Matt chat

The Overthinking trap

Week 2 of my #nudge plan and other than a two-day dip (which I’m conveniently blaming on feeling the effects of Covid Jab#2), all still on track. Great to get some early quantitative affirmation of achieving sprint goals with an astonishing half stone weight loss for myself and seeing our friends at Black Isle Group | BIG Isle Group seeing accelerated growth in their sales and marketing metrics.

I’m not a fan of the phrases “with the benefit of hindsight” and “if I knew then what I know now” but I can’t help wondering what the spotty, intensely shy sixteen-year-old version of myself would do with the knowledge of the power of everyday actions. By then my childhood dreams of being an astronaut, fighter pilot and cricketer had already been crushed (not through lack of ability clearly). Instead, I had become addicted to programming which way back in 1980 wasn’t seen as a mainstream career choice.

I looked with envious eyes at my elder brother, who was living the rockstar life in the embryonic but already glamourous electronic games market. Whilst I developed a program to help quantity surveyors manage their “Bill of Quantities” he was building BC Bill, a “strategy” game involving a caveman, a collection of dinosaurs and a large club. There is no doubt I could’ve followed his path but I wasn’t quite sure what I wanted to do. Result – do nothing.

So what everyday action would I advise 1980’s Matthew to do to overcome his recurring theme of procrastination?

Simply: “don’t over analyse, just do”

Throwing that challenge to you all out there, what everyday action would you have advised your younger self to do so it stuck as a habit into later life.

JURY SERVICE. Like death and taxes, eventually, it comes to us all.

I jest, but as a small business it has a large income and reputation impact when you can’t deliver services. Having delayed the inevitable call-up (to finish analysis for the NHS to recruit thousands of vaccinators), I dutifully attended court.

I say court. it was in fact a cinema. Imagine the scene – the entire jury COVID-ly dispersed across the cinema theatre each juror with their own individual camera with a live feed from the court projected onto the big screen. It felt as though you were going to see the ultimate immersive blockbuster movie with the ability to vote for which ending you preferred. You couldn’t help but be impressed how they’d worked around COVID.

Having mentally prepared myself for an extended absence with stupidly long hours to catch-up, I was not prepared for the disappointment of being only a substitute juror – couldn’t make the first team, on the bench, not a proper juror, what a failure. Ultimately I was destined for an early bath – thanked by the presiding judge for my “sacrifice”, I was sent home.

During the process, I was fortunate enough to chat to a fellow reject juror which turned out to be THE most educational experience I’ve had since I met my now wife in Newcastle 7 years ago. He was the director of a company building offshore wave and tidal energy solutions. We skirted around a wide range of subjects including
· Seaweed for carbon capture and why it needs to sunk to 1km
· Carbon offset schemes – eg providing solar panels for remote African villages as an alternative to diesel to generate power
· Big corporates taking responsibility – Microsoft not only committing to net zero but repaying their carbon impact dating back several years

We turned to my most recent post about #nudges and #thebigapproach Jeremy Campbell being a way of embedding new habits for the good of the planet as well as wellbeing and business performance. Asked what his top daily nudge for us, ordinary individuals to collectively make a difference would be – after “vote for the right government” (impossible to make a good habit), he landed on “eat beans not beef”. The current meat supply chain is an end-to-end eco-disaster – land desecration, water usage, farty cows. Eating beans imported from Namibia is more environmentally friendly than eating Sunday roast from your local farm. So until we genetically modify cows to be less gaseous and meadow consuming, we’re stuck with reducing our meat intake by around 30%. That’s just two meals per week being vegetarian-based rather than meat – easy? Venison is fine, apparently Scotland is overrun with deers but convincing the population to eat Bambi for breakfast to save the planet is a daily nudge too far.

In summary, what an amazing week – experiencing justice in action, chance encounters, seeing how tech transforms and finding a really powerful daily #nudge

And love my clients for their understanding!

Don’t make me post…

So the first week is done and (if I finish this post!), a 100% successful week of completing all my daily nudges. Doing the three things I know I should do regularly, they will make a difference, they are important – but somehow I never have enough time or something else crops up or I’m nervous about doing them or, or, or….
 
Yeah, I’m not a prolific Linkedin post jockey – it takes an inordinate amount of time for me to write one simple comment (teasing the right words into the right order, just enough intellectual oomph without appearing to be a show-off, humour, personality and is this really of any interest?). I love a daily walk, but without a dog, it’s just me and the F1 podcast. I convince myself I’ve far too much work on so revel in the crazy notion that another 12-hour stint in front of a screen without a proper break is the answer. And as for talking to real people unless I really have to? Give me strength, I’m an IT geek at heart and as you probably know we’re generally not known for our extroversion. Chatting to anyone on 1 to 1 basis beyond my cat and my keyboard can be a challenge if I’m not in the mood. So this weeks result is quite amazing. Only week 1 admittedly, but still, it’s a total turnaround for me.
 
