treated in their processes.  Where this element of humanity breaks down even further is that when given another source of data – a cover letter – the algorithm performs worse, the strike rate falling to 69%.  How many of those 132,531 the algorithm did not invite to interview went on to be hired? We’re not told.  The other aspect of this any many similar stories to consider is that humans aren’t great at dealing with large numbers.The reason for this is that our sense of number is based upon two innate systems which essentially deal with small numbers accurately or large numbers only approximately.  We don’t often encounter large numbers, so when we do, it can be easy to struggle to know if that number is statistically significant.  LinkedIn boasts 433 million members and Facebook has 1.65 billion monthly active users but at this scale those numbers are almost meaningless when applied to the hiring goals of one company. Our inability to connect large datasets with real people is rampant. Big numbers dehumanise us, and the bigger the numbers, the worse the effect. If these raw numbers alone aren’t enough for a little doubt to be cast we can look to those elements that the decisions are being made upon.

Whenever I read about a potentially revolutionary algorithm I’m always keen to understand how it is arriving at its results. In particular in these screening algorithms, what is the programmer choosing to include, what do they exclude and what weighting are they giving those elements on which they are basing those decisions? In this example experience, workplace and education are all measured. We’re also told that “Contextual factors were also taken into consideration, such as ‘did the candidate apply in time’ and ‘was the candidate already employed by the company?’”.  Then potentially more problematically, as alluded to in the full version of the PhD thesis this press release is taken from, demographic factors like “age, gender, nationality, marital status, and distance from the hiring companies” are also included.

This post is to comment on the representation of emerging technology and its application to recruitment, and it’s not my intention to speculate on a possible future of robots replacing humans, but there’s an algorithmic future that’s being neatly swept under the carpet by those who are “pro-robot”.  Research from Harvard University found that ads for arrest records were significantly more likely to show up on searches for distinctively black names or a historically black fraternity.  Research from the University of Washington found that a Google Images search for “C.E.O.” produced 11 percent women, even though 27 percent of United States chief executives are women. (On a recent search, the first picture of a woman to appear, on the second page, was the C.E.O. Barbie doll.)  Google’s AdWords system showed an ad for high-income jobs to men much more often than it showed the ad to women, a new study by Carnegie Mellon University researchers found.  Those who advocate a perfect future will have to confront this research and much more like it.  Whilst it’s often cited by overzealous salespeople that algorithms based on data are free from bias, software is not free of human influence. Algorithms are written and maintained by people, and machine learning algorithms adjust what they do based on people’s behaviour.  All this is even before a well meaning but industry-novice programmer opts to include factors like “age, gender, nationality and marital status” which are explicitly protected in discrimination law. Would an organisation deploying such an algorithm to sift candidates have to expose how the selection was arrived at?  Would candidates still be afforded the same protections?

The problem here is that programmatically applying a simplistic model doesn’t allow for any degree of nuance, and when we’re seeking to measure humans, nuance is everything. Sorting and ranking algorithms for stock in a warehouse have a great advantage over those that seek to catalogue people, and books on a shelf or a can of baked beans in a supermarket don’t have the free will to opt out of the process at any time, but humans do.  Historically, humans have opted out of over-automated processes. I remember a UK bank luring customers back to the fold with the “promise of no automated call centres” and several websites offer the opportunity to “talk to a real person”.  For those companies unwilling to interact at these early stages there may come a time of reckoning when candidates opt instead for a more human process, and not to become a human to be processed.

So how did we get here?  Why is it that the future is either an electric nirvana or a desolate dystopia? Like a lot of science reporting in the media the rise of technology is held up as a scare story, a robo-bogeyman to frighten HR.  Uniquely in the world of HR and recruitment a wealth of the content on the rise of technology is written by those who are selling the products. We have a discourse owned by the vendors, and an audience that doesn’t want to, or hasn’t invested the time to learn about the tech.  It’s no wonder that somewhere in the middle of all this, there is misunderstanding, acceptance and skepticism, and for some money to be made in this new wild west frontier.  For the rest of us there is plenty of content filled with wild claims and spurious statistics, you might not agree with the findings of studies of the claims or the vendors, but I’m sure someone somewhere is ready to tell you that “60% of the time, it works every time”.

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Data and HR – Numbers are Nothing Without Insight

Managing employees and an HR function requires a more holistic approach than exploring issues from the surface.

People use statistics as a drunk uses a lamppost – for support rather than illumination. – E. Houseman (1903)

Our online activity has meant a ubiquitous lens is shone on our lives where the accessibility of data leaves leaders believing every metric can be measured, compared and leveraged, contributing to a curious new world of seemingly ‘crystal clarity’ that was seldom thought possible. The problem, however, arises when data collection occurs without the insight required to take contextual influences into account. Leading the way for deeply consequential misinterpretation and misjudgement to arise. This ubiquitous lens, unless harnessed correctly, stands to confuse as much as it will clarify.

