HR data and analytics drives profits but at what cost?





By Neil Patrick

HR analytics can punish your employees, but you won’t worry about that if you want to win.

In his 1976 book, 'Computer Power and Human Reason: From Judgment To Calculation', author Joseph Weizenbaum laid out the case that while artificial intelligence may be possible, we should never allow computers to make important decisions, because computers will always lack human qualities such as compassion and wisdom. Weizenbaum made a crucial distinction between deciding and choosing. Deciding is a computational activity, something that can ultimately be programmed. But it is the capacity to choose that ultimately makes us human. Choice, however, is the product of judgement, not calculation.

Stephen Hawking went even further when he said,  "...the development of full artificial intelligence could spell the end of the human race. Once humans develop artificial intelligence, it will take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded."

I cannot say if this distopian vision will or will not ever manifest. But it is plain to anyone that we are racing down this path with scarcely any care. We are already seeing the first applications of big data and AI based workforce decision and management systems. HR leaders like it because it promises to solve several of their most longstanding and vexing problems.

HR has been fed up forever about not being taken seriously. HR big data and analytics promises to be their saviour. It suggests that HR can transition from being perceived (wrongly in my view) as fluffy and utilitarian to having a proper seat at the leadership table, because like its rivals in finance, sales and marketing, it can now deploy hard ‘scientific’ data to back up its proposals.

It also promises a happier, more engaged workforce. One in which every twist and turn of employee sentiment can be quantified and responded to. If the data says people are feeling worse about something, HR can know this quickly and help rectify the problem.

I wish this were true. But I fear the opposite. That’s because every technological advance includes the option of being deployed for good or evil.

What HR may not like so much is that HR data delivers an extremely useful tool for business leaders to push and punish people. It’s a deal with the devil, in which HR’s quest for happy, engaged workers, risks being hi-jacked by the rest of the business to brutally force up productivity and drive down cost.

Incidentally, my argument skims over the very real practical questions around HR data and its inherent unreliability as Marcia LaReau has convincingly described here in her post, ‘To a Hammer, Everything is a Nail’.

Business has some critical problems today. Growth and profitability are chief amongst these. And countless studies show there is little correlation between hard profit and employee satisfaction. Sure there are plenty of examples of firms growing successfully who also invest in their people. But when we look at the most established large firms who are making the most money, most care much less about their people.

This is actually a very simple economic truth to understand. In an open competitive market, whoever gets the most work done for the lowest cost, wins. And if that means some people suffer, then so be it.

Every employee survey I have ever seen identifies that a person’s manager is the single greatest determinant of job satisfaction. It’s not pay, it’s not perks, it’s not flexible hours. It’s the person who manages you. If they are inspiring, caring, transparent, supportive, their staff will enjoy their work.

But here’s the problem. Managers that display these qualities are becoming an endangered species. Because their bosses usually don’t care very much about strategic HR. They do care about smashing their immediate revenue and profit targets. HR gets people hired for them, sorts out people issues and keeps them out of court. Everything else is fluff.

It’s that simple. Good leadership (not data) delivers happy and productive teams.

But we also know that good leadership is a frustratingly elusive and expensive resource to acquire and maintain. One accidental bad hire of a psychopathic manager and the whole of an organisation’s carefully nurtured culture can be demolished in a few months.

So if good leadership is expensive and scarce, but data is cheap and plentiful, the choice becomes a no-brainer.

What is becoming visible now is that there are firms who take HR data very seriously. And what we can also see is just how punishing and dehumanising the application of HR data can be in practice. To verify this, all we need to do is examine the firms where HR data is most developed and embedded in the day to day operations of the organisation.

And right now, probably the most advanced organisation in this field is Amazon. In January 2019, Amazon became the world’s third most valuable company by market capitalisation, after Apple and Microsoft.




Yet in some US states, nearly one in three Amazon workers are on food stamps. For Amazon, this is even better than paying people almost nothing. It is the transference of part of Amazon’s wage bill to the taxpayer.

In Amazon warehouses, every second of people’s work is measured and evaluated. They may walk over twenty miles on a day’s shift. Their productivity is tracked and ranked against their peers, with whoever is at the bottom of the table likely facing disciplinary actions and threats. A toilet break can cost you your job if it exceeds a tightly prescribed time allowance. Many describe it as a daily hell, which they endure only because they have few other options.

Welcome to the brave new world of HR big data. It’s being corrupted from the get-go. And if you’re an HR leader, be careful what you wish for.