Aristotle is quoted as saying “We are what we repeatedly do. Excellence, then, is not an act, but a habit” (and before the Linkedin police start shouting I know it was Will Durant’s interpretation, but Aristotle has more star appeal). I’m not suggesting that Jeremy Campbell of Black Isle Group | BIG is the modern-day Aristotle, but his mantra of “Small steps lead to BIG results” is a brilliant business interpretation of that ancient wisdom.
 
The simple idea of the nudge app has the right blend of encouragement, clarity, self-accountability, coaching and tips that’s got me doing these important extra things.
 
And we now know this thing works – it can transform results and people. For example, in one of the pilots, a sales team dramatically improved their pipeline and became a more cohesive team. And at a personal level, I lost around 50lbs – the new health habit stuck with me.
 
The roadmap for the next year takes it to a different level and it’s fantastic to feel an integrated part of the team to deliver it.
 
Congratulations to Maverick Insights Limited‘s superb team of Jack Mills #paresh #manthan #paras for all your hard work, creativity and expertise (By the way, all of you guys please add the daily nudge of “Do not ask Matthew for another high def monitor”).
 
Click send. 100% done.

Getting insights from People Analytics is the easy bit. ROI? That’s far, far harder.

Netflix’s recent documentary, The Great Hack, exposed the dark side of data exploitation using its power to influence millions of peoples’ voting intentions.

If the claim that “Data has surpassed oil as the world’s most valuable asset” is true then why is it that the benevolent intention of people analytics to improve workforce productivity rarely leads to the kind of impact seen by Trump’s (first) presedential campaign?

It’s an all-too common story. A forward-thinking HR director invests in a people analytics platform to mine the company’s people data. They’re hoping for insights that might boost productivity, reduce sickness absence, and cut attrition, among many other benefits.

The new system springs into life, using complex algorithm after complex algorithm, trawling the shifting sea of employee data for hidden connections. Finally, it brings forward a tantalising list of “actionable insights”, and then… well… nothing much happens.

There’s a good reason for that. Yes, selecting the right technology is important, but it’s only the start. Convincing a sceptical organisation to make workforce decisions based on data is just as important – and it’s a whole lot harder to achieve.

If people are your great asset, then people analytics is your greatest ally… potentially

People analytics is now established as a source of value for HR. Used well, it can help uncover hidden workforce issues, improve the quality of recruitment, reduce absenteeism, predict which staff are most likely to leave and improve competitive advantage.

And as a result, a growing number of employers are investing in HR analytics tech; 71% of companies see people analytics as a high priority.

Going back to our recently-procured people analytics system as it spits out an endless stream of so-called actionable insights – when does information become insight, and when does insight become actionable?

You might have seen a dashboard which is supposed to provide powerful insight. You ponder for a few moments, hoping that the thing you’re missing will hit you. You scratch your chin; maybe you mutter “that’s interesting” before shrugging your shoulders and moving on to the next equally actionable insight to ignore.

To get anything to be of actual benefit from your people analytics, it needs – as a bare minimum:

  • To be trusted
  • To be understood clearly
  • To be important to the decision maker

You also need to overcome any tissue rejection from your organisation’s culture.

It’s no wonder so many supposedly actionable insights end up on the “too difficult” shelf, slowly gathering dust.

There are lots of reasons for that. You – or your colleagues – might not trust the findings. Your organisation might be resistant to change. Sometimes the promised benefit isn’t worth the disruption, and sometimes there’s not really any clear action to take: it’s “just the way it is…”

None of this is the fault of the data, or the technology. But it stops you achieving the return on investment you expected – and every suggestion or initiative that falls flat undermines HR’s hard-won credibility.

Inertia is cumulative. Every change that isn’t implemented makes future change less likely too.

The problem with people analytics? People.

It’s tempting to think that just having insights into your workforce’s behaviour makes improvement much easier. Here’s the thing some HR tech companies seem reluctant to say: dashboards are not a magic bullet. The truth is, spotting an “actionable insight” is only the beginning of the job.

You still need to work out which insights are worth acting upon, and what you should do about them in the real world. You need to sell the idea – to leadership and, often, to managers throughout your organisation. You need to provide evidence and track the results. You need to deal with real characters, and their real-world concerns.

