New research: Companies with diverse leadership yield higher profits

Diversity

Let’s start with the good news: The conclusion. The companies in Denmark with the most diverse leadership earn on average 12.6 percentage points more than the companies with the least diverse leadership. Not only that, the study also concludes that companies with the most diverse leadership average an operating margin that is 5.7 percentage points higher than their competitors’. On the other hand, the ten companies with the lowest degree of diversity in leadership earn an average of 5 percentage points less than their competitors. The conclusion is clear; diverse leadership yields significantly bigger profits.

 Screen Shot 01-18-16 at 12.28 PMScreen Shot 01-18-16 at 12.28 PM 001

How did we arrive at this conclusion? In the survey, we collected information about 6.012 leaders across 321 large and medium-sized Danish companies in Denmark. We then ranked them by how diverse their leadership is according to four diversity parameters: 1) gender, 2) seniority (meaning length of service within a company), 3) ethnicity and 4) age. We then collected operating profit (EBITDA) data on all the companies.

We got the data from three data sources:

  1. LinkedIn, which was used for collecting diversity data on managers within Danish companies. Almost 1.8 million Danish profiles are registered on LinkedIn, and large and medium-sized enterprises accounted for an exceedingly large share of those profiles. For each company, up to 30 profiles were obtained across management tiers categorized as ‘manager’, ‘director’, ‘VP’, ‘CXO’ and ‘Board’. Companies with fewer than nine profiles were excluded in order to guarantee a statistically valid basis.
  2. Bisnode, which collects a large volumes of business information from official sources such as the Danish register of companies, the Danish Business Authority and Danmarks Statistik.
  3. Annual reports. Finally, we collected financial data and other information from the companies’ own annual reports. 

We have developed a model – Diversity Profit Chain (a modified version of the Service Profit Chain) – which is a robust explanatory model, which demonstrates how internal processes affect employees, customers
and the company bottom line. We have adapted the model so that the focus is on how
diversity in leadership influences these specific factors. The model is shown below. This is important because as you know, correlation is not the same as causality. We have linked other published studies to the model as a way to validate the conclusion of our investigation. Diversity Profit Chain can be used as the basis for any business case for diversity.

Screen Shot 01-18-16 at 12.29 PM

Source: proacteur, 2015

Basically, the measurable and value-creating impact is achieved in two ways:

1) diverse leadership results in a more diverse organisation, which in turn creates a number of positive outcomes for the organisation, customers and shareholders.

2) the management as a group works more innovatively, is more dynamic in its decision-making, more productive and stable if its composition is diverse. In short, leadership decisions and the effect of leadership are better.

As stated earlier, diversity impacts the entire organisation, but not only positively. In general, companies should expect more conflicts in diverse organisations and teams. Diversity is in no respect a one-way track to better financial performance, but the results are undeniable: diverse leadership influences the organisation’s financial performance in a positive direction.

We have always believed that diversity is good for business. Now we have measured and documented that the value added in terms of money actually even bigger than expected.

Download the full report: “A diverse leadership yields higher earnings” here:

18/01/2016 at 15:06 Leave a comment

5 reasons why HR Analytics must sit in HR

HR Analytics in HR

In my last blog post I gave five reasons why HR Analytics should not be located in HR.  The five reasons I gave were: 1) It will lose its strategic potential, 2) HR does not have the capabilities – and will not be able to attract the right ones, 3) HR does not have ownership of all the relevant data, 4) Efficiency gains from pooling all relevant capabilities together and 5) HR is not credible enough to work with analytics.

Fair to say that while some agreed others did not. However, that post was only the prosecutor’s arguments. Now it is the time to ask the defendant to rise and present his side of the argument.

In this blog, I want to present the case from the other side’s point of view: 5 reasons Why HR Analytics must sit in HR:

