Posts tagged ‘HR analytics’

How important is leadership for business success? Soccer may provide an answer. Workforce Analytics should do the rest

Value of the CEO

How important is leadership really for business success? It is obviously difficult to answer. Perhaps we could start by asking; how important are the top leaders for a company’s success? While more specific, this question is also difficult to give a brief and unambiguous answer to. However, we can begin to approach an answer by starting a completely different place; in the world of football (or soccer to you US readers).

Many sports have for several years been working quite intense with numbers, data and analysis in order to make the best decisions and to prove (or disprove) common myths in sports. Michael Lewis’ fantastic book “Moneyball”, which has been made into a movie with Brat Pitt in the lead role, describes how Billy Beane, the manager of the small team Oakland A’s used figures, facts and analysis to produce results in baseball beyond what one could and should expect. The same has happened to some degree over the past 10 years in football (soccer).

In their equally great book “Soccernomics” by Simon Kuper and Stefan Szymanski, the two authors show that the correlation between the wage expenditure of each club in England compared to average and the average league position is a staggering 91%. This is illustrated in the figure below from the same book. In the conclusion of the full study, the authors says that “the size of the wage bills explained a massive 92% of variation in the league positions, if you took each club’s average for the entire period”. In other words, if you know how much each club spends on salaries compared to the average, you can pretty much predict where in the table the team will finish on average.

Soccer analytics

If 92% of the variation of the league postion is explained alone by the salary it goes without saying that the rest do not mean that much. The rest in football (soccer) is a lot of things like training facilities, the size and quality of the field, tactics, medical staff, fans, coaching staff and of course the manager (which is equivalent to the CEO). Only some 8 variance percentage points are left.

And before the obvious point is raised; you cannot just take a random group of players and double their salaries and then win the league the following season. The high explanatory effect is present because there is a fundamental mechanism at play in international football; you know who the best players are and the best players get the highest pay. Over the last 10 years the transparency level has increase a lot about how good a player is and therefore the salary that player deserves. In the business world, there is also a significant difference between different CEO’s pay but it is more questionable if the best are also the ones who are paid the most and visa versa.

If 92% is explained chiefly by the level of the total remuneration of the squad, you can – with a couple of assumptions – measure how good the managers are relative to each other. Kuper and Szymanski make that analysis by measuring managers are at achieving the positioning in relation to the statistical position that the team should reach based on the total amount of wages they pay. If you take a squad of players who statistically should finish in 6th place, but due to the manager’s motivation, tactics, gut feeling, management and everything else can get the team to end up in 3rd place, then you can conclude that the manager performed better than expected. This analysis shows that there are some football (soccer) managers who have performed on that metric much better than others. These include Bob Paisley, Bobby Robson, Alex Ferguson and Arsene Wenger. In those and other cases, one can statistically prove that the coach has added value, by how much and how consistent.

So to summarize; the manager of a professional football (soccer) club does not matter that much and the game is best explained as Kuper and Szymanski cites Jamie Carragher (a British footballer) for; “The bottom line is this: if you assemble a squad of players with talent and the right attitude and character, you will win more football matches than you lose, no matter how inventive your training sessions, what system you play or what team-talk you make.”. However some managers are significantly better than others and this can be measured and evaluated.

What about manages, leaders and CEO’s of companies? Is the same true as with football (soccer), that the impact of the leadership team has little explanatory power in relation to the overall business performance? That it is ‘just’ about finding the right employees? We must take an evidence-based approach. Which is not without problems.

I believe that there are many reasons why the wage of the employees (or talent as popular lingo is) do not predict a company’s performance to the same extend as is the case in football (soccer). Firstly, the fundamental mechanism around pay is not as efficient in companies as in global sports; the transparency of global talent is lower and it is not always the case that the best person gets paid the most. Performance Management systems are simply not that effective and efficient.  At the same time, I expect that the IT infrastructure, processes, products and brand contributes significantly more to business success than it is the case with a football clubs – although it is probably much lower than business books in general assume.