For me, context is the cornerstone of moving from data to insight in a human way, which will clarify, rather than confuse. Nowhere is this more important than in the HR function, where the source of data; humans, operates in a fashion that led author Phillip Lieberman to label us as the “unpredictable species.”

This is best illustrated by highlighting a data source that is predictable, like books in a bookstore. For the bookstore, doing inventory is easy. The metrics needed are things like how many books are on the shelves and how many have been bought. Once a book is bought, there are no external factors influencing its behaviour not to be removed from that shelf. The book doesn’t decide that its commute is too long, or that its wife no longer wants it to work at its company, or that its pay packet isn’t satisfactory. Thus the likelihood of it leaving the bookstore, (unless I am seriously overlooking something) for any reason other than it being bought or stolen is highly unlikely. This is because the external factors surrounding it are predictable.

Humans on the other hand are a different story. Aristotle wrote that we are “rational animals” pursuing knowledge for its own sake. We live by art and reasoning he said. Whilst I’m sure many HR professionals would love to see their employee population be as predictable as books on bookshelves, myself included, the multiple external factors that make humankind human are unlikely to change soon. Which makes producing blanket metrics and chasing large numbers such a dangerous game, because in doing so, the individual is naturally rejected. It’s like saying “we have increased our likes on Facebook which means that we are doing better as a company.”

All that we get from this is a top line number. What do you actually mean by this? What does this equate to? If these questions aren’t followed up, then where does the return on investment come from? Is this useless? This problem becomes even more salient when you consider that HR systems for the most part aren’t built in ways that enable contextual data to be factored in. Nor will a majority HR systems allow data to be exported for further analysis, leaving you stuck analysing the top line, leaving retroactive approaches to remain.

Until contextual factors can be included within HR systems and data analysis, I personally believe HR isn’t critically equipped to have a discussion around data at all. Instead of building a data set from the ground up specifically focussing on important questions we want answers to alongside the external factors that influence them, we tend to take a retroactive approach by attempting to find data to support new questions. The same applies for the big data phenomenon (which by the way, unless you are Facebook or some other such platform are unlikely to truly have a big data set). Gartner’s definition of big data is “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.” Playing with data sets of this nature without taking into account the contextual factors that directly influence decision-making processes as they happen will ultimately lead to skewed results and misinterpretation.

So my advice to those individuals taking the data approach is simple: ensure you keep the human in human resources. I am not ruling out this approach whatsoever, however ensuring HR professionals take a step back is key, especially if we are to glean the results required that are free of bias.

“You’ve got to have an Algorithm!” A Recruiter’s Guide to the A-word

Even if you only have a casual interest in the world of hiring it’s not long before you’ll encounter someone touting a magic bullet, a panacea for the “broken” world of recruitment globally.  Currently riding high on the buzzword bingo cards of these self-styled saviours is the humble “algorithm”.  This isn’t the algorithm you might know from maths or computer science however, the “algorithms” of recruitment are like a sorcerer’s incantations able to transform the world and imbue those that utter the word with sage like prowess and a cloak woven from the finest of Thought Leader hair…

In other disciplines the word algorithm is well defined (a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer), yet despite this there is a shift in understanding when we talk about algorithms for recruitment.  Because the discourse of recruitment is largely owned by those who wish to sell to practitioners the humble algorithm has become the magic in the box that’s too difficult to explain.  Algorithms are the elves who make the shoes whilst the shoemaker sleeps.  It’s because of this lack of understanding that each new tool that’s presented to HR, from personality quizzes based on dubious pseudoscience to true recommendation engines and all touted as having an algorithm with little or no distinction.

For the ease of understanding an algorithm is a set of well-defined instructions for carrying out a particular task. It must be sound and complete. That means it must give you the correct answer and it must work for all cases.  Usually, an algorithm is predictable, deterministic, and not subject to chance. An algorithm tells you how to go from point A to point B with no detours, it doesn’t stop to look at the flowers or to consider other factors outside of it’s available data along the way.

Does that sound like a recruitment process to you?  I’d struggle to find a recruitment process that supplies a “correct” outcome, and the notions of sound and complete aren’t interchangeable between organisations – what makes a developer a great fit for one company might be less relevant for yours. Recruitment processes are subject to chance and to that perfect serendipity of the right person available at the right time.  When a recruitment process is good for both the company and the candidate it bears the unique fingerprint of the culture of the company that created it.

All this must be very disappointing for those fans of buzzwords but fear not! Here’s a new one for you! Whilst I don’t believe in an algorithmic approach outside of tools that aid human efficiency there is a way to describe the recruitment process and still get the reflected glory of using a lovely big word.

H-Bomb

heuristic is a technique that helps you look for an answer. Its results are subject to chance because a heuristic tells you only how to look, not what to find. It doesn’t tell you how to get directly from point A to point B; it might not even know where point A and point B are. Heuristics are a practical methodology not guaranteed to be optimal or perfect, but sufficient to accomplish the goals required.  Examples of the use of this method include using a rule of thumb, an educated guess, an intuitive judgment, stereotyping, profiling, or common sense.  In effect, a heuristic is an algorithm in a clown suit. It’s less predictable and it’s more fun.