AI in recruiting really means ‘abdicated intelligence’






By Neil Patrick

What is advocated and marketed as technology-enabled recruitment processes increases the difficulty in finding and retaining great people. The best people are made not found. We don't need to get better at ranking people by increasingly tightly defined data points, we need to take ownership of our responsibility (and self-interest) to find good people and make them great. 

Artificial intelligence is the big topic in almost every professional field right now. From drones in farming to robot surgeons, the overriding narrative is about the tasks which AI will enable us to perform better, faster and cheaper.

HR and recruitment are no different. The application of AI is spreading like wildfire as new tools are developed which expand the range of tasks that AI performs to assist recruitment management processes.

Until now, it was relatively simple to integrate the digital world with our own professional world. Have a LinkedIn profile which is properly constructed. Expand our professional networking onto one or two social media platforms. Write some commentaries or blog posts. By these means, anyone checking us out could easily discover our credentials.

Some people understandably chose not to participate or did so in the most cursory way possible. They had no wish to participate in the online race. They had fears about privacy. They didn’t understand how social media worked. They had better things to do with their time. All these were legitimate grounds to not participate. But not anymore because…


This is now all set to change

The next wave of AI and big data is going to transform the processes of hiring way beyond anything we’ve seen to date. Traditional recruitment is a gruelling, complex process for employers and recruiters alike. Recruitment teams have to be heavily incentivised to commit to the heavy workloads involved. And this costs money. A lot of money. But AI will streamline and speed up these processes. It will be able to identify suitable candidates in a few seconds. It will message the chosen few and chatbots will perform initial screenings. Candidate selection decisions will be made on the basis of data and scoring algorithms rather than fallible human interactions. Very little human intervention will be needed.

Recruitment costs will fall even more. Hiring efficiency and speed will increase. Hiring choices will be validated and justified by the ‘scientific’ methods involved.

At least that is the vision. The reality is more worrying.


Why it’s flawed

Data is not science. The principal predictors of job performance cannot be discovered by algorithms. The first attempts to automate the selection process created a bigger mess than before. Online job boards and applicant tracking systems (ATS) drove application numbers sky-high and candidate quality tumbled. But acquiring 500 applications cost around $50. Using a professional head hunter costs about $30-$40,000 per hire for professional vacancies. These economics ensured that automated recruitment processes took hold and continue to grow in usage.

Nick Corcodilos explains in this video why the application of data driven metrics to recruitment ensures that employers miss many of the best candidates for any given role:





I agree with everything Nick says here. This process is flawed. It cannot find the best people because the available data points cannot determine that they will perform well on the job. And because quite a few of the very best people choose not to present themselves online in their professional capacity. Yet for all its weaknesses, automated recruitment is only going to expand because the cost differential is so compelling.

In a strong and growing economy, organisations can invest in quality processes. In an economy which is uncertain and faltering, when profits and growth are elusive, focus inevitably shifts to cost reductions. Cost trumps quality in such times.


Your career is at risk if you choose not to participate

It seems logical to me that the current and anticipated applications of technology and artificial intelligence in recruiting will continue to erode the quality of hiring decisions made. This may deliver short term cost gains, but will push up long term costs as turnover rises and employee performance falls.

Yet this is not my greatest worry. My fear is that the relentless advance of this technology will create a new underclass of smart, educated and capable people who have chosen for legitimate reasons not to present themselves online. These people will become completely invisible to the data capture bots. And that invisibility will slowly but surely eat away at their employment opportunities.


There’s not a ‘talent shortage’, there’s a leadership vacuum

 
For organisations, the deployment of recruitment AI encourages organisations to abdicate their responsibility to create and nurture their own human talent pool. This creates a downward spiral of ever increasing data point discrimination reinforcing the mythology of what employers disingenuously call their ‘talent shortage’.

And if the belief in the pseudo-scientific reliability of these systems persists within management, we will see the abdication of leadership’s responsibility for taking good people and helping them become great. Instead, the tools will be adjusted to cure perceived shortcomings, when the real shortcomings are rooted in the mistaken faith in progress through technology.

Artificial intelligence is well named. Because it’s not real intelligence…

PS. My good friend Marcia LaReau at Forward Motion Careers has a great post here about what jobseekers can do to avoid becoming a victim of this situation. 

Wonga is not the sort of fintech we want thank you






By Neil Patrick

Does technology have a moral compass? I guess most would say it’s morally neutral. It is the people creating it who must carry that responsibility. But technology and big data is the great power of our age. And with great power comes great responsibility.

