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Saberr Workforce Analytics Summit 2016 Amsterdam
- 1. Analytics: How to make the dream a reality Tom Marsden & Alistair Shepherd Saberr
- 3. In ‘People Analytics,’ You’re Not a Human, You’re a Data Point
- 4. I strongly believe that anyone working in a company that really is like the one described in the NYT would be crazy to stay. I know I would leave such a company.
- 6. “Success in creating AI would be the biggest event in human history…..unfortunately, it might also be the last, unless we learn how to avoid the risks”
- 9. trust
- 10. • Less than 50% believe employers are open & upfront • 1 in 4 don’t trust their employers • 1 in 3 said employers aren’t honest or truthful American Psychological Association’s 2014 Work and Well-Being Survey http://www.apa.org/news/press/releases/2014/04/employee-distrust.aspx AMERICAN PSYCHOLOGICAL ASSOCIATION
- 11. American Psychological Association’s 2014 Work and Well-Being Survey http://www.apa.org/news/press/releases/2014/04/employee-distrust.aspx AMERICAN PSYCHOLOGICAL ASSOCIATION Two factors to build trust… 1. Communication 2. Involvement
- 14. involvement
- 17. Valuable information goes to important people first, then trickles down sometimes. Frederic Laloux Sensitive information is kept within top management, if it must be presented it is filtered and presented in the best possible light. The underlying assumption is that people can not be trusted
- 18. Gather data Store data Data curation Machine learning Analytics Visualisation Action
- 19. A B C D ?
- 20. Performance
- 21. Engagement surveys… Big moral questions…
- 32. + a collection +
- 33. a network
- 34. Can we predict the performance of sales people if we: a) treat them as individuals? b) treat them as networks?
- 36. Openness, r=-0.29 Conscientiousness, r=0.05 Agreeableness, r=0.14 Extraversion, r=-0.20
- 37. 50 people, 1 year of performance data r = 0.25, p = 0.14 Emotional Stability
- 38. Does relationship quality impact sales performance?
- 40. 50 people, 1 year of performance data r = 0.41, p = 0.015
- 41. B A
- 43. r = 0.52, p = 0.001
- 47. communication patterns between people accounted for >50% of the variance in performance
- 48. dream nightmareor 1. Build trust 2. Involve people 3. Focus on teams
Editor's Notes
- Good morning. I’m Tom Marsden CEO of Saberr. In this session today we want to reflect on the promise of analytics: For many the dream has not yet become a reality. Why is that? In fact for some the prospect of people analytics is more of a nightmare than dream? What is it that people are scared of? In the session today we will discuss some factors that will determine whether analytics will be a success or a nightmare 1. How much trust do employees have in the process? 2. How involved are they in benefitting from the data they provide? 3. I’m then going to handover to Alistair to talk an area we believe that can play a big part in fulfilling the potential of people analytics: team analysis
- So what is the nightmare? The nightmare of analytics was captured by the Orwellian view of the world. Organisations are big brother monitoring your every move. Data privacy is a thing of fiction. The 1984 phrase “ignorance is strength” captures the idea of a centralised state making decisions that affect all aspects of our lives - many fear the same in modern workplaces
- In modern newspaper headlines it manifests itself in headings like this.
- In stories about corporates it manifests itself in situations like the spat between the NY Times and Amazon. The NY alleged that Amazon treated some of its employees as live robots. So bad was the article that Jeff Bezos reacted by saying….
- In context of society nightmare manifests itself with threat of mass unemployment. As algorithms make many of our jobs obsolete. There are countless stories of robots taking over the jobs: Second machine age and rise of the robots two award winning books Oxford academic study anticipated that up to 40% could be replaced Recent articles focus specific industries - this one about Wall Street Prediction of 900,000 job losses in retail by 2025 (FT)
- Eventually Artificial intelligence leads to destruction of the human race. Stephen Hawking, Bill Gates & Elon Musk open letters warning of the dangers. Stephen Hawking quote Given this no wonder many people don’t trust analytics
- What about the dream? Steve Jobs eloquently summed up the dream. Scientific American study of the animal kingdom Distance travelled versus energy spent. Humans were about a 3rd of the way down The condor was the most efficient of all the animal kingdom, Until one of researchers included a human on a bicycle; blew away condor’s record. Jobs compared to Apple building computers: amplify man’s efficiency with tools “We aren’t building computers, we’re building bicycles for the mind”. This was and remains the dream. Data can help the human race far exceed our natural abilities.
- On a more prosaic level this is research from Pew institute indicating some of areas that people where people say technology and analytics make them better informed. Areas such as: shopping, news, staying in touch with friends, health many of us have come to rely on apps we trust. I would be lost without buying my super-market, look forward to personal recommendations from dojo app that uses things I have liked about London to recommend things to do. I trust their recommendation….
- At the heart of this question is Trust. Trust that the data we provide is well managed Trust that the people using the data are benign Trust that we get something out of the data we provide Yet Trust in the corporate environment is low.