So the question is how much? How much does a leadership team mean for the business outcome. I would like for Workforce Analytics to come up with the answer to that. We should have the data. We have the software. We have the clever data people. But do we have the insight and the incentive to find the right answer?

09/09/2014 at 13:13 5 comments

How you create a Superhero analytics team

Analytics superhero

Analytics is not easy. Or to be clear; getting the most business value from your data is not easy. There is so much to get right before you can unlock the hidden gems which are unquestionable lying deep inside your databases.

Just consider the journey: First you must work on something which is highly important and valuable to the business. Then you must use all available data to make analysis which gives new insights and knowledge upon which decisions can be made. Finally, and most importantly, you must convince the decision makers to make these decisions based upon your analysis and to do that you must present it right, show them the value, consequences and risk of failure. Only then will your work bear any fruit.

But be careful – don’t assume that decision makers will believe you capable of doing all of that on your own. This reminds me when my daughter was younger, I one day tried to convince her, that I was a superhero. She verged on believing me but when I told her that I could do anything, she said “Now I know you are not telling the truth, dad. Everybody knows that superheros only have one superpower”.

I was not able to fool my daughter and neither will you be able to convince your head of HR or your CEO, that you possess all workforce analytics abilities at expert level.

I therefore propose that you assemble a team for you analytics which has a multiple of superheroes each with a superpower of their own. Specifically I suggest six competencies (in random order);

Analytics HR Team Skills

1. Excellent statistics and numbers skills

There is no getting around that good analytics requires excellent statistics and numbers skills. You can get far with doing simple regression-, factor- and t-test analysis but at other times, you will need skills in more advanced statistics when the data set become really big or you are looking for more predictive analysis.

2. Strong data management skills

Let’s be frank; you will get nowhere in your analytics journey if your data is not clean, good and have a strong governance structure around it. Those and many other data management issues are essential for good analytics. For some it is mundane work, for others it is a passion. If it is the former for you, get somebody on board for whom it is a passion. It is that important.

3. Captivating storyteller

Analytics – even predictive – will only add value if a decision is made on the back of it. It sounds trivial, but data does not speak for itself and to move a decision maker into making a decision you must create a compelling story around it. Sounds easy? For some it is for others it is not. Find somebody who does this well. It will make a big difference to the value of your analysis.

4. Ability to visualize your results

Studies on ex. cognitive load show that if you give a decision makers too much data, they will either not make any decision or make the wrong one. Visualization techniques is a powerful tool to present complex data in a simple and easy-to-understand way. This is not about making your pie charts 3D. It is a whole different category and an art more than a skill.

5. Strong psychological skills

There are so many reasons why I feel that strong psychological skills may be the most essential of all six skills. Just to name two reasons here; it is partly because you will understand how to make more impact with you data if you understand terms such as cognitive dissonance, bias, over-conficence etc. And also because your data has not meaning if you don’t understand how to convert information to knowledge which in essence requires a deep understanding of psychology.

6. Understand the business

A final skill which I find is most often not present as much as it should is the simple but powerful skill of actually understanding the business.  This requires you to fully understand what is the customer value proposition is, what the strategy is (the must win battles), key differentiating factors, financial situation and more. I mean really understand the business.

Analytics require a lot of skills and abilities – superpowers if you like. The best way to ensure that you have the right ones to deliver on your task is to assemble the best team. An analytics superhero team.

02/06/2014 at 11:44 6 comments

In workforce analytics just pretend that correlation is the same as causality

causality

If you read any article, book or blog post about workforce analytics, HR data or evidence based HR you will eventually read the following sentence “…but remember that correlation is not the same as causality”. And this is factually correct. Just because two items correlate does not mean that they are connected or that there is causality.

The problem is that it does not help you with your communication with top management or your recommendations for action. Take this example;

Imagine that you have worked really hard with your analytics project, and you have build a model which shows connections between various training programs and profits. You identify that one particular training program correlate very strongly with profits. You then produce your fine PowerPoint slides and present your findings to the management board. You say “I have identified this strong correlation between this particular program and bottom line profits BUT as you all know, correlation does not mean causality”. The managers look at each other and one say “So what do you say, should we roll this program out to all of our sales people or not?”. You look confused down in your papers and then respond “Well, I can’t actually say for sure that attending this training program will make your sales people produce higher profits or if it is the sales people who are very profitable who happens to attend this particular training program for unknown reasons. The only thing I can say is that the two variables appear to correlate”. The meeting ends and you wonder why your next analytics project don’t get funded.