  1. Nobody else care about HR data. If a central BI or Analytics unit should do all analytics work in a company my guess is that HR issues would be prioritized pretty low. In fact, it is not that many people outside of HR that thinks that engagement data can be used for much more than a pie-chart report once a year. However, in HR we know that engagement can drive business results. We also know of many ways to use analytics on engagement data. It takes an HR mind to see this and to care for this kind of data. Therefore, the more positive re-wording of this point might be that HR data has a lot of potential value; HR people are the only ones who know this and can get the most out of HR data.
  2. It takes HR knowledge to interpret HR data. Data is fine but where it really has an impact is when it is converted to information and later to knowledge. Computers can to some degree make these conversions but at some point they must be converted and interpreted by a human being. This is not easy and interpreting HR data requires HR knowledge. Leadership profiles, performance data, engagement survey data and many other HR data can only be fully utilized if the HR department helps. While a steering group with HR capability may go some way to resolve this problem, I would argue that either the meetings with the steering group must be very frequent or HR Analytics will have to sit in HR to get the required frequent interpretations of data and findings.
  3. It may make HR more data driven and improve HR impact on business. With some exceptions, I think it is fair to say that many HR departments do not have the influence in the organization (I believe) it deserves. There are many reasons for this; lack of strategic focus, difficult to quantify HR’s impact on the business results, a lack of basic business acumen and finally I often hear that HR does not speak the language of business. While I have always disliked the last argument, I hear it a lot. I think it has to do with not being able to make proper business cases, identify the strategic link, provide some kind of evidence to support the suggestions and quantify the value of the initiatives. I also think it is fair to say that HR is not very evidence-based. Do we really use the best knowledge available to design our practices? HR Analytics can help to achieve this to some degree. If used properly this will without doubt in my mind help HR gain credibility and impact on the business.
  4. Data ownership sits naturally in HR. Much of the HR data needed to conduct HR Analytics sits firmly in HR.  It is always a problem if the analytics department does not have easy access to the data required. Many times the HR data is located on different IT platforms than the rest of the business’ and being located near the data makes access easy. HR will also understand the legal aspects of the data much better than anyone else (with the exception of the legal department of course) and this will help the HR Analytics team avoid running into serious legal issues.
  5. It will increase the likelihood of the analytics actually being used. HR Analytics is really about creating knowledge to make better HR decisions. HR decisions are many times made in HR by HR and therefore it makes sense to make the analytics department sit close to the people who are actually going to use it. It will make it more likely that it will be used and have the impact intended.

Did I forget any obvious good reasons to place HR Analytics within HR?

In this and my previous blog I have presented the two cases; 5 reasons for and 5 reasons against placing HR Analytics in HR. In my next blog post, I will discuss the two sides with Peter V.W. Hartmann who is the Lead on HR Analytics at Maersk Drilling and see if we can get to some kind of overall conclusion. So stay tuned…

19/11/2014 at 11:51 10 comments

5 reasons HR Analytics should not be located in HR

Simple organizational structure

If you were to build an HR analytics department where would you put it in your organization? The obvious answer may at first be to put it in HR. But at second glance this may not be the right place. Maybe even the wrong place.

I believe there are five good reasons why HR Analytics (or Workforce Analytics or HR Data or whatever it may be called) should indeed not be placed within the HR department.

  1. It will lose its strategic potential. It is common knowledge that HR Analytics has far greater potential if it is directed towards strategic issues rather than operational and tactical ones.  To put HR analytics in HR will create two problems. The first is that I often see HR Analytics focus on improving HR processes only. Nothing wrong with showing the link between engagement and employee turnover rates, it is just not very strategic. Nothing wrong with improving the training programs with analytics – it is just not very strategic. Tactical at best but most often operational. The strategic mindset is often not present. The second reason is that HR is mainly strategic when it is working outside of HR’s own silo and instead fronting the business and the front line. Some even argue that it is not HR’s role to be stategic. Putting HR Analytics deep inside some (often random) HR function makes the decision making process far removed from the business – and hence strategic – side of matters.
  2. HR does not have the capabilities – and will not be able to attract the right ones. I have argued that to succeed with any kind of analytics you will need to attract a number of different capabilities to take charge of your analytics effort. These capabilities, competencies and skills include i) being excellent  at statistics and numbers, ii) strong data management skills, iii)) captivating storytelling skills, iv) visualization skills, v) strong psychological skills to understand terms such as bias and heuristics, and vi) the ability to truly understand the business. Very few in HR master these and fewer yet have a strong team which complement each other enough to be able to build the right team. Likely they will fall short on data management skills, visualization skills, statistics and understanding the business. And worse, those people are not looking at HR job ads. Trust me.
  3. HR does not have ownership of all the relevant data. Many of the data which HR has ownership of (recruitment data, performance data, engagement data etc.) needs to be combined with data which reside in other parts of the organization such as finance (payroll), IT, legal and most importantly all the customer related data. These may also just be basic things such as master data. Many times HR Analytics people do want to work on strategic matters but need data which they are not allowed access to for internal reasons. In my experience these are often customer and profit data. If HR Analytics resides in a function outside of HR these data may be more available.
  4. Efficiency gains. Purely from an economic and efficiency point of view, it does not make sense to have several small teams scattered around the organization trying essentially to do the same thing; namely to do analytics. Instead pull the people together, let them build on each other’s experience and competencies, save money and efficiency by having one big analytics department instead of one in HR, marketing, IT, business units and where else they may be.
  5. Credibility issues. If HR has not been using data well in the past but instead has created and submitted poor business plans made more by gut feel than by use of good data, and if HR has produced endless of meaningless data reports on absenteeism, employees turnover, sick-days and percentage of women in workforce with questionable data quality, and if HR generally argues by what feels right rather than what is right, then the quality of the analytics work will be called into question just simply because it comes from HR.  HR does not have the credibility to work and argue with data. It may require someone from finance or business intelligence to produce and conclude the HR analytics for it to be taken serious.

