Posts tagged ‘measure HR’

The number one reason why HR is still an art

Is HR a science or an art? You might think that with the advent of Workforce Analytics, Performance Management, tests in recruitment, ROI measurement and much more that HR is becoming more of a science than an art.

I am afraid not!

One of the problems is that HR fundamentally is based on studies done within psychology; social-, cognitive- , personal- , clinical-, and Industrial psychology. Most of theories about Performance Management, Talent Management, Leadership and Employee Development, Recruitment and more originates from psychology. Nothing wrong with that. Being a psychologist myself, I think the profession has a lot to offer HR and the art of managing people in general. But despite its best efforts, psychology is still far from being anything resembling science. It is fundamentally an art.

I recently read a great article, which compared different fields of study, and it found that psychology tends only to publish ‘positive’ studies i.e. those that supports the tested hypothesis. Why is that a problem? The problem is, that most of the studies cannot be replicated and those which can aren’t. So if somebody publish a research piece which claims to show that “you can improve job satisfaction by coaching your employees instead of telling them what to do” this claim will go completely untested. It doesn’t mean that it isn’t true, it just means that nobody will test it and correct all the stuff which really isn’t true.

You may argue that all fields of science does this. Only to some extend and certainly not as much as psychology (see the table below). I encourage an evidence based approach to HR. A lot. But if the evidence we can find is bad what then?  Consider this:  In 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.

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HR is full of claims of what works and some (though not many) draw on academic studies from psychology and related fields. Much of it is contradictory, much has no foundation in real research (produced by consultants or vendors) and the rest goes untested by others. We need to change this.

HR Data, Workforce Analytics and ROI measures will do what it can to make HR more science based. I actually think that is a good thing. We are still taking about people and therefore you cannot make HR a complete science. But for my worth, I think it is a good thing, that it is moving a bit more in the direction of a science. BUT this will not happen until psychology as a field will mature. One way is to test and challenge the results and look into the data upon which research is made.

So let me end on a positive note: HR has the potential to be the thing that impact companies and society the most over the next 40 years. As Peter Drucker once remarked then our growth over the past 50 years has been our ability to increase productivity through machines. The challenge for the next 50 years is to do the same with people. I believe HR can do this. To help us, we need better data, research and science.

24/06/2013 at 16:10 6 comments

There is no best Talent Management KPI

Imagine your CEO asks you to come up with one KPI he can track to evaluate if your talent management program is successful. Which talent management KPI will you choose?

The example may be hypothetical, but not unrealistic.  I know HR executives who have five or seven KPI’s which they discuss with their CEO once a month. One may be on recruitment and one may be on talent management.  So if you had to choose one for talent management, which one would it be?

When I talk about one KPI, I don’t mean to say that I think the success of a talent management program can or should be measured by one KPI. Instead I think such a program should be measured by 3-5 KPI’s.  No more than that – measuring HR should be simple – but also no fewer than that.  But I have experience HR executives who are forced to pick one.

I do believe that you can find one which is the best for you. That is the good news. The bad news is that unfortunately there is not a single generic KPI you can just copy-and-paste. It simply doesn’t work like that. BUT there is a KPI which is best for you.

Just as an aside, if you are looking for the five best generic talent management KPI’s, you can find them here.

Since I cannot offer you the one best talent management KPI, let me instead offer you the process through which you can find it. It is a fairly generic process and you can therefore use it on all types of programs. But will all generic processes; the value is not in the process design itself but in how you conduct the process and what content you bring to it.

It is a four step process:

  1. Identify what problem the talent management program is trying to solve. This is the purpose of the program. Although all programs are talent management programs, they are trying to achieve different things. Some focus on attracting talent, some on retention of talent and some on development and deployment of talent. There is no right or wrong, but which is more important to you?
  2. Imagine that you have implemented your program successfully and it has achieved its purpose, how do you know? What objective, tangible, measurable things have changed? Is it behavioral, attitude or more financial things which have changed? Which one matters most?
  3. Identify what data you have – or can get – to track the program. Most organizations suffer from bad data, wrong data or simply difficult-to-obtain-data. Ignore all of that data. Find the few data that you really need and focus your effort on that. Don’t sweat the small stuff.
  4. Define the KPI clearly. It must track the ultimate purpose of the program as well as being easy to monitor and understand. Formulating a KPI is not difficult, but you should follow these best practice steps when formulating it. Most organizations use KPI’s extensively but for most they don’t do what they are meant to do – help you make HR better. They use bad KPI’s

If you follow this simple process you are likely to come up with the one KPI which you can show to your CEO. He (she) will thank you for it.

03/04/2013 at 15:20 2 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

In defense of HR Best Practice

The concept of ‘best practice’ is so yesterday. So I am told. Although I fully understand where the critics are coming from, I think they are too negative on the concept. Let me expand…

Yes, ‘best practice’ has come under some criticism lately and quite frankly also with some justification. It is impossible to go to a conference, read a white paper or just look through the blogosphere today without being overdosed with best practice by consultancy companies (such as yours truly), who are trying to make their products and services sound better than it really is or are selling it to their customers as ‘this is how all the best are doing it and see how much money they are making’.