You see the problem? On one hand you are right. You cannot for sure say which way the arrow turns between the two variables. But on the other hand the management board does not care about correlations (nor should they), they only care about solid and value-added recommendations.

What to do? My advise is; pretend that correlation is the same as causality. Test your hypothesis with qualitative data and present a recommendation based on your findings. So in the above example, interview five sales managers and ask them about their view on the causality; do they believe that the particular training program has led to significant increase in profits from the sales people attending the program? If it has they that’s it. I know it is not ‘correct’ or ‘true’ but it is good enough. And most likely you will be right.

 I know there are a lot of fancy and even simple ways to test the hypothesis with time-lagging-tests or blind-tests or something similar. I have just experience a lot of places where even that kind of tests are not possible, too expensive or not available due to poor data. Make it easy. Do a qualitative test and present your findings as if correlation is the same as causality

 (just don’t tell anybody I told you so – this approach is not looked well upon in analytics circles. Leaders don’t mind – trust me)

06/01/2014 at 22:19 8 comments

Workforce Analytics can add value in two ways (only)

Workforce Analytics Benefits

I think it is fair to say that Workforce Analytics, HR metrics and Big Data in HR is firmly on the map now. It is one of the key trends in global HR right now and everybody’s taking about it. And with some good reason. But before we become like a deer caught in the headlights (with references to the flashy dashboards, shiny conference stands from cloud based HRIS vendors and glossy brochures from data warehouse providers) let’s step back and see how Workforce Analytics actually can add value. This will also help us make our business case a bit stronger rather than the irrelevant “we will have our data at our fingertips”-type of arguments, which really hardly adds any value.

Looking at it from a high-level perspective; Workforce Analytics can add value in two ways; help to make better HR decision and help to make our internal processes better for the benefit of our customers. Let me elaborate on both.

The first one – to make better HR decisions – is obvious but it is probably to many not quite clear what this means in practice. The object here is to either test the efficiency of current programs, to produce knowledge upon which programs/interventions can be made or to predict which programs are more worthwhile implementing.  This can be “what is the most efficient way to reduce talent turnover?”. Or “Which leader profiles have the most engaged employees and why?”. Or “How can we reduce our time-to-hire by 25% and improve our quality of new hires?”. Or “what will our turnover be next year if we implement our new Engage Our Employee program”. Or something along those lines.

The benefits can be significant. For example, If you can identify critical roles and reduce employee turnover in those positions this is worth a lot. Not only in direct cost savings but more so in productivity improvement and competitive advantage in critical areas. This should be enough to make even the most stubborn CEO love you.

But at the end of the day, it is ‘only’ directed at making your HR processes better. Chances are that you already do a lot for your talent and the object for workforce analytics is to make it even better. To many line and functional managers this is only of some relevance. They don’t really care too much for HR anyway. Some even hate HR.  So it is worth also baring in mind the second type of benefits which Workforce Analytics can bring; to add direct business value.

To do this you need to combine workforce data with other data such as customer data, profit data and market data. The questions or hypotheses that you may look into are “Which leadership behaviors correlate most with high customer loyalty?”, “In which positions does employee engagement correlate the most with customer satisfaction?” or “How much will our sales increase if we higher 20% better (and more expensive) sales people?”. These questions are still about employees and leaders (HR stuff) but have their focus on their effect on customers and thus on profits. This is much more relevant to the functional manager who can see the direct impact on earnings. This is still HR stuff but this time he cares about it. As an aside; correlations do not imply causality – but lets stick to the easy stuff to begin with.

The competencies needed to do Workforce Analytics aimed at the two types of benefits are different. The benefits aimed towards the business value requires a more rounded set of competencies. You need to have statistical knowledge, HR knowledge and business knowledge. And the most important is probably the right strategic mindset. Not many – if any – excel in all. The best way to solve this is to build strong teams with complementary competencies to solve issues like this. But it is worth it.