- The American Psychological Association’s 2014 Work and Well-Being Survey found that less than half believe employers are open and upfront. Nearly 1 in 4 workers don’t trust their employers and 1 in 3 reported their employers aren’t always honest or truthful.
- Two factors in the survey predicted significant amount of the variance in trust: First, how well the organisation communicates. Second how it involves its workforce in decisions. Both of these have clear implications for analytics.
- If we are to build trust its important we communicate how we using data This is a complex area given the range of data privacy regulation This chart from DLA Piper shows the very broad range of legislation on data privacy Some very heavy (in red) and some quite limited (Green) According to this chart US and EU both in red and yet we know that in fact its even more complex than this
- In US: no central authority protections vary by industry & state, no right to privacy, In the EU each country a data protection authority, right to privacy in the workplace This has led to restrictions on transfer outside EU and the complexity of safe harbor If employees are not going to end up overwhelmed and confused employers need strong communication. Communication goes beyond legal requirements into data policies of the company This area of communication is key. However it’s not one we are going to tackle in depth today b. its highly contextual a. we are not lawyers and c. its quite dry.
- We want to focus on the second factor that drives trust. How involved do people feel. In analytics this can be summarised by a simple question: what’s in it for me? This is where many people feel the analytics journey turns more nightmarish than dreamlike and an area we will explore
- Many feel there’s an information asymmetry in data management in the workplace. The employee is being asked to give more and more data but getting little back. The employer is gathering more and more knowledge on the employee. Asymmetric information result in two problems: adverse selection and moral hazard. Adverse selection leads to the less informed party selecting “bad” choices. Moral hazard: where the more informed party misuses private information.
- If we are honest we should recognise that employees are skeptical about how management use data. As ever Dilbert sums this up so well .
- Frederic Laloux in his interesting book on “reinventing organisations” sums up the same issue in a different way Describing the way information flows in most companies Read quotes So how can we address this problem of Information asymmetry and build trust?
- We are all aware of the a variation on this model for analytics: ….. There’s nothing wrong with this model. It’s a great process. It’s just that in reality this process happens in a particularly way…
- It’s very hierarchical Information is collected at point A. It may be analysed by a team at point B. they pass information to a HR expert at point C who explains it to a manager at point D. Trust can erode easily in this process. For the manager at D; hard to drive change action once you’ve centralised insight. Managers are wondering why person A isn’t taking responsibility for the problem. People at point A wonder what happened to their data. Too much technology and analytics has been built on this hierarchical process Let’s take 2 examples of the problems that this causes
- Think about performance reviews which are subject to much re-evaluation now: 48% HE Execs think performance review weak in driving value 48% think they are weak in encouraging ongoing feedback 58% think weak in driving engagement with employees 58% think weak efficient use of time Why is this? One reason is that they are too centralised: Not trusted by employees. Too much work for managers. Too much form filling. Reduction is high quality conversations and feedback One off interventions rather than ongoing
- Another area is engagement surveys. I googled 2 terms problems with engagement surveys and problems with death penalty. 105m for engagement surveys and 24m for death penalty. At it’s heart the problems are similar. Again the problem is that its too centralised the people filling in the data divorced from the results. the people receiving the results disconnected from the ability to do something. So the question is - Is there a better way?
- We think there is. Part of a much larger movement of how can we improve the way we run any organisation: devolve, decentralise, localise. We need to design the analytic solutions at more local levels in the organisation. We need to give our employees bicycles for the mind So how far do we push the decision making? The honest answer is that it depends. We need to be more thoughtful of who can use the insight to drive change There are three levels: The organisation, The team , the Individual. In our view one of the most exciting areas to explore in analytics is the level of the team and to explain why my friend and colleague…
- Ok so we have this concept of the ‘Team’. We talk about it so much, but it’s mostly just anecdotes. One thing I’ve become interested in is to what extent can focusing on teams help make the dream of people analytics a reality. But since we’re at an analytics conference, let me use analytics to illustrate why measuring teams is better than measuring individuals, or whole organisations.
- Here’s a flock of starlings. In many ways, this is symbolic of a large enterprise. There are thousands of birds, each performing a role within a much larger body, presumably with some goal, find a nest, find food, find Africa etc. There’s no apparent leader and yet they move in such elegant and undeniable unison. But is the movement predictable? Enterprises ask the very same question of their analytics team? How do we predict the actions of our employees? For this scenario, we’re going to ask ‘how do we predict the action of a single bird’?
- It’s near impossible if you treat the birds as individuals. (But remarkably simple if you understand that each bird is in fact an integral part of the network.)
- Here’s a mathematical model of a flock of individual birds, all moving at various speeds and in different directions. So far, nothing like a real flock of starlings. Remember we’re trying to produce a model that is able to reflect the complex movements of a whole flock and the actions of a single bird. So let’s apply some rules.
- Rule 1, all birds must travel at the same speed. Already it’s easier to predict the movement of each bird, but not the flock.
