This is why psychological knowledge is essential to success with People Analytics
I am often asked why I rate psychological skills so essential to a great People Analytics team. Indeed, why I rate them higher than statistical skills, when I look at core skills in a SuperHero Team.
Let me give an example:
Let’s say that you produce an algorithm/robot/AI, which is perfect for recruitment; it can predict the best possible match between a candidate and a position including all elements of team composition, company culture and job description. The algorithm is bias-free, cheap to use and based upon company specific data, meaning that all sorts of BigData has been collected, analysed and optimized to predict match and success for your company. Not only that, it does not require any type of interaction with people. The candidate is sitting in front of a camera at home, and with use of face recognition abilities, optimised open-ended questions and random multiple choice questions during the interview can scan the candidate within 15 minutes, which reduces waste of time for the candidate and the company thus reducing the elusive time-to-fill KPI.
The situation is perfect; easy, cheap and accurate recruitment, which can be done at the convenience of the candidate.
None of this requires psychological skills, it can be done with great data, innovation and statistical nouns. So what is the problem?
To find a problem with the perfect algorithm described above, we need to visit a well-known finding within social psychology; effort justification, which states that we tend to attribute a higher value to an outcome, when we have gone through a great deal of effort into achieving. Effort justification is a unconscious bias based on the classic work by Leon Festinger on Cognitive Dissonance, which states that when people’s behaviours and beliefs don’t align, they experience discomfort. To relieve that discomfort, people often change their beliefs to match their behaviour. The theory of effort justification was formulated though the research of Elliot Aronson and Judson Mills, who concluded that “persons who go through a great deal of trouble or pain to attain something tend to value it more highly than persons who attain the same thing with a minimum of effort”. Interesting. In their research (dating back from 1959) they saw that their participants rated the groups they joined more interesting and valuable if the access to the group was harder even though the groups were identical.
A good example outside of organizational life is probably during “Hell Week” held each year on college campuses across US. Here young students make their fraternity pledges through a variety of activities some of which includes social embarrassment and sometimes physical pain. Why do young people go through such a recruitment processes? Simply because that the enduring effort makes it so much more desirable to enter.
To look within the corporate world, just look at the alternative route taken by Zappos who invented something called “The Offer“, which is where they say to their newest employees “If you quit today, we will pay you for the amount of time you’ve worked, plus we will offer you a $1,000 bonus.” Zappos actually bribes its new employees to quit! Why does that work? Simply because people who stay have gone through an extra level of ‘pain’ (by giving up $1,000) and hence value Zappos higher.
I am not saying that you should not build the perfect algorithm. In fact, I am saying that you should. But instead of just releasing it as an easy, convenient and efficient way to hire, I am suggesting that the true value of the algorithm is only realised with the aid of (social) psychology insights which states that you should find ways to make it really hard to be recruited.
How can you make it feel like it is really hard to be recruited if you have the perfect algorithm where you can make the best possible decision within minutes? I don’t know and would love to hear your view, but my immediate answer with to use an assessment center, where more people are gathered and competing for the same jobs with an algorithm ultimately deciding the outcome. Maybe that would work? Or perhaps used more innovative ways as Zappos did. In any case, so solve this riddle requires more that data knowledge and is why your analytics team needs to be multi-talented.
References:
- Aronson and Mills (1959) The effect of severity of initiation on liking for a group, Journal of Abnormal and Social Psychology, 59, 177-181.
- Festinger, L. (1957). A theory of cognitive dissonance. Evanston: Row, Peterson.
Why People Analytics and Change Management is a match made in heaven
People Analytics is maturing fast. 2017 in particular was a stellar year if the published cases, presentations at various people analytics conferences and interest from the wider HR community is anything to go by. However, for this trend to take a more permanent hold, it must in my view be recognized as an area of importance across the wider organisation, something it is not now.
A solution to this is to look in another direction – a new avenue.
A third avenue
I see people analytics applied in primarily two areas; 1) making better (evidence-based) HR decisions and 2) solving business issues. The first includes a wide range of things from improving on internal people reporting and scoreboards, engagement surveys to creating predictive models for turnover, talent performance, recruitment success or assessing leadership training and innovation processes. The second area uses people data to answer questions such as “How to we sell more widgets?”, “What engagement activities creates the best service delivery which impacts customer retention the most?” or something like this.
But there are challenges with both, which makes it difficult for many People Analytics departments/teams/units in many organisations. The challenge with the first type is that HR processes in themselves only creates limited measurable value – or is not perceived to create a lot of value. The challenge with the second is, that there are only few business issues where people analytics currently is used either due to lack of relevant data to answer the questions, that HR/People Analytics they are not invited to contribute with an answer or that the issue is not people related. In any case, while on one hand there are many great People Analytics projects, studies, examples out there and the size and quality is increasing a lot and on the other hand the impact across the organisation is still relatively low.
I believe there is a third avenue for People Analytics to add value, namely by working closely with Change Management. Change management are in many mature organisations (project wise) an integrated part of the most strategic projects but are in need of help.
What is Change Management?
Change Management is a structured process with a specific set of tools to handle the people side of change and is an essential part of making projects succeed; project management delivers the solution on time and budget and change management makes the organisation ready to embrace and use the solution. But whereas project management is a well-established discipline with industry standards with well adopted tools and best practice, Change Management is still relatively young and are still looking for ways to improve.