There are many reasons that the application of technology can make recruitment so much better for all involved but the miscalling of the effects of these improvements won’t help the industry or recruitment as a discipline.  The exaggeration of the effects, range and successes of the “algorithms” is hyperbole at best and at worst a thin veneer, attempting to add shine to the same old business practices shunted online.  The majority of recruitment success stories that herald algorithms as earth-shattering go on to describe a single section of the overall recruitment process being automated not the utopian future in which we are awarded jobs by are robot overlords as they seem to suggest.

Remember that a first round online screen or adding an automated stage into an existing process is less advanced and has less effect than the introduction of the water frames and power looms of the Industrial Revolution, despite what the hyperbolic headlines will tell you.

In the blind solutionism of the HR Tech vendors and those who seek to build personal profile for their technical leaps-that-aren’t, there are real dangers.  Not least of all at risk is the candidate experience, the diversity of our organisations and the objectivity to think critically to improve these tools further.  That’s a high price to pay for the acclaim of an attention grabbing headline.

There are a great number of fantastic HR Tech tools and new ones are arriving all the time.  It is the skill of a modern recruiter to know when to utilise which tool, at what time, to have the maximum beneficial effect.  In seeking to replicate the processes of others or glorify ourselves for our own successes we aren’t embracing a bold new technological stance we’re contributing to the “broken” world we look down on.

Distopia

Podcast – Tech Recruitment and Why Recruitment Algorithms Don’t Work – with Matt Alder

Another podcast, this time with the excellent Matt Alder.  Matt is compiling a series of excellent perspectives on recruiting called Recruiting Future, you can listen to the whole series here.

To listen to me talk about Startups, scale-ups and sledgehammers hit play below…

[c5ab_audio c5_helper_title=”” c5_title=”” url=”http://hwcdn.libsyn.com/p/f/e/8/fe813528ab2ad306/Recruiting_Future_Ep_11.mp3?c_id=8929389&expiration=1430861284&hwt=cfef33ab1a8601d67e9927b50bed7764″ ]

How to be Happy – Time to call off the “Engagement”

“Yes that’s right, we had a one hundred percent response rate to the survey.”

The speaker from a international airline proudly stated his results. His lovingly compiled pie charts labelled “Engagement” were resplendent in the Power Point behind him.  The audience were incredulous, a one hundred percent response rate how was it possible they asked?

“…well, of course, we made the survey mandatory”.

Whilst there are many different definitions of “Engagement” in the HR world broadly speaking and employee’s commitment to and involvement with their work and their organisation seems a decent place to start.  Like most HR initiatives the will to measure engagement starts out as well intentioned and a move towards concern for the employees themselves.  However, too often, in the application and the building of processes around this the notion of “Are our employees OK?” has been replaced with “Are our employees productive?”.  The change is a subtle one and has occurred as the results of surveys have been used as the answers to questions for which they weren’t intended.  Engagement, when measured effectively can be broken down into constituent parts.

  • Intellectual engagement – Does the person feel challenged in their work, are they thinking.
  • Emotional engagement – Does the person feel positive about doing a good job, is there an efficient emotional reward structure in place.
  • Social engagement – Does the role facilitate positive interaction with their co-workers.

When used incorrectly they are answering different questions.

  • Productivity – Is the employee producing the requited amount of labour that the company expects
  • Performance – Findings of surveys here are used as a proxy to explain results.

The shift is small and goes beyond just the semantic. For one set of employers “engagement” is now about measuring what we can “get out” of a workforce.  Like many other terms the human aspect has become distanced and now the people in the organisation can be reduced to a resource, counted and catalogued accordingly.  Even the manner in which these surveys are conducted can affect the results.  An annual engagement survey sent to everyone in the organisation is little more than the pointed stick poking at the bloated corpse of your organisation’s apathy.  Everything they measure has already happened, it’s post-mortem and the changes it would be possible to make are already too late.  A few click boxes on a website once a year is a chore not a meaningful interaction, regardless of the best intentions those questions are compiled with.

Some forward thinking companies have found a better solution.  In 2003, Fred Reichheld, a partner at Bain & Company, created a new way of measuring how well an company treats the people whose lives it affects—how well it generates relationships worthy of loyalty.  His Net Promotor Score or NPS was widely adopted and in use of companies of all sizes, segmenting the people into Promoters, Passives and Detractors with it’s simple one or two questions.  There are a great deal of benefits of adopting this approach and adapting it for employees.  When creating an eNPS (employee NPS) the annual laundry list of questions and ratings is replaced with a more frequent check-in, trends in happiness can be linked to real changes in the environment.  An entire team’s mood seems to be changing? Maybe it’s that new office space? Maybe it’s that new manager? Waiting 6 months to a year later to issue a survey is all too late.