Getting ROI from your Human Resources technology isn’t really a technology challenge at all. It’s a human one.

Choose your recommendations wisely

When you implement HR analytics, there are a number of things you can do to stack the odds in your favour. The first is to be careful where you start.

If you spot promising-looking patterns in the data, it’s tempting to start highlighting them straight away, to show the value of the analytics as quickly as possible. But some insights are more actionable than others – if the one you’ve identified isn’t sufficiently important, or if there’s no obvious action to take, it could have the opposite effect.

Data analytics can’t usually tell you real-world impact of what you’ve found. For example, imagine you see there’s a 30% chance a given employee will leave; how you respond might depend on other factors about that employee (such as their performance level).

Analytics can tell you the chances, but without context that’s not insight – it’s just information.

Communicate in a way that suits your culture

I remember implementing a recruitment analytics programme at a major financial services company, in the days before anyone in HR had really thought about big data or AI.

For six months, we piloted an AI project to predict who to recruit, based on their application and a series of aptitude tests. 200 hires later, we had doubled the number of successes; absence was down, employees were staying longer, and performance had increased.

The statistics were impressive – and we were excited to report back. But when we showed senior management our findings, they were unmoved. This was a billion-dollar company that had been trading for over 150 years, had more assets under management than Greece, and continually performed well; why fix what isn’t broken?

That mood music dramatically changed when an MD from one of the divisions talked about a customer service agent that had been hired as a direct result of an AI prediction. At first glance, he explained, this was as far from sort of the person they normally hired – they had the wrong background, wrong past experience, lacked the minimum qualifications, and even wore the “wrong” sort of clothes (jeans and a black “The Dude” T-shirt).

But then he heard this maverick employee talking to customers… and witnessed what he described as “the best customer service call I’ve ever heard”.

That recruit became our Customer Service Agent of the Year, and the AI insights became an integral part of the company’s future hiring decisions.

Our statistics were great, but that leader needed to see the real-world impact of the work. And the culture meant that once he was convinced, the organisation followed.

When you’ve found an insight that’s important enough to be actionable, you need to communicate that to the right audience, in a way they’ll understand. Remember, your insight is only important if it’s important to them.

This is often about how you frame the information.

A sales director is more likely to respond to a proposal showing an improvement in Sales Order Values than a reduction in the attrition rate of high performers.

A Medical Director cares more about having six more nurses on the ward than a 0.5% reduction in absenteeism.

A CEO is more likely to notice a 0.5% bottom line improvement than a £250k saving on the training budget.

Collect your data with the outcome in mind

Even with worthwhile insight that’s easy to communicate, many actions fail to happen because the evidence doesn’t seem robust. If a finding seems counter-intuitive, it’s easy to question whether the data’s accurate. That can mean a recommendation gets rejected – or that a good idea is dismissed out of hand.

One way to avoid this is to decide – in advance – what impact is needed to achieve the goals set out in the business strategy and HR strategy, and how to measure that.

All too often, key measures are based on the data that’s currently available, which severely constrains the impact that KPI can have. Switch it around: choose the impact first, then determine which measures are needed – which in turn defines what data you need. If the impact is seen as important enough, data collection and quality can always be overcome.

I’ve hosted many Analytics Strategy workshops with HR and business leaders. My opening line invariably includes something along the lines of: “Today we’re not going to worry about what data we have or don’t have – if it’s important enough to acheive the business goals, we can find a way to collect it.”

This is one of the most cathartic sentiments – it releases the inner business mojo of the leadership. As a result, organisations get the perfect measures to impact outcomes, a series of tight improvement hypotheses, everybody on the same page and most importantly everyone engaged to act.

It’s also important to realise that unlike management information, people analytics doesn’t need 100% accuracy of data. Analytics is about directional trends, and statistically valid exceptions – so a data set missing a few absences here which make 0.1% difference is not important. Generally in-built inaccuracies remain relatively constant. You can still get a good sense of the scale of an issue, and whether it’s getting better or worse, for how long, where and why.

And often, that’s all you need to make a good decision.

Data, technology and people skills, together

I’ve seen many clients transform their businesses by applying people analytics to spot and solve problems, and get a stronger, more engaged workforce. But technology and data can’t do that alone – ultimately, it’s just a smart tool.

Using it correctly also requires the collective expertise of HR and the business – along with a huge dollop of empathy, persistence and patience.

HR system Darwin-ism – the Breeders continue to proliferate

So what ethos is winning the battle between the single unified HR solution versus the multiple “best of breed “ employee systems for HR, recruitment, talent and attendance?  

