Posts tagged ‘hr data’
Storytelling is rightly hailed as a must-have competence in people analytics. In my own competency model, it is one of the six core competencies any analytics team must have. Other models do the same. Compelling arguments are being made about the value of good storytelling. In other words; master it or beat it.
So don’t get me wrong; it is important. But my point in this post is that storytelling requires the presence of a theory to be successful. If you do not have a proper – i.e. a plausible and documented – theory behind your data, storytelling can do more harm than good.
Angela Duckworth observes in her book: Grit – the power of passion and perseverance, that “a theory is an explanation. A theory takes a blizzard of facts and observations and explains, in the most basic terms, what the heck is going on”. I could not have put it better myself. And funnily enough, this is also what storytelling is doing – explaining what the data says.
Let me give you an example why you need a theory to tell a story: ZengerFolkman – an excellent US data-driven leadership development consultancy company – has compared the combined leadership effectiveness scores as measured on 360-degree evaluations for men and women respectively at different leadership levels. The result is, as you can see below, that women score better than men at all levels and that this difference is more significant the more senior the leaders are.
I recently made the same observation within a financial institution. They had collected performance data for all their leaders and we were comparing performance data – split into different KPI groups – and it was clear that the performance rating was significantly better for the female leaders and also that difference was greater the more senior the leaders were. The data at this company confirmed the international data I had found. I had data and I had other similar data points to back them up.
So far so good.
The problem is, that although the difference between performance scores is significant the data makes little sense without a theory to explain the observations. Why are women leaders rated better than men? All we know is that the performance ratings/360-degree evaluations put women higher than men. It may be that women are better leaders than men. It could also be that women are reported to be better leaders but in reality are on par with men. Maybe there is a bias in the evaluation of female leaders. Or it could be a third reason.
Another thing to consider is the relationship between the portion of female to male leaders vs. overall performance. Is it linear or does it have another shape as depicted in the figure below? If it is linear and you conclude that females are better than male leaders, then a natural recommendation is that you should replace all male leaders with female. If on the other hand the relationship has some other shape – such as the one in the second figure below – you should identify the optimal point to reach leadership effectiveness.
My point is that without an answer as to why there is a difference you cannot create a story and a recommendation. To come up with a proper recommendation you must have a proper theory to explain the why. The basic analysis cannot explain it and you cannot go straight to storytelling because you are still left with the basic question of ‘why’. And what you will be left with are leaders sitting around a table wondering what to do. In this case, maybe there is a good theory. I don’t know of it (but would love to hear it if you happen to have one).
So you need a theory behind your data. An explanation if you will. It does not need to be verified by Harvard or any such institution. But you do need an explanation. Let’s say that you find that the talent you source from one university performs significantly better than the talent you source from another. You need to understand why. If you cannot explain why through a theory, your storytelling will lack the power it has the potential to have.
So: please do not do storytelling on people analytics without a proper theory explaining your data. It really makes no sense.
Over the last year, I have with interest read and heard a lot of workforce analytics case-studies both at conferences, in network groups and in companies by practitioners. And I find myself hearing good news. I believe, we are as a profession starting to deliver on our promises and heading towards a brighter future. Let me elaborate.
Two years ago, Workforce Analytics was in my view in grave danger of over promising and under delivering. And that is a recipe for failure and extinction. At conferences, Google and other large wealthy US-based companies were showcased as the promised land and of what to come in the near future of HR Data and Workforce Analytics. Airtime was primarily given to these companies and to vendors who were trying to over-sell software capabilities and the picture drawn was that not only was workforce analytics adding a lot of value, it would finally bring HR to the promised land: To the table.
But reality was far from that picture. Bad data, incompatible software, data illiteracy and a lack of a data-based strategic mindset was rather the norm than the exception. And worse, it left practitioners disillusioned when leaving conferences as they knew that their maturity level was far from what had been presented at the shiny conference.
Two years later and things look different. The promises made at conferences, in case studies and at presentations within companies are more realistic. Workforce analytics will not radically change HR – what it hopefully will is to improve the decisions we make in HR. And what’s better, we are starting to deliver. The data has been cleaned (somewhat), the software has been installed (to some degree) and is working (sometimes) and a data mindset is creeping in within most HR functions now. Armed with this, real projects with tangible benefits are starting to show. Nothing major. Nothing fancy. Just credible analytics projects delivered by HR to the business.
So having been a bit of a worried pessimist on behalf of the workforce analytics community, I now find myself being optimistic. If we continue to promise less and deliver more, who knows, perhaps the business will start to listen.
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.
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:
- 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.
- 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.
- 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.
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:
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.
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:
- 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?
- 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?
- 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.
- 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.
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.
I have met many who question if HR analytics, HR data and HR reporting should be located in the HR department. People with this view generally have one of three arguments. The first group is some vendors. They say that whenever they try to sell their software/solution to a company, the HR department don’t really ‘get it’. They see a piece of software and refer to the IT department. In the mind of this first group, HR analytics should be part of IT because they understand how software can support the business and the need for and challenges with integrating software across a business.
The second group is concerned with the mindset of HR. They also don’t believe that HR ‘get it’ but they are concerned with it from a strategic point of view. They see HR as being primarily operationally driven and they believe that HR data and HR analytics require a strategic mindset. They either hope that HR become more strategic or hope that HR data will be located anywhere but in HR.
Finally, some believe that it is better to have a central data department, where all analytics should take place. The argument is that by centralizing and getting the best brains in the same room, better analytics will be the result. They don’t really have a view on HR, HR analytics should just not be part of it. They also see many advantages from having a central department other than optimizing brain capacity.
I freely admit that my own view is that HR analytics should definitely be part of HR. I simply don’t buy any of the above arguments. Not only that, I believe that by having HR analytics as a central part of HR, it will make HR more potent, more strategic and more business orientated. So HR must take control of HR data.
The first step to do this is to take ownership of the Master Data. Sounds simple – and actually is a bit common sense – but many times HR master data is not owned by HR and this is a major problem.
Master data is essentially basic data about employees which can be used in single applications, systems and processes. Examples include name (first and last name), position, reference (who is your boss), social security number, dob, address, telephone numbers (internal and external) etc. Master data supports other reporting processes and for example HR analysis is greatly dependent on an organization’s master data.
The problem is, that master data about employees are in many cases not located in HR. They may be in payrolls (which may or may not be a part of HR, but is often a silo-function within HR or finance) or in IT. So basic statistic about turnover, absenteeism, performance rating, competence map etc. can only be liked to people and positions if you control the master data. These HR departments who don’t control these data have to go and ask for this data in order to run their reports. But by not having the actual ownership you cannot control the quality – and bad data results.
To build great HR analytics, you must have great HR master data and to have great master data you must have ownership of them. So HR: It is time to take ownership of your master data.