ENPS-eng

So engagement surveys are too little too late and misused tools for measuring productivity.  eNPS is an improvement but in it’s attempt to be as answerable as possible still misses some of the larger aspects of the employee experience.  Some companies seem content to only measure those periods of time they are extracting effort from their employees, ignoring completely the fact there are external influences that might occur after 5pm.  The current toolset of HR is ill-equipped for the reality that the productivity and performance are great to measure but just as important are those things that employees themselves want to get out of work.

There is an alternative that more progressive organisations are adopting and in doing so re-humanising the process of collecting this data.  Instead of asking if an employee would recommend a place of work or waiting a year to prod at them with a laborious survey they ask a one question daily.  “How happy were you today?“.

Happiness at work and employee engagement are similar ideas but have unique and subtle differences in meaning.  Imagine you are managing a team and told to make them “more engaged” it might sound like a request for more meetings, for incentivising longer hours or an edict to start “cracking the whip”. Compare this with a manager tasked to make their team “happier”, this request isn’t about driving productivity. It feels more like a search for ways to empower the team, remove obstacles and better motivate them.

There are a number of new tools that seek to give a better insight into this broader question, at Forward Partners we use the MoodMap tool from Happiness Works. The tool asks the single question “How Happy were you today?” every day at 5pm.  I can answer in one click of the mouse.  Monthly “Climate Studies” probe deeper and allow more insight but not at the cost of provoking respondent apathy or the feeling that it’s all “too little too late”.  Better yet the tool allows respondents to offer “ideas” for improvements in the workplace.  In our use of the tool it’s been interesting to note that these ideas mirrored almost perfectly sections of Maslow’s hierarchy of needs.  The first suggestions were physiological or environmental – the air-con is noisy, my chair is uncomfortable etc.  In fixing these small, nagging, yet solvable items the “ideas” we’ve captured have evolved too.  There have been ideas for conference attendance, skill sharing, training budgets and social outing suggestions.  How’s that for “engagement”?

moodmap.io example

No software tool is magic and whilst it has been incredibly beneficial perhaps the biggest benefit is in facilitating the conversation around employee happiness.  For those companies looking for a way to gain insight into their employee’s well being and then empowering them to improve themselves and their environment these tools could be a great way.  Of course for the rest of you you have about 12 months to sharpen that pointy stick, or maybe wait to get that insight at the exit interviews?

pointy stick

The Bidding War for Talent – When Motivation is More Than Money.

The war for talent is a term coined by Steven Hankin of McKinsey & Company in 1997.  It has since become a cliché. It’s used as both a rallying cry and a cause for concern for HR and recruiting professionals everywhere.  Whilst the “war” metaphor is overused and without appreciation of the nuance of hiring it has become popular to look upon hiring people as either winning or losing.

In the current labour market certain skill-sets are at a premium.  The current demand for developers/programmers/software engineers, call them what you will, in both the tech giants and the smallest of startups has led to an increase in the cost and the style of hiring.  Scarcity or the perception of scarcity has meant that salaries have increased.  This is even happening to the point that certain programming languages become annually fashionable, “Ruby was so last year darling! It’s all about Python now!”.

In support of the notion of that scarcity a raft of tools have begun to appear and enabled a new breed of recruiting professionals – the Sourcers.  In the new paradigm more weight is given through the sifting of information and “finding” is the goal, occasionally it seems, at the expense of hiring.  The market seems to support this as more companies are created to solve the “problem” of talent discovery. In turn salaries rise and more tools appear.

I am in favour of developers being paid a fair wage for their work.  I’m even more in favour of the more skilled coders be paid better.  In my time as a recruiter so far I’ve personally hired developers on basic salaries as low as £25,000 to as high as £2,000,000 (really!).   However, there’s a problem in how the industry is accessing this skill set.  Increasingly, recruiting departments facing the need for volume have dehumanised the very people they are seeking to attract to the point of commodification.  This seems to have affected developers even more so as the traditional HR departments demonstrated their lack of understanding of their technical staff.  In the climate of scarcity and increased demand the recruiting industry has responded by shifting the easiest lever to pull, money.

This seems to make sense at the surface level.  Surely people will be more motivated to apply for a new job if the salary is higher than their current remuneration?  The latest aberration of this mindset is the online auction for talent, Hired.com.  Here recruiters effectively bid for the opportunity to interview candidates. There’s even urgency injected in the form of a time limit on the “auction”.  Here’s the real problem for me, any tool that changes the behaviours of an organisation it is being utilised by is also changing or at least reflecting a different culture.  For the candidate who is looking for a role having a rabid pack of companies compete for you may seem flattering but the truth is in this eBay of humans the “product” being sold is the very people Hired has ostensibly been set up to help.

Edward L. Deci is a Professor of Psychology and Gowen Professor in the Social Sciences at the University of Rochester, and director of its human motivation program.  Deci has conducted a multitude of experiments on human motivation and uncovering the “why” of why we do the things we do.  Far from agreeing with the prevailing thought that explicit financial reward was a motivator for increased performance he found the opposite “When money is used as an external reward for some activity, the subjects lose intrinsic interest for the activity”.   The basic certainties we hold about labour and “work” haven’t really been updated since the industrial revolution.  The initial boost of productivity offered in response to the external motivation of money soon wears off – to hold interest and that increased productivity there has to be something more.