- Rule 2, all birds must stay close to their neighbour. Now it’s very easy to predict the movement of each bird, and even the flock, but it doesn’t resemble the real world
- Rule 3, all birds must avoid danger. And now, all of a sudden we can very accurately predict the movement of each individual bird as well as the flock in way that is representative of the real world.
- Here’s the interesting thing. Each bird is only influenced by its closest seven neighbours not itself and not the whole flock. Much like ourselves at work. We’re influenced by a handful of peers. Maybe our team mates, our line manager and potentially the CEO. But certainly not all of the 3,000 other people who work for my company.
- As in the case of the birds we are only able to understand the complex dynamics of employee performance by treating each employee, not as an individual, but as part of a small local network. So if we’re serious about using analytics to measure people, in a way that reflects reality, then we must understand that we too belong to small networks, in other words, people are influenced by teams not organisations.
- However, we mustn’t make the mistake of thinking that a collection of individual results counts as measuring the network.
- It’s the links between people that are important, not the accumulative sum of individual results. Let me cement this with another example, not from nature, but from the workplace.
- This scenario is from a piece of work we did with one of our clients. This is the holy grail of people analytics - Can we predict the performance of sales people if we a) treat them as individuals or b) treat them as networks? First, to make it a fair test, we normalised the data for things like market opportunity, seniority and tenure. Then we looked at 3 different metrics.
- Number 1. We assessed their individual behaviour. How much would their ‘behaviours’ or ‘personality’ impact their ability to sell? For this we used the ‘big 5’ methodology. It’s a reasonable hypothesis, this type of study has been done many times before. For example, we might imagine extraverted people do better in a sales than introverts. Here’s the data:
- These are all graphs of performance, measured in revenue generated, on the y-axis (it’s a log scale) and big 5 trait score on the x-axis. As we can see, there are slight but weak relationships between performance and big 5 traits.
- For this particular client the trait with the biggest impact on performance was Emotional Stability. For the statisticians among you this graph has r = 0.25, which means performance is only impacted a very little bit by Emotional Stability. It also has a p value of 0.14 which means we’re there’s a high degree of uncertainty on this result. So, there’s clearly other factors that have a bigger impact on performance.
- So the second metric we looked at was: Does relationship quality impact sales performance?
- To measure relationship quality we combined a few years of research in the data patterns from millions of people in online dating along with the european social survey on values. The headline result was that relationship quality largely depends on your values and your tolerance for other peoples values. If you’re interested in this come and find me after the talk.
- So, when we look at relationships rather than behaviours we see a much better correlation with performance. Which is interesting because we all know that other people have a big influence on us, but now we can see exactly how much they influence us. This time r=0.41 and p=0.015. Meaning the effect of relationships on performance is larger than behaviour and we’re more certain of this.
- For our third test we modified resonance to include the concept that the relationship between two people could be impacted by the presence of a third person. In other words, a team. For example, let’s say Tom and I are a two person team and I have an average relationship with him.
- And then we start working with a third person, to what extent does that third person impact my relationship with Tom? If I have a much better relationship with this new person, then my relationship with Tom will be lowered because now I have a benchmark. We call this ‘dynamic relationship quality’.
- So when we correlate this to performance we get a much better result. r=0.52 which is a statistically significant relationship and we can be even more confident that this is true because p=0.001 which means we have the smallest error margin yet. This obviously isn’t a silver bullet and there are other important factors that lead to high performance in a sales person but you get the point, big gains in accuracy and predictive capability can be made when we start making simple changes to our assumptions. People shouldn’t be measured in isolation. I’m sure you all know from your own careers that when you work with someone where you have a bad relationship, then it is very hard for either of you to do your best work. Conversely when we work with people who we have a very good relationship with, then our performance is often at a maximum.
- What’s interesting is that similar results about the importance of relationships and teams are being found by other leading researchers. Here’s a study from MIT professor Sandy Pentland.
- Their goal was to document what makes teams ‘click’. What they realised was they could sense a ‘buzz’ in teams, even if they didn’t know what they were talking about. That suggested that the key to high performance lay not in the content of a team’s discussions but in the manner in which they were communicating.
- They looked at lots of industries where teams were comprised of people with similar tasks and had similar skill sets but had a big range in performance. Hospital teams, call centres, back room operations, customer facing teams in banks etc.
- They equipped people with special badges worn round the neck that measured things like tone of voice, body language, whom they talked to and how much etc. What they found was that patterns of communication could account for more than 50% of the variance in performance. That’s more than all the other factors: individual intelligence, personality, skill, and the substance of discussions — combined. And yet many organisations still ignore team dynamics. The question is how can we combine all the elements that contribute to team performance to firstly forecast performance with much greater accuracy but more interestingly, how can we use this data to improve the teams at a local level and this is the journey that we’re on.
- So that’s the choice facing analytics. Will it be a dream or a nightmare? The biggest part of this is about building trust A key way to build trust is to involve people And the right place to involve people is at the team Do this and you’ll get dream results from your workforce analytics.