Posts tagged ‘measure HR’
My second take-away from the workforce analytics case-studies and conferences I have heard, attended and experience over the last year is what I call the confusion of cost savings and value creation. While the good news is that we are starting to deliver, my warning would be, that we should be careful not to deliver on the wrong things – or more important; on the least value added things. Let me elaborate.
At most conferences and in most reports by leading consultants, we are being presented with a maturity model, which illustrates activities from the least mature to the most. It goes something like this; first there is some descriptive methods, such as reporting and trend analysis, then maturity increases and the methods goes on to being predictive and prescriptive and finally the maturity goes on to machine learning or something like this. One example of such as maturity model is from IBM shown below (but frankly they all look very similar).
I fully agree with the idea of maturity and that prescriptive analysis is better than descriptive. It is also a good way to illustrate this maturity journey albeit they could be a little more operational in terms of assessing level of maturity and suggested next step depending upon current level. However there is one dimension missing from this picture: the focus of the analysis itself. All good at being mature of the methods but we must also assess maturity on the object of our analysis.
In rough terms: If it the focus is on cost savings elements then the potential shareholder creation will always be limited (it will be the net present value of the cost savings minus the investment). If the focus is directly on creating customer value/business value then the potential shareholder creation will be great.
In fact, I will propose, that there is more value added in doing predictive analysis on a business matters than doing prescriptive analysis on an HR matter.
To be clear, let me come with a few examples. If you are analyzing sickness, employee turnover, recruitment effectiveness or training effectiveness, you are really at the cost savings end of the spectrum. There is no harm (at all) in coming up with evidence based suggestions to reducing employee turnover. Indeed for many companies there are significant money to save in doing that. It is however still cost savings and it won’t get you a seat at the table. So do some of that, but don’t put all your efforts there.
At the other end of the spectrum, you are adding workforce data to customer/profit/sales/other business data. Here the examples are less generic as they are (should be) tailored to each company’s specific strategy and situation. A few I have witnessed/been part of: Finding which service behavior adds the most impact to customer experience/satisfaction, and which training programs are most effective in embedding this behavior. In this example, the workforce data leads straight on to more sales and higher profits. Another example; how does change load (employees’ load of change relative to ability to handle change) impact strategy execution.
These two specific examples had a heavy use of non-workforce data as part of the analysis. In fact, you can test your value maturity on the cost/value axis by testing how much business data you have compared with how much workforce data. If you only work with workforce data, you are probably focusing on cost savings rather than value creation.
Some will sometime argue that “Attracting talent is always business critical and therefore what we do is value creating”. That may be true in some cases but they are missing the point. Indirect value creation is important but less straight forward to prove. In most cases they misunderstand HR processes with business matter.
I therefore suggest that we add a dimension to our maturity models. Perhaps some large consultancy company can show how this may look?
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:
- 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.
- 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.
- 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.
- 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.
- 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…
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.
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);
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.
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)
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;
- 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.
- 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.
- 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!
- 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.
Tom Peters gave an excellent advise when he in 1982 said ‘Formula for success: under promise and over deliver’. This goes very much hand-in-hand with the truism in service management (and is HR not one big service organization?) that excellent service = customer perception – customer expectations. The more you raise the expectations the harder it is to deliver excellent service.
My point is; deliver the best product and service possible but just don’t over promise.
The field of Workforce Analytics is in danger of over selling itself. I think stating something like “HR has good data but needs to use them better in order to make better decisions” is a sensible statement and does not over-promise. It is a sort of statement which can lead to a discussion about which decisions we make from pure guess-work (most) and which decisions are so important that we need good data and clever analysis to improve those decisions.
What I do think HR, consultants and vendors should stop saying are things such as:
- “I believe that well thought out predictive HR analytics could become as important to the CEO as the balance sheet and P&L statement,”
- “Workforce Analytics can measure these things [engagement, profit, turnover and customer service] and show a direct, causal relationship to prove the bottom line impact”
- “Workforce analytics will enable us to reach our goal which is to reinvent work”
- “Workforce analytics will provide the language for HR executives to speak the language of business: numbers. This will give HR a seat at the table”
Many of these statements come from people who have a vested interest in the hype surrounding workforce analytics. People such as myself – consultants and vendors. But they are actually doing a disservice to the whole field.
Workforce analytics will contribute a lot over the coming years; it will make use of big data, provide new insights about the workforce and make HR more efficient and effective. But it is not a game-changer. It is not something radically new. And its limitations lies not with the software and the tools themselves – they lie with the mindset of the HR executive, the biases of the people using it and with the level of trust HR has within the organization.
But to get to the place where we can add the value from Workforce Analytics we need to stop over-selling it.