So Workforce Analytics can add value in two ways; to make better HR decisions and to make better business-related decisions. Both are important. Don’t just focus on the HR stuff.

02/12/2013 at 15:41 8 comments

Is HR evidence based? Seriously, are you kidding me?

Is HR Evidence Based - Are you kidding me

The hype surrounding Workforce analytics, metrics and Big Data in HR has really increased over the last 12 months. Every conference, article, blog and strategic initiative is filled with buzz words around data and fact-based HR. So much so that Workforce analytics now is in danger of overselling itself. To outsiders it may appear that HR is becoming more evidence-based in its approach. Unfortunately this is not the case.

When I completed my master in psychology 10 years ago I read a book called “Evidence-Based Practices in Mental Health” by Norcross, Beutler and Levant. It is a great book, which argues for a more evidence-based approach for psychology. Because to be frank, it really isn’t that evidence based. Take an example. If a person has mild depression there are many potential approaches to take. Lets take just a few; therapy (cognitive), medication (ex. Prozac), therapy (behavioral), meditation, physical exercise, therapy (psychodynamic), mindfulness therapy, self-help books and many more. You would think – and hope – that the advise and subsequent treatment this person would get would be based upon evidence. What works. For example, there is a lot of  evidence which suggests that everything else being equal that cognitive behavioral therapy is significantly more effective than both medication and psychodynamic therapy for treating mild depression on its own. But if a patient happens to stumble upon a therapist who focus primarily on psychodynamic therapy – that’s what the patient will get. Psychology is a lot of things, but evidence based it is not.

With HR it is the same. The options we chose and our design of solutions are not based upon evidence but instead on intuition, personal preferences and habits. And often not the right ones.  This is very problematic and with HR there is the added problem that we still don’t even know what works (in medicine and health care some evidence is available). What works and what works best are two questions w cannot answer.

Why is HR not more evidence based? I think there are four reasons;

  1. We don’t share data. There is too little data and evidence out there. Some is being produced but very little shared. A study from 2006 published in American Psychologist, showed that almost three-quarters of researchers who had published a paper in a high-impact psychology journal had not shared their data. This is not just an issue at universities. When I see Google and other leading companies in Workforce Analytics talk about great findings they never share their data. At conference when speakers talk about their great internal studies they never share data. And frankly it is not that sensitive. It really isn’t. I hope they don’t share because they wrongly believe their data is sensitive rather than the studies are really not that good. We must produce better evidence.
  2. Lack of the right competencies. Working with EB-HR mean that you have to understand what evidence is and how to get it. Many in HR wrongly believe that evidence means 100% certainty or proof of something. That is not correct. Evidence is always about probabilities and assumptions. Always. Even in natural science. Also, it is also not just about quantitative data but also includes more fluffy things such as qualitative data (my favorite) and experience. But too few Being able to design and implement a executive leadership program using an evidence based approach is something too few in HR can do.
  3. Lack of the right mindset. As with EB-mental health, many in HR don’t really know why this is important. “We have a talent program, it works, people are happy about it and talents are staying at the company, why should we used another approach to our program?”. While it may sound tempting to think like this – and most in HR do – it is missing the point completely. HR must – as any other support function or organization – be as effective and efficient as possible. The only way it is possible to tell if HR is that is to measure and use evidence to improve. The only way!
  4. We can get away with not being evidence based. Our primary stakeholders (managers, employees and shareholders) do not demand us to be evidence based. We can many times get away with presenting a talent management program with little or no proof that this is the best way to develop talents.

BUT it is not all bad. I think there are many small movements which suggests that the interest is evidence-based HR is growing, our knowledge of what works in HR is also improving, we have the technology to help us find facts, our basic data is better and there are more people with a broader mindset entering HR. Perhaps things will change. But for now please don’t pretend that HR is evidence-based, because it is not.

11/10/2013 at 12:09 12 comments

To succeed with Big Data in HR – start small!