Because most organisations are executing their most critical strategic initiatives – or Must Win Battles – as projects, and a large part of the potential success of those projects lie in successful Change Management most companies are looking for ways to make Change Management better. People Analytics can play a major role in delivering excellent Change Management and hence directly impact the successful implementation of strategies within an organisation. This is in my view an under-utilised avenue for People Analytics to affect business results.
How can People Analytics support Change Management?
There are many concrete ways HR data and People Analytics can support Change Management; engagement data can help identify likely resistance, the new generation of real-time employee sentiment tools can help assist in the implementation phase, network models can identify useful change agents/ambassadors, effective evaluation of effectiveness of training classes, predictive models for adoption usage, effective dashboards for usage of the new solution and many more. I believe the list is long. In a recent Harvard article, social media analytics was used as an example of HR data usage in change management.
It is a win-win really; Change Management need more data to assess risks, progress, adoption and usage and People Analytics needs to apply its findings find more value. A partnership between the two functions will instantly add measurable value. Some question if organisations are ready for this. Let us step up to the challenge.
Why evidence-based HR is critical to success and how to get started
I am huge fan of HR Data & Analytics and I have had the privilege of working with it for many years now. However, it is important to remember one thing; HR Analytics is only a mean to and end; one tool and one mean to better HR.
I talked about exactly that at Human Consult Network with Annemarie Malchow-Knudsen. We discussed among others the need for more evidence in HR and how you in small- and medium sized companies can get started.
Place your bet where you have the highest chances of winning
The purpose of evidence-based HR is not to find “the Right Answer” – we are dealing with people after all. The purpose is to use all available evidence (research, internal data, analysis, experience, interviews etc.) to find the solution with the highest probability of adding the most value to your organization and start out from there. If that doesn’t sound strong enough, believe me, it will be a huge improvement from where we are.
Why will the solutions be better? Psychological research shows that even the most reflected people fall into pitfalls such as biases (see some of the most common ones here, here and here)and prejudices when judging what the best thing to do is. We simply often choose less probable outcomes over more probable ones without even knowing it. Ordinary people like you and I do it all the time.
One way to get around it is to apply an evidence-based approach to establishing the most optimal people interventions. And this is where data comes into the picture. By being better at testing your HR-hypotheses with the use of data and valid analytical tools, you will eliminate the number of times where you decide to go for an HR intervention, which sounds appealing does not have the effect you hope for.
Start with the business challenge and then identify the data
I have seen too many good people get stuck in data cleaning, data management and tough IT-implementations without getting any business results to know that there must be a better way. So, if you don’t want to end up in that situation start with the business challenge and focus where you can add value quickly. My experience is that many start the other way around as the only option and that can mean that business results take too long to materialize.
Start from the top of the figure (for more info about the content of the pyramid see here) shown above by asking for the business issue, which you will help solving. If the primary business focus is on cost-optimization, your people activities should also focus on cost-optimization. You should focus on getting most value for money invested whether you are involved in leadership development, induction programs, talent management, staffing or something else.
Then ask which knowledge you will need to get that insight: do you need more knowledge of learning efficiency, more knowledge of staffing costs versus performance outcomes of different staffing strategies, insight to identify the best-fit candidates when recruiting etc.
Then identify the information you will need to create that knowledge. You can get inspiration externally from scientific research and best practices, and you can strengthen the argument by analyzing your own organization.
Only then, will you know which data you will need to establish to underpin your intervention with convincing evidence. You can now gather exactly the data required to make an ROI-assessment to underpin your argument – and help you chose the approach with the highest probability of success.
Taking this agile approach will enable you to build your data foundation along with creating value-adding insights to inform business decisions. You cannot avoid investing in data and technology, but providing a flow of value adding insights will ease the funding.
Storytelling is nothing without a proper theory – here’s why
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.
Six must-have competencies in a world-class analytics team
Succeeding with workforce analytics is difficult. It requires a mix of skills not found in one person only, and you should not assume, that you can do it on your own. We are all decent at most things but really only good in a few. You should therefore assemble a team, which has a multiple of superheroes each with a superpower of their own.
I described this in a previous post, where I suggested six competencies a superhero analytics team should have:
- Strong data management skills
- Captivating storyteller
- Understand the business
- Ability to visualize your results
- Strong psychological skills
- Excellent statistics and numbers skills
But what happens if just one of those skills are not present? Can’t we manage anyway? My answer is no. If just one of the skills is missing from the team, six outcomes are possible – each with a disastrous outcome – as shown in the figure below:
In essence, if you:
- have no good data, you will not be able to perform analytics. It is as the old saying goes: crap in – crap out. If you do not have good data, it is sometimes better not to do analytics.
- lack of storytelling abilities, the message will nog. As Tom Davenport describes: “Narrative is the way we simplify and make sense of a complex world” and it is the way messages are most effectively conveyed and the best way to get people to change (which is the ultimate goal of analytics).
- have no business acumen will mean that your team will perform excellent analytics on the wrong issues. Workforce Analytics should help decision making on vital must win battles for your organization. Understanding the business is vital to understand what those must win battles are.
- are not able to do visualizations you will bore your audience. Data and numbers are boring (and I am a numbers guy), but data and numbers effectively conveyed through visualization
- lack psychological skills you will misunderstand your findings, be unable to convert your information to knowledge and be subject to important challenges such as bias, cognitive dissonance, imposter syndrome etc.
- have poor numbers and statistics abilities, your analysis will just be plain poor. You can get really far with simple regression-, factor- and t-test analysis skills 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.
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.
Cost and value – the difference that makes a successful workforce analytics function
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?
The state of Workforce Analytics is pretty good – and improving
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.