When someone tells you big data is the answer, it’s smart to look beyond the data

This week Wonga.com went into administration. It’s a business I’ve watched with interest since its foundation in 2006. But not with admiration, rather a growing unease that its business model wasn’t just unethical, it was fatally flawed. It was bound to have a messy ending.

Wonga was the UK’s biggest payday lender. Payday loans in the USA remain illegal in 14 states yet created several multi-millionaires there in a few short years. And as is usually the case, when that happens, the idea travelled across the Atlantic very quickly to the UK.

Not very long ago, Wonga was a posterboy for the fintech sector. They invested in lavish TV commercials. They sponsored Newcastle United Football Club for £8 million a year. And they were once courted by investors eager to share in their profits.


Papiss Cisse of Newcastle United.
 Photo credit: Dudek1337


And Wonga's founders would be the first to admit that they trusted data and algorithms more than people. Couple big data with a fast and simple internet-based application process and Wonga were sure they would be the next big thing in money lending. They would take a proven business model from the US and turn it into a data-driven internet giant in lending. Tech is so much cheaper and more reliable than people after all. Or is it?

It all went horribly wrong for reasons which are not the same as the ones talked about in the mainstream media. They give you the headline facts. I’m more interested in what created those facts…

A short history of payday loans in the United States

Banking deregulation in the United States in the late 1980s caused many small community banks to go out of business. This created a void in the supply of short-term microcredit, which was not supplied by mainstream banks due to their unprofitability. That unprofitability was easy to explain – banking regulations wouldn’t permit the ultra-high interest rates needed to cover the high default levels that such loans inevitably create.

The payday loan industry sprang up to capitalise on this void and to supply small short-term loans to the working class at very high interest rates. But how was it that they were able to make these loans when banks could not?

W. Allan Jones, the 'father of payday loans'

In 1993, Check Into Cash was founded by Allan Jones in Cleveland, Tennessee, and became the largest payday loan company in the United States. He’s known as ‘the father of payday loans’ - I am unsure if this is a tribute or an indictment - and his business was made possible only after he donated to the campaigns of legislators in multiple states, convincing them to legalize loans with such high interest rates.

And thus a massive new industry was born…

Subsequently, the industry grew from fewer than 500 storefronts to over 22,000 and a total size of $46 billion. By 2008, payday loan stores in the United States outnumbered Starbucks shops and McDonald's fast food restaurants.

And this was the point at which two entrepreneurs named Errol Damelin and Jonty Hurwitz envisioned an internet-based payday loans business in the UK. Both had previous internet start-up experience; but critically neither had any experience of retail banking. I could see exactly how Wonga would make money. I had no doubt about that. What I could also see was that the business model was completely unsustainable.

What exactly are you disrupting?

Wonga claimed and possibly believed they were disrupting big banks. They were not. They were actually disrupting doorstep money lenders and loan sharks; some of the most odious and exploitative businesses you will find.

And guess which other online sector is also in trouble today? Online ticket sellers. They claim they are making event tickets more easily available. But they are actually using all sorts of devious online trickery, enabling inflated prices not to mention a raft of fees and charges which are added to the bill. If they are disrupting anyone, they are disrupting ticket touts and making a killing in the process.

And just like the payday loans sector who became the target for heavy intervention by the Financial Conduct Authority, so too are the secondary ticketing websites, including Viagogo, StubHub, GETMEIN! and Seatwave. All are now under similar legal and regulatory threat by the Competition and Markets Authority.

So when new businesses say they are disruptive, that’s not automatically a good thing. What matters is that the disruptors are challenging an expensive or exploitative sector, remedying the fundamental failures of that sector, not amplifying them through mere digital deployment.

Investors loved this business – at first

In 2008, when Wonga was still an early stage start up, I was doing the rounds of venture capital firms in London, capital raising for another fintech startup. I distinctly recall one venture capitalist telling me that what he really wanted was another Wonga.

Wonga had already raised £3.7m to fund its initial platform development. In July 2009 Wonga raised a further £13.9m of funding through other VC firms (including the one I talked to). These investments enabled Wonga to complete their first platform and begin lending money.