Employers who base their attraction strategy solely on a financial driver are missing the opportunity to attract potentially better suited candidates to their roles.  Whilst is may be true that working in a larger organisation may offer a higher financial reward this may come at the cost of other areas of reward – the ability to make a personal impact on the product, recognition or even a sense of personal pride.  As an employer who competes only on price you always run the risk of being priced out of the market yourself.  A developer role at a games company may be fulfilling and a passion project for someone, a larger games studio can afford to pay more and cherry pick individuals, however when those skills suddenly become important to an investment bank with even deeper pockets individuals motivated by money can be further tempted away.

Corporate recruiters have blindly accepted that the way to engage the job seeking community is the price tag and minimal description of the role or why it matters to the larger organisation.  As recruiters we are taking away some of the best ammunition we have in this “War for Talent”.  If you can communicate what a candidate will be doing, who they’ll work with, why that’s important and how they’ll go on to contribute to the future of the company you might just see a greater engagement from those that see the ad.

If winning isn’t just ownership of the “resource” but winning the engagement of a person, the “hearts and minds” if you will how can we compete?  The answer is to know your true value proposition.  You might even want to consider talking to your current employees and asking what made them join. Tell your potential hires why they might like to work for you, not just that you have a spare desk and have priced their skills in relation to your competitors.  Venues like auction sites are not the answer for true long term engagement, for that we need to make sure we are creating roles that people would love to do – that they are paid fairly in relation to their peer group and rewarded for the value they add should be a given.

“You can only become truly accomplished at something you love. Don’t make money your goal. Instead pursue the things you love doing and then do them so well that people can’t take their eyes off of you.” – Maya Angelou

The Mis-Match of Algorithmic Recruitment

It’s the not so distant future.

A mobile app linked to a wrist mounted wearable wakes you, at precisely the right moment.  It monitors your sleep patterns and pulse rate and greets you each morning with a chipper “Go get ’em!”.  You dress and get ready to leave the house, the fridge has emailed to remind you that you’ll need to buy milk on your return.  You lock the door behind you with a swipe of your cell phone, keys are no more.  Outside, you step into a self driving car and take a different route to the usual commute – the car knew about the traffic before you did.  You arrive at work and boxes are moved into the previously vacant office next to yours.  You weren’t aware of a new co-worker. There were no interviews. They were algorithmically selected from the passive talent pool.  Kept warm on a diet of Pinterest photos of the office and Youtube videos of kittens selected to be the most humanising for the Mega Corp you happen to work in…

As far as predictions of the future go the vision I offer above is hardly advanced.  The technology exists for the wearables, the Internet of Things and the self driving cars, it’s just that last part that seems incongruent.

In the growing adoption of technology for HR departments seeking to differentiate their sourcing efforts, the idea of algorithmic matching is seen to be the magic bullet in the “War for Talent”.  Beyond the clichéd war metaphors and gullibility of HR Tech buyers is the future of recruitment to be left to the robots?

Technology has made the discipline of talent acquisition better.  We’ve moved far beyond the data entry and green screen databases of a decade ago.  As a modern workforce migrates to online services so their digital footprint increases making them all the more easy for the new breed of sourcers to find.  Now the future, according to some, looks set to be the automated addition of new workers and a touted increase in the skill of selection.  I’m no Luddite but I can’t help thinking this is a version of a technological utopianism whose primary supporters are those that seek to benefit financially from the adoption of the technology in question.

So many of the products available that claim to have solved matching are the same providers who don’t recognise some of the fatal flaws that their products exacerbate. The primary example of this is the reliance on the quality of data on both sides necessary for a match.  The majority of matching systems are parsing CV’s and then matching against a job description analysed in the same way.  This is exactly the limited key word matching that these systems say is so weak.  Even when other data are relied upon to beef up the input, suggestions of LinkedIn profiles and even LinkedIn endorsements are laughable. Especially in the case of unverifiable LinkedIn endorsements like mine for “Midwifery” and “Cheese Making”.  Of course I’m totally brilliant at both of these things…

Even the more advanced of the matching algorithms that incorporate some elements of semantic search (context of search, location, intent, variation of words, synonyms, generalised and specialised queries, concept matching and natural language processing) are constrained both by the data the candidates provide and the job description or criteria the employer matches against.  Anyone who works in recruiting will be able to quickly see that both of these sources of data are flawed and subject to constant change.  Data in both these areas can be knowingly falsified, incomplete and always out of date.

This data is inherently flawed because people themselves are inherently flawed.  Candidates will always seek to portray themselves in the best light, hiring managers will always add some extra “nice to haves” or even make the work of two people into one mythical job description.  A matching algorithm is forced to make sense of too many moving parts and results will suffer.