If you take this perspective where should you then place the HR Analytics people? One approach would be to create a central business intelligence unit where all the company’s analytics will take place. Another approach is to create one or more center of excellence so much of the BI capability can be decentralized to the business units. And there are many other ways.

But maybe HR Analytics should not be placed in HR.

11/11/2014 at 09:23 12 comments

Beware: HR Analytics leads to overconfidence

Overconfidence is a term used in psychology to describe a person bias for being right. The overconfidence effect is a well-established bias and describes a person’s subjective confidence in his or her judgments is reliably greater than the objective accuracy of those judgments, especially when confidence is relatively high.

Daniel Kahneman writes in his epic book ‘thinking fast and slow’ that “neither the quantity nor the quality of the evidence count for much in subjective confidence. The confidence that individuals have in their beliefs depends mostly on the quality of the story they can tell about what they see, even if they see little…our associative system tends to settle on a coherent pattern of activation and suppress doubts and ambiguity”. What this means in practical terms is that one would assume that

How is this relevant to HR Analytics you may ask? In many important ways. Imagine that you present a fantastic ‘Predictive Employee Turnover Analysis’ to your boss who is the head of HR. You have collected a ton of data, used complex algorithms and found the following conclusion: female leaders at level 3 and 4 who have been performing among the 10% best leaders (at those levels) for three straight years and have not received a promotion are 60% likely to leave the organization within one year. Wow. And based upon this analysis you suggest a targeted effort towards that group of leaders. You calculate that this effort alone will save the organization more than $3m a year.

Your leader is truly impressed and now ask you how confident you are that your result is correct. That this group of employees are that likely of leaving and that they effort will save that much money. This is a fair question because your boss has to make a decision based in part on your findings. What do you respond?

According to the theory of the overconfidence effect, you are likely to say a very high number. Perhaps even 90%. In any case, the number will be much higher than an objective accuracy of likelihood. Frankly this number is much lower than we think it is. And this is a problem because your boss then makes a decision based on wrong input from you. If he thinks you are right, he will underestimate the risk of the project – the risk of being wrong – hence estimating a too high ROI.

Why will you make a higher estimate of the correctness of your finding? According to the theory and what I have experienced many times in reality is that because when we work with data over a stretch of time, as such a project mentioned above would imply, we are slowly developing a story in our minds. A working hypothesis if you will. And slowly we are only finding data which supports our hypothesis. This is a natural inclination. We confirm our hypothesis when we work with data.

The solution to this problem, which is quite a significant problem; always have a steering group for your projects with many types of people, continuously present your findings and assign someone to be the devil’s advocate and make it that persons responsibility to ask the stupid questions and challenge your beliefs and hypothesis’.

Overconfidence is by the way only one of many biases we carry with us and which work against us. A couple of others such as cognitive dissonance, illusory correlation, availability and anchoring. They are subtle but important and possible devastating to good analytics.

My main message here is as follows; we would like to believe that because we work with data and facts we then automatically make decisions that are more rational. We do not. We have a fantastic ability as human beings to disregard facts and make decisions based on irrational biases. If we can overcome this natural tendency then working with data and facts will make our decisions so much better. Exponentially better. But we must continue to work on our psychology.

21/10/2014 at 14:32 7 comments

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

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