HR Big Data

Big data is a big thing. It promises to revolutionize the way we do business. It also promises to change the face of HR. Big Data will firmly put HR at the table . Or that is the promise at least. But as with anything, it is easy to promise and quite harder to deliver. This certainly seems to be the case with HR Big Data.

My experience is that most companies are struggling with Big Data – getting all the systems to talk together, to clean the data, to get the legal issues sorted, to understand what data to collect, to get the right master data, to avoid bad data and so on. With exception of a few leading companies, most are not even close to getting the basics right.

I attended an excellent conference in London on HR Data last week and was given some fantastic stories and cases from some of the leading thinkers and companies on how to work with Workforce Analytics and HR Big Data. It was really interesting to hear what can be done and how it is applied by the best. But at the same time I was thinking about the realities of most companies and the issues they face.

Big Data offers HR an opportunity to create real insights and to make better HR decisions, which in turn can create real (shareholder) value . HR must not let this opportunity pass it by. At the same time, to the large group of companies who are just now embarking on the journey of Big Data in HR my advise is; approach Big Data in much the same way as you would do if you were to eat an elephant (one bite at a time); one small step at a time. Don’t be too blinded by the opportunities presented by smart software vendors or best in class analytics companies such as Google and IBM. Instead start small.

What does ‘start small’ mean in practice? It means you should

  • clean your data – all credibility is lost if the data is not correct and accepted
  • create a seamless interface between your different data bases – hard but necessary, manually copying and pasting between sources will create mistakes, delays and complicate the process
  • make simple analysis – start with trends and simple correlations
  • share the initial findings, highlight how this leads to better HR decisions and imply the value of these better HR activities
  • collect your learning from the process to take a slightly bigger step next time around

In many ways, workforce analytics and Big Data is not hard. It is actually not rocket science. But starting on predictive analytics before you have got the basics right and before you have earned the trust of the organization will make it more difficult than rocket science.

So my advise for you Big Data journey in HR: start small.

25/03/2013 at 10:39 7 comments

How to tell the difference between good and great HR Analytics – part II

In my last post I argued that great workforce/HR analytics share four common traits; they are

  1. predictive
  2. made on reliable data
  3. combined with qualitative data (and perhaps some intuition)
  4. used an evidence based approach.

But there is one thing missing and probably the most important thing; It must be based upon a strategic approach. I know that”‘strategic” is such an overused word in HR now and frankly most of what is said about strategic is anything but. However there is no getting away from the fact, that you can do good analytics with the above four traits without actually adding much value.  Without doing analytics on the right things AND in the right way it really amounts to very little.

Or to put it in another way: Workforce analytics without a strategic approach will only with the help of luck turn out to be truly value added.

What does strategic approach mean in practice? The best way to illustrate this is to look at the difference between a bottom-up or top-down approach.

Strategic HR Workforce Analytics

Analytics can be bottom-up (operational) or top-down (strategic). The bottom-up approach is the approach many take. They combine their data into Big Data and look through interesting ways of diagnosing, measuring, illustrating, visualizing, trending and reporting the data. They find interesting links between employee turnover and profits (no kidding!), talent profiles and performance or particular training programs and customer satisfaction. That’s when they are good. Sometimes they just show which divisions are experiencing lower employee turnover!

The top-down approach on the other hand start with your HR strategy (which of course is aligned with the business strategy). You then look at which areas you want to focus your HR efforts. Then you find the desired knowledge you require to make the right decision. The you design the data required to provide you with this knowledge.

Good analytics made from a bottom-up approach can give good results; they can surprise you, show links you hadn’t seen before and even challenge your strategy. BUT that approach must not stand alone. The primary use of analytics should be top-down. That’s the strategic approach and that is likely to support you most.

Remember: Workforce Analytics is ‘just’ a tool to make better HR decisions. It is a great tool for that, but if you are an HR executive looking to make strategic HR decisions, your analytics has to be strategic too. Don’t look at the data you’ve got and make the best of them but instead look at what data you need to create the most value-added predictive analytics.

04/01/2013 at 14:17 5 comments

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