Well, surprise, surprise, it seems the Breeders still continue to dominate the Unifiers. A recent survey confirms this with over two thirds of organizations having four or more different employee systems covering the various HR specialisms. Good old Gartner and a host of other reputable neigh-sayers are having the same non-purist thoughts.

Why ? Well no doubt Breeders are seduced with generally more functional rich (or should it fit for their needs ?.. ahem) and  quicker / cheaper to deploy systems when compared to the six figure $ costs and complex implementations measured in double digit months rather than single figure weeks.

Will it change ? You’d have thought it was a no brainer based on balancing the conundrum of having just a single supplier with a single system with zero integration versus the complexity of multiple systems, multiple suppliers, multiple integrations and your IT departments’ neurotic negative obsession over “Cloud based systems” with their mystical objections that are randomly thrown up.

Well apparently not – doing the day job more efficiently and effectively still trumps having a couple of extra invoices to process and “The Cloud” being a little damp.

All of which, whilst great for the £ per functionality  and speed of deployment, raises challenges beyond multi-system integration – most importantly in a world where big data is fast becoming the old “new” (come on HR – time to catch up) – how do you combine data from multiple employee systems which is so core to good HR analytics? 

So all you Breeders out there – your proliferation decision was totally justified after all….

What does the most technically advanced car in the world, clean teeth and your workforce have in common ?

As 2020’s Formula 1 season drew to a close, the rather predictable news was that yet again that dispite a massively COVID disrupted season a German team (based in Britain) powered by a German engine (built in Britain) and led by a British driver (not based in Britain) had won 13 of the 17 races. Behind those stats suggesting the rather depressing scenario of domination rather than competition it’s still worth remembering that the 20 cars after driving nearly 2 hours for 200 miles racing at 5 times the speed an average family saloon still crossed the finishing line within a feet of each other.

Imagine that.. still that close even after the ten teams have spent their colossal annual budgets totalling around £2bn.  Consider the variability in the system – tens of thousands staff based around the world with different ideas, different engines, different aerodynamics, different brakes, developed over several years racing on different tracks, different corners with thousands of ways to set a car up, different tyres types and tyre pressures, different pit stop strategies, different racing lines – the list goes on. And the biggest variable of all – the stroppy, egotistical and over-emotional driver eeking out every last inch of advantage out of the delicate £100m car on a narrow and twisting wet track at 200mph.

A hundredth of a second per lap is the difference between winning and losing – so F1 teams are as much in a development race as in the actual race itself. After the initial big improvements have been made – what next ? They have deep pockets to find the smallest performance increments that a faster car is all about the aggregation of marginal gains. Key to this is that the better teams analyse the mass of data collected about performance and environment, then put it in the hands of those geniuses who are able to make important design decisions.

So what’s this got to do with HR ? In the same way as F1, HR provides many of those small increments in improving the workforce for competitive advantage – not just to “win” once or twice, but to keep on winning time and time again. And in the same way, HR measures and tests what incremental benefits an employee (/ skill / training programme / absence policy / and so on) will / has made over the competition.  

But HR are special, right ?

One of the most successful F1 teams has used analysis of data for every single aspect and movement to reduce the time taken for a pit stop (changing all four tyres on the car) reducing from over 5 seconds a few years ago to less than 2 seconds time and time again (next time you have a flat, time yourself) – orchestrating the most precise choreographed dance of twelve burly mechanics kitted out in helmets and heavy fire suits you could ever imagine. The same team was asked by British pharmaceutical giant, GlaxoSmithKline, to reduce time for their own pit stop – swapping over production lines for different toothpastes. The outcome – an increased production of 7 million tubes of toothpaste and an estimated £100m bottom line impact – using data.

No doubt the naysayers will be shaking their heads in exasperation pointing out that it might work for F1 and cleaner teeth (and marketing and sales and operations) but it’s just “not relevant to HR”. Orchestrating a workforce is completely different – more complex, more moving parts, more variability, and requires the human touch. Which in conclusion means gut feel is the only way to go.

The counter-argument – there are many technical challenges to overcome which may seem daunting to adopt an evidence based HR function (all of which are entirely solvable without the need for a £2bn annual budget) – but in the end, the biggest challenge to overcome is the right mind-set to embrace data as a key ally for decision making in the quest for those marginal gains to aggregate.

The aggregation of marginal gains is a concept made famous by British cycling coach Dave Brailsford, who lead the British cycling team to 10 gold medals at the 2008 Beijing Olympics. Prior to this, the team had not managed any significant wins.