In moving towards this style of recommendation the people in the processes are reduced to the status of commodities.  Subtle nuance is lost and the chance for innovation curtailed by inelastic parameters.  People are not a product.  When Amazon presents you with a book based on your buying preferences it has only to reckon with your fickle, transient tastes.  A book doesn’t reject you because it feels it’s too far to get to your house, or because the other books on the shelf don’t feel your reputation is strong enough, a book doesn’t want to work from is own home or have a counter offer from a series of rival readers…people do.

Recruiting is a constant stream of edge cases.  Whilst a matching engine might work for less complex roles at large numbers, it won’t help you compete in winning that all important “War for Talent” you were so desperately spending your way out of.  The current level of technology is no match for the ability of a good recruiter.  This is not an indictment of the technology, it’s an acknowledgement of the greater problem that exists in the institutionally flawed HR departments and Recruiting processes the world over.  Using a tool like this to gain another datapoint to inform decision making is a valid use – it’s the shame of HR Tech that every new tool is paraded as “the answer”.  If the industry could wean itself off it’s obsession with the novel and shiny we might be able to tackle some of these issues at the root cause and realise that the skills we learnt whilst toiling at our green screens might not be entirely redundant.

Why the Recruitment Revolution won’t be sparked with Tinder – Candy Crush for your Career?

The world of HR and recruitment software seems to be going through something of a renaissance as of late.  The world that was dominated by user-unfriendly bloatware is becoming increasingly fragmented.  As more players rose to fill the gaps in usability for a beleaguered audience so smaller competitors rose up too.  For a small provider or startup, HR is a domain ripe for disruption.  It bears all the hallmarks of an industry that at it’s surface looks unchanged.  For the founders of startups who may have been at the unfulfilling receiving end of so many HRBP’s in larger organisations HR is a logical starting point for your new disruptive software solution.

In the mists of history where HR met software has only led to monolithic structures or rebrands of logistics software. The people in these electronic processes treated in the same way as stock to fill shelves or car parts for an insatiable assembly line.  The same clunking UI that held payroll information for accounts and performance data for HR was rolled out and forced on recruiters for managing the applications of new candidates. The biggest competitive advantage was the supposed “ease” of managing a candidate process.  In effect this led to a system in which people applying to large organisations were held at bay with template emails and auto-responses.

There are a great number of new systems for managing recruiting in a way that is more effective.  If you’re still managing the hiring process for your organisation in a “spreadsheet of doom” now is a great time to change to one of the newer systems – Greenhouse, Lever or my ATS of choice Workable are all enabling their users to manage applicants through the process in amore human way. (Provided you use them in a human way – template emails that sound like template emails still suck).

To match the rise of the new round of applicant tracking systems (ATS) we’ve also seen new tools for other areas of hiring.  Recently we’ve seen large rounds of investment for many mobile based “job discovery” tools.  They all have the obligatory cool names like Jobr, Emjoyment and Blonk. The trait these apps all share is their appropriation of the Tinder style user interaction.  Like a job? Simply swipe and you’ve applied, or at least made contact with the posting company. It’s so easy!  And that’s my problem.

“It’s a Match!” …but does either side really care?

There are enough problems with application processes that are too lengthy but to remove or lower the barrier to application to a simple swipe, by extension, must also lower the thought process behind the application.  Does scrolling through job listings on your phone equate to the same thought and consideration on the candidate side as seeing an advert, being taken to the companies website to learn more and then making an application?  There is an innate disposability in the action of a single swipe, there is little effort either physically or mentally in idly swiping through career options.  As a recruiter, I want more than that.  I don’t want the company I work for in a beauty parade held up for the swipes of someone looking for a Candy Crush Career…

Whilst the act of application, that is expressing interest in a job via one of these apps or polishing a LinkedIn in order to apply, fulfils the basic criteria of “job seeking” it does seem to overestimate the impact of technology on human behaviour.  The “ease” of use for the candidate is the equal and opposite reaction from the Recruiter side who is now given over to service of a greater number of applicants that haven’t really gone to the lengths of application they normally would have.

There are a greater number of applicants and it becomes all the more difficult to find the signal in all that noise.  Those who are not at the coal face of recruiting often tout an increased volume of applications as beneficial.  As if throwing more bodies into the top of the funnel will result in the same level of quality and increased output from the same recruitment team.  Whilst this can be true it’s only true if the quality is maintained. Scaling a recruitment effort is much more than opening yourself up to more applications. The best adverts for vacancies should cause potential applicants to opt in or out and gauge their own cultural fit.  The worst metric for the success of any recruitment effort is the raw metric of applications.

Perhaps at the root of all this is the transient psychology of a Tinder swipe. People are time deprived and the application of the swipe to jobs seems like a saving but in effect shifts a burden to a recruitment function that will only truly engage if they too swipe your application.  Monotonous, machine like swiping. Less and less meaningful engagement. Just as Tinder was a nail in the coffin of notions of romantic love perhaps Tinder-clones for recruitment are just at odds with my romantic views of candidate experience?

The Magic of the Myers-Briggs Personality Type Indicator – The Technological Panaceas of Hiring that aren’t.

Hiring is scary.