The point here is that investors are looking for a quick return on their investment. Usually a sell out or exit after 3 - 5 years. They want a deal they can buy into cheap, and sell fast with a big return. This rapid rate of buying in and selling out enables them to avoid possible regulatory trouble because:

Regulators are far too slow to intervene to remedy exploitative practices

As I was watching the growth of Wonga and other payday lenders, I was also watching what regulators were doing. One of the first to take any notice was the Office of Fair Trading. Yet in their initial reviews they claimed that they had too few complaints to merit any sort of intervention. Instead they opted to merely keep an eye on things. It wasn’t until 2012 amidst explosive growth of payday lending and mounting criticism, that the Financial Conduct Authority decided to intervene.

The Financial Crash of 2008 was great news for payday lenders

Although when Wonga was founded, the financial crash of 2008 was still ahead, when it came, this event was to ensure that payday lenders were to benefit. Banks and other mainstream lenders stopped lending to virtually everyone. But people’s need to borrow didn’t diminish, which left Wonga with suddenly much less competition from more traditional lenders. Moreover, the economic doldrums which ensued in the Great Recession created new customers in their droves.


Screenshot from Wonga.com showing the cost of borrowing £100 for 30 days as at 17 Nov 2013

In 2012, a typical loan from Wonga had an annual percentage rate of 4,214 per cent. This equates to a charge of £42.96 for borrowing £100 for just 36 days. The debate still rages about this. Payday loans are by definition very short-term. And small. But most traditional loans are bigger and long term, so historically, the annual percentage rate of interest (APR) was a convenient and appropriate way to measure the cost of a loan. Not so much when the loan is for just a few days. My view was and remains that APR is not relevant when assessing the cost of ultra-short-term loans.

No-body wanted to buy Wonga because other predators were eating it…

In September 2012, Wonga reported profits of £45.8m for 2011 from revenue of £185m. But the threats and cracks were already showing. Not least of these was the Financial Conduct Authority’s new rules and ongoing investigation into the whole payday loans sector.

Already anticipating a new financial compensation opportunity, legal claims management firms saw that payday lenders would become their next carcasses to feast upon. And they were right – payday loan regulation created a whole new raft of claims opportunities. Coupled with the capping of the fees that had originally made Wonga so profitable, these claims grew and grew until the business became unsustainable.

Last week in a desperate last hour bid to save the Wonga from collapse, the shareholders stumped up a further £10m. But it was not enough.

Never leave the techies in charge of the business

The mainstream media, politicians and even the Archbishop of Canterbury have complained endlessly that Wonga’s business was exploitative. I agree, but that’s not how the business made so much money. The key to business’s early profitability was NOT through its high interest rates, but the fees and charges it applied to every borrower that failed to meet their repayment terms faultlessly and the rolling over of these charges into new loans.

Before regulators stepped in to tackle this in 2012, if a customer failed to repay the loan in full on the due date, a default fee would be charged and interest would snowball the debt endlessly thereafter. The debt would then be ‘rolled over’ into a new and bigger loan. Such things would involve a lot of letters and phone calls of course. And every letter and phone call would also incur a large fee which was also added to the debt. In the matter of a few weeks, a smallish loan could be transformed into a debt many times larger. This was insanely profitable.

Regulation is inevitable but is always too late

On 28 November 2012, following concerns that small loans, intended to be short-term, could become prohibitively expensive, the government announced it would give the Financial Conduct Authority powers to prevent indefinite rolling over of loans and effectively limit charges.

By this point, Wonga had already made millions in profits. But from this point, their business model couldn’t work. It was just a matter of time until the whole thing crashed. The only thing that surprises me is that the business was able to limp on for another six years.

When the body falls, the vultures complete the kill

The coup de grĂ¢ce that finally finished Wonga ironically wasn’t it’s business model. True, this had become fatally wounded by the imposition of regulations to prevent their loan roll overs and excessive fees for defaulters. What finished them was another group of predators – the claims management companies. In 2014, the firm introduced a new management team and wrote off £220m worth of debt belonging to 330,000 customers after admitting giving loans to people who could not afford to repay them. But even this was not enough to deflect the inevitable.

The endless cycle of financial ‘innovation’, profiteering, regulation and collapse

Wonga is more than just a tale of dubious morality. It is a perfect demonstration of how a purely technological vision, lacking depth of understanding of the industry sector and its unique characteristics, inevitably wrecks the lives of customers, investors and staff alike.

I am pleased that Wonga is no more. But I am not optimistic that we won’t see this cycle perpetuating again and again in other business sectors. It’s not entrepreneurial innovation or disruption, it’s a perfect storm of lack of morality, self-delusion, arrogance and greed, going unchallenged until long after the damage is done. And as usual the biggest victims are those least able to bear it.