Jane Watson formulates it well when she says:

“Best practice” has become a largely meaningless label an individual applies to one or more business practices that they hold in high regard; practices that they, or their industry or profession, consider to be ‘best’, presumably in comparison to other practices previously or currently in use. There might be theoretical underpinnings or research that illustrate the efficacy of these practices, but quite frequently there is not. It seems to me that in cases where such supporting data is lacking, the evidence used to justify the labeling of a practice as ‘best’ is the degree to which it is popular amongst similar organizations, especially if those organizations are successful (e.g. profitable, recognized and positively viewed). Often these practices are advanced as ‘best’ by the very organizations that employ them, or by consultants, thought leaders or professional bodies that champion the adoption of the practices in question. Given these questionable motives, it can be difficult, I think, to assess whether a ‘best practice’ is effective, or simply the latest craze.”

Spot on Jane. I totally agree.

As I see it, best practice can be criticized from five angels:

  1. Lack of evidence. Frankly most of the so called Best Practice are hailed as such without any real evidence, research or anything substantial to back it up.
  2. You will not be better than your competitors. By adopting best practice (assuming it is), you will still not be better than your competitors. Indeed all you are doing are imitating them and probably doing it worse than them.
  3. Lack of context. Because something is working in a young start-up in Sillicon Valley does not mean it will work in your old mid-western production company.
  4. Based on (very) few cases. It appears that a best practice often is the result of one or two companies doing something which works for them. Also, it seems like it is the same few cases which are doing the rounds.
  5. Illusion of simplicity. Reading best practice cases – such as Zappos and Google – it gives the illusion that it is actually quite simple to replicate. What they do is smart and easy to do. Wrong. The best practice cases never seem to capture how long time and how much effort it has taken to make it work.

BUT BUT BUT wait a minute before you discard best practice all together. It is easy to criticize but instead of saying that we can’t use best practice cases at all we should recognize it for what it is (and importantly also for what it is not) and then use them intelligently.

There is nothing wrong with listening to what others do, be inspired by it, adopt it to your particular context and use it how you see fit.   Let me illustrate my thinking:

15 years ago I decided to run a marathon. I didn’t know how to prepare for such an event as I had never run long distance before. I decided to buy a book. It was written by someone, who had completed more than 50 marathons. In the book he gave details about nutrition, running program, do’s and don’ts, equipment and advice on what to expect. As I was travelling quite a lot at the time, I had to adapt the training program significantly. Also much of what he suggested I should eat was not easy to prepare while travelling, so I had to adapt that as well. I know there are many ways to prepare for a marathon and his was only one way. Indeed many successful running experts may even had disagreed with some of his advice. Who knows. Also, he was not the best in the world. His fastest time would not have made the top 50 in the world. But he was pretty good and certainly better than I was (and still am). But I learned a lot from the book. I improved and most importantly I completed the marathon. Along the way, friends gave me advice which contradicted the advice in the book but by and large I stuck to his advice.

I guess my point is: don’t think that best practice is the only or even the best way to do something. It is not. And what works for one company will most definitely not work in exactly the same way for another. But best practice cases are about companies and people who have done something with success and are passing on some key learning points. Take those learning points. They can be a source of inspiration. Listen to it, adapt it, use your common sense and see what you can learn from it. There may be value in best practice after all.

26/02/2013 at 16:50 6 comments

The Top 5 Posts of 2012 from the ‘All About Human Capital’-Blog

It’s been a fantastic and fun year writing blog posts on this blog and in honor of the New Year, I’d like to share the most popular posts to my readers over the past year.

Here are the top 10 posts in terms of views and re-tweets from this blog for 2012, enjoy:

5: The challenge for HR analytics is not data – it is the mindset

The software is good, the people are bright – it is the strategic mindset around data which is the challenge.

4: Why HR KPIs still matter but why they still fail to deliver

KPI’s are criticized but they still matter – you must however follow theses rules of thumb when using them.

3: HR KPIs: The good, the bad and the ugly

KPI’s actually work most of the time. If you measure people and you link it to their pay they will in most cases try to meet these goals. Bad KPI’s therefore do more harm than good.

2: Top 5 Talent Management KPIs

A list of the five most important and strategic Talent Management KPI’s

1: Cognitive dissonance and HR Analytics is a bad cocktail

The most popular post was about psychology and HR Data – how cognitive dissonance will create a bias for a certain decision despite facts and evidence may favor the alternative.

02/01/2013 at 09:07 3 comments

How to tell the difference between good and great HR analytics – part 1

Great HR Analytics

Question: What is the difference between good and bad analytics?