Hiring is a risky process that we all know can do irreparable damage if we get it wrong.  There are countless studies that all make the case that a false positive is more damaging that a false negative.  It’s hard to “undo” a bad hire.  So how do we mitigate against this?

In the world of hiring there is an anti-pattern that the answer to the question of “how to hire?” is always answered better elsewhere.  We tell ourselves there exists a panacea for hiring.  There is a strategy to beat all others.  A technology so advanced that it alone is enabling a rival to mop up all that talent that’s spilling all over the place.  In effect, in making strategic decisions about technology in hiring we have outsourced our facility for critical thought.

We believe the purveyors of these advances because they come with the trappings of authority. They quote statistics in polished powerpoint presentations, wield certificates with pseudo-scientific credentials or a hat.  So much of the decision making for strategy in recruitment has become about copying our competitors.  We assume that if something is working elsewhere it will work for us. Often this is based on information that is outdated and organisations don’t change their processes to fit in with the new thinking.  Take for example the role of those “impossible to answer questions” pioneered by Microsoft and later Google.  It is now industry wide common knowledge that there is no correlation between the ability to answer these brainteaser questions and the ability to perform well in the role you are interviewing for.  Yet how many organisations are still asking them because they think they should be?  When was the last time you ran an audit of the questions asked at interview in your organisation?

Ever since companies have needed to hire people there have been providers offering them magic-bullet future predicting insights into their candidates.  With just a few answers to a test you can predict the suitability of a candidate for your company.  The granddaddy of these magical tests is the Myers-Briggs Type Indicator.

The test sorts it’s takers into one of 16 different types each with a description that have now been misappropriated by HR departments to make wide ranging judgments about the suitability of prospective employees.  There have been many more erudite take downs of the lack of use of the MBTI this is a great place to start.

Here, as a primer, are a few reasons why the MBTI shouldn’t be used in decision making when hiring –

  • The test is based on the work of Carl Jung and uses his “types” in a way he said they shouldn’t be used “Every individual is an exception to the rule,” Jung wrote.
  • Jung’s principles were later adapted into a test by Katherine Briggs and her daughter Isabel Briggs Myers, who had no formal training in psychology.
  • The test uses false, limited binaries that force the taker into a either/or choice often on measurements where a better representation is that we are all somewhere on a spectrum.  Jung himself wrote “there is no such thing as a pure extravert or a pure introvert. Such a man would be in the lunatic asylum.”
  • As much as 50 percent of people arrive at a different result the second time they take a test, even if it’s just five weeks later.

Lastly and perhaps the best first step to make when evaluating the claims of any HR holy grail is to look at who stands to benefit from the introduction of any new test, technology or methodology.  More often than not this benefit is either financial or one of prestige.  In the case of the Myers-Briggs there is a self supporting industry of those that pay for the licensing to become testers and then propagate the test’s worth within their organisations thus increasing the need for their own services.  The real winner in the “success” of the MBTI is it’s producer.

This is a truism for any of the latest crazes and bandwagon technologies that present themselves in the hiring space.  If someone stands to benefit then they will tell everyone that it’s the best thing ever and will change the face of recruitment as we know it.  Be wary of that hyperbole for that way lies a trail a misspent dollars.

The hard truth that we all face is of course that there is no one perfect system.  There is no solution that can be purchased that will solve all your hiring ills.  There are organisations that make great strides in their own hiring and those stories have worth.  However, as an industry we shouldn’t seek to become an inferior copy of another’s success.  Instead we should ask ourselves what are those aspects that seem to work for others that we could trial and adopt at our own companies.  Listen to the stories of others but know that the stories themselves are not the path to knowledge. Knowing something requires research.

We should think critically about both the message and the messenger before going ahead with those decisions that will shape our ability to attract and retain talent for years to come (or at least until the next bandwagon we jump on).

So the Myers-Briggs Type Indicator isn’t magic. It’s that magical thinking that is a failure of critical thinking. Not thinking critically about a testing framework that you later use as a reference point to inform your decision making is an act of sabotage against your employer… but then I would say that I’m an ENTJ.

7 Myths About Great Résumés

When friends find out I work in recruitment they often have a lot of questions.  They might ask for funny stories, the strangest applications I’ve seen, but it’s never that long until I’m asked if I’ll look at their own resume.  Sad though it may seem, I don’t mind doing this, actually I quite enjoy it.  Almost every time I’ve done this I hear the same justifications for formatting, length, and content come up again and again.

I’m sure that this advice is always given with the best of intentions to those seeking jobs.  It’s folksy, friendly and given in the same tones as the motherly maxims we were fed as children. However, times have changed.  We know that if we pull “that face” we won’t stay that way, we know that eating those crusts didn’t put hair on our chests, we even know that if you swallow chewing gum it wouldn’t “wrap around your heart and kill you” (my elder sister used to tell me this with absolute conviction).  So much of this weird advice is now dismissed and yet when it come to job seeking we hold certain things to be absolute truths.  Here are seven thing people blindly accept as the “right way” and the reasons I think we can now give up on them.

