Posts tagged ‘People analytics’

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.

Analytics HR Team Skills

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.

 

05/04/2018 at 08:19 Leave a comment

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.

30/01/2018 at 12:18 Leave a comment

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.

HR Data Value Chain

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.

 

14/09/2017 at 11:43 2 comments

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.

Screen Shot 08-01-16 at 10.33 PM

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.

Screen Shot 08-01-16 at 10.30 PM

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.

02/08/2016 at 18:42 3 comments


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