The answer is probably best illustrated like this;

This is bad analytics:
You have received the latest performance management data from all five divisions. You know from experience that the data is questionable – in two of the divisions many of the inputs are the default settings. You also hear that the managers have a somewhat relaxed attitude towards the accuracy of the data (to say the least). Your report show that absenteeism is flat at a reasonable level. You report this with satisfaction.

This is ok analytics:
You get the latest voluntary employee turnover data and see that the figures are trending upwards. The level is above the 6 month moving average. You dig deeper into the data and see that it is in particular in three division the employee turnover has been higher than expected. These division have three things in common; a new leader has been employed, workload has increased and they are client facing. You contact the relevant HR partner and suggest that they implement the usual retention initiatives.

This is great analytics:
You get the latest voluntary employee turnover data and see that the figures are trending upwards. The level is above the 6 month moving average and 2 %-point higher than sector-adjusted benchmark. You dig deeper into the data and see that it is in particular in three division the employee turnover has been higher than expected. These division have three things in common; a new leader has been employed, workload has increased and they are client facing. You then decide to interview relevant people in those divisions including leaders and employees as well as the HR partners. You read relevant academic research and case studies on effective measures against voluntary turnover in your particular sector and discuss this with experienced managers and HR partners within your company. You suggest three actions; coaching for the leaders, competency profiling to match job demands and team building. Your data suggest that these three initiatives will reduce the voluntary employee turnover from the current rate to 1 %-point below the benchmark at a ROI of 55%

 So what is the difference? Great analytics

 Any other suggestions to differences between bad and great analytics?

14/12/2012 at 15:42 10 comments

This is what you will see in your Big Data

Confirmation Bias in HR

Data is very ambiguous. It often doesn’t give you a clear answer to the question; “What should I do?”, which is really the question you hope that Big HR Data will help you answer. To get to that point, you must take your data and convert it into knowledge. The question is; what will you see when you stare at the data? The answer is clear but not very encouraging.

I have previously argued that psychology has a lot to offer us in our understanding of how we work with data. This is also the case when we need to understand what we look at Big Data. Big Data is a much hyped term which essentially just refers to a lot of data – a lot of data in terms of volume, variety and velocity. But a lot of data it is and you therefore need to cut and slice it. You need to choose to pay attention to some data over other.

The psychological concept of confirmation bias can help us understand what we see in Big Data. Confirmation bias refers to the fact that people will search for, pay attention to and interpret data in a way which supports their own preexisting view. And conversely, people has a tendency to undervalue or look away from data which conflicts with their existing view.

In short, when you are looking at a lot of data, you will focus on the data which supports your view and ignore the data which contradicts your view. Hence, you will find data that shows that your project has been a success, that you do add value and that your decisions were right. Funny that.

Confirmation bias has been used to explain other bias effects such as cognitive dissonance (where people continue to hold their beliefs despite evidence showing the opposite) and illusory correlations (the belief that two variables are associated with one another when little or no actual association exists).

Several studies supports the confirmation bias theory. An interesting one [described here] took place during the 2004 US presidential election and involved a group of people who had described themselves as having strong feelings about one of the candidates. They were shown apparently contradictory pairs of statements, either from Republican candidate (George W. Bush), the Democratic candidate (John Kerry) or a politically neutral public figure. They were also given further statements that made the apparent contradiction seem reasonable. From these three pieces of information, they had to decide whether or not each individual’s statements were inconsistent. There were strong differences in these evaluations, with subjects much more likely to interpret statements by the candidate they opposed as contradictory.

In this experiment, the subjects made their judgments while in a magnetic resonance imaging (MRI) scanner which monitored their brain activity. As subjects evaluated contradictory statements by their favored candidate, emotional centers of their brains were aroused. This did not happen with the statements by the other figures. The experimenters inferred that the different responses to the statements were not due to passive reasoning errors. Instead, the subjects were actively reducing the cognitive dissonance induced by reading about their favored candidate’s irrational or hypocritical behavior.

This is important for HR analytics and HR Data (or Big Data) for several reasons. First, if analytics is supposed to help us make better HR decisions, confirmation bias will instead make us make the same decisions just with better arguments. That’s not good. Second, if analytics is a way for HR to argue its case more credibly up in the organization the credibility will be shattered if there is a sense that confirmation bias has been prevalent in the analytics stage.

While psychologist have been good at identifying this behavior and making studies to prove it they still have to come up with a great way to overcome it.  But in practice what you need to do is:

  1. Be aware of your existing bias, hypothesis and view.
  2. Seek information which challenges specific biases. For example, assign a person to play the devils advocate during meetings. See as many objective data to contradict questionable biases.
  3. Reflect on the information which challenges your view.
  4. Incorporate the contradictory view into your existing hypothesis and test it

Like any four-step program this sounds really easy but is difficult to practice. Especially because  it is a lot of hassle and easy to ignore. But I fear that confirmation bias may stand in the way for unleashing the true value of Big Data in HR.

04/12/2012 at 17:02 7 comments

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