Myth Number 1 – “Your resume should only be 1 page.”

Truth – This is one of the most pervasive pieces of advice I hear.  Often I find people struggling to fit their experience on a page, resorting to 10pt font size or self-censoring and leaving some great things out, desperately attempting to make everything fit into no more than two sides of A4.  The problem with that?  I will probably never print your resume.  “Sides of paper” is a physical restriction that modern ATS’s (Applicant Tracking Systems) and candidate tracking systems have made redundant.  The truth is that I will scroll through a CV on a screen, normally in a frame within another application, I’ll be reading your resume not counting pages.  Some recruitment software even removes page breaks so the length is purely a measure of holding a recruiter’s interest. Write interesting, relevant content and a recruiter won’t mind if you add a page.

Myth Number 2 – “Avoid all complicated fonts or design elements.”

Truth – This is another of those things that was potentially true in the past.  When looking at a paper resume it may have been the case that in printing a complex design would be corrupted in some way.  Similarly, early ATS’s couldn’t cope with any design elements as they tried to parse documents and strip out information.  Any modern system will now happily display submitted resumes in a variety of formats, even as beautifully crafted .pdfs the better systems are now advanced to the point where they can do this and still strip out information and enable searching.  Never has this advice been so misplaced when I was recently looking for designers.  The number of standard template resumes I received was scary – if you’re a designer show it! If the design you send to a recruiter is overly complex and doesn’t convey information clearly it will tell them a lot more about your abilities than the content.


Myth Number 3 – “Recruiters only spend 5 seconds looking at a resume.”

Truth – Recruiters only spend five seconds looking at a bad resume.  With clarity of format and inclusion of relevant information you encourage a reader to read on.  Irrelevant, clichéd or boring copy means anyone, not just a recruiter won’t linger for long.  You should write in a consistent format that is easy to take in – I have suggested the following format for wring about each job –

Company – Role Title – Dates of Employment
Who the company are, what they do – just a couple of sentences. 
The role you were tasked to perform – the duties you had
Achievements in the role – Call attention to specific things that match the role you’re applying for or experiences you want to call out. 
This makes for easy reading, it tells me what you did, and how you did it. I don’t have to second guess obscure job titles and still offers you the chance to blow your own trumpet a little.
 
Myth Number 4 – “Use Bullet Points.”
 
I like bullet points, when listed the duties you undertook or telling me about specific individual elements of a whole they’re great.  However, not everything should be bulleted.  I’ve seen resumes that are so clipped and hammered into bullet lists that they are no longer comprehensible.  As a rule any stylistic choice should enhance legibility.  If a resume is comprised totally of bullet points, each with their own clipped structure it can be like reading a newspaper using only the headlines.  I’ll thank you for the brevity but I’ll also doubt your ability to write a complete sentence.


Myth Number 5 – “Identify the problems of the employer.”

Truth – Don’t do this. I’ve never seen an example of this that doesn’t sound arrogant.  I can’t imagine a case where it wouldn’t.  Cite relevant experiences, give examples that you think may resonate with the problems that your target employer would also face, but the assumption of a candidate leaping in and saving the company they are applying to work for is a turn off for most recruiters I know.


Myth Number 6 – “Don’t use jargon.”

Truth – Don’t dumb down your resume to the point that it looks as though you don’t know what you’re talking about.  This is particularly true for technical professions.  A candidate is correct to assume some level of knowledge from the recruiter who is reviewing the resumes before they reach a hiring manager.  If a developer or sys admin is giving more details about a project they worked on I want to to know what technologies they used.  There’s another reason to keep in the technical terms too – they are often how resumes are searched and candidates are discovered in the first place. In any database of resumes, LinkedIn included, search is initially about filtering millions of people through key words – they have to be there.
   Technical terms are not meaningless, include them.  Don’t include the truly meaningless, clichéd company specific terms or management speak but if the term is relevant and needed don’t be afraid to use it.  A good recruiter can either be relied upon to google the term or if the rest of the resume is good they’ll ask you.

Myth Number 7 – “Don’t add your hobbies or interests.”

Truth – As a recruiter I tend to see all candidates as meaty flesh bags containing a skill set, their only possible use being to serve the organisation for which I currently ply my trade, said no one ever.  An organisation that would discriminate against you for your hobbies or interests probably isn’t one you would want to work for.  However, there are some people who may have legal yet contentious pastimes.  Things that might not be a good idea to add are religion or political activity or hunting as an example.  It’s important not to give the recruiter a reason to reject your application out of hand but at the same time as a recruiter I’d still like to know you were a well rounded human being.  
In a related area, don’t make up hobbies or interests, recruiters will ask you about them.  There’s nothing more awkward for us both like a sudden improvisation about your made up live action role-playing experiences.

Remember as Mary Schmich said “Advice is a form of nostalgia, dispensing it is a way of fishing the past from the disposal, wiping it off, painting over the ugly parts and recycling it for more than it’s worth“. The next time that someone offers you some advice on your resume make sure that it really applies to the application you’re making, but this is just my advice.