PowerPoint Presentation
Analytics:How to make the dream a realityTom
Marsden&Alistair Shepherd
Saberr
Good morning. Im 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. Im 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
The nightmare
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 People Analytics, Youre Not a Human, Youre a Data Point
In modern newspaper headlines it manifests itself in headings
like this.
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.
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
StreetPrediction of 900,000 job losses in retail by 2025 (FT)
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
Eventually Artificial intelligence leads to destruction of the
human race. Stephen Hawking, Bill Gates & Elon Musk open
letters warning of the dangers. Stephen Hawking quoteGiven this no
wonder many people dont 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 condors
record. Jobs compared to Apple building computers: amplify mans
efficiency with tools We arent building computers, were 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.
trust
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
benignTrust that we get something out of the data we provideYet
Trust in the corporate environment is low.
Less than 50% believe employers are open & upfront
1 in 4 dont trust their employers
1 in 3 said employers arent honest or truthfulAmerican
Psychological Associations 2014 Work and Well-Being Survey
http://www.apa.org/news/press/releases/2014/04/employee-distrust.aspx
AMERICANPSYCHOLOGICALASSOCIATION
The American Psychological Associations 2014 Work and Well-Being
Survey found that less than half believe employers are open and
upfront. Nearly 1 in 4 workers dont trust their employers and 1 in
3 reported their employers arent always honest or truthful.
American Psychological Associations 2014 Work and Well-Being
Survey
http://www.apa.org/news/press/releases/2014/04/employee-distrust.aspx
AMERICANPSYCHOLOGICALASSOCIATIONTwo factors to build trust
Communication
Involvement
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 dataThis is a complex area given the range of data privacy
regulationThis 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 harborIf employees are not going to end up overwhelmed and
confused employers need strong communication. Communication goes
beyond legal requirements into data policies of the companyThis
area of communication is key. However its not one we are going to
tackle in depth today b. its highly contextual a. we are not
lawyers and c. its quite dry.
involvement
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: whats 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 theres 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 .
Valuable information goes to important people first, then
trickles down sometimes. Frederic LalouxSensitive 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
Frederic Laloux in his interesting book on reinventing
organisations sums up the same issue in a different wayDescribing
the way information flows in most companiesRead quotes
So how can we address this problem of Information asymmetry and
build trust?
Gather dataStore dataData curationMachine
learningAnalyticsVisualisationAction
We are all aware of the a variation on this model for analytics:
..Theres nothing wrong with this model. Its a great process. Its
just that in reality this process happens in a particularly way
ABCD?
Its 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 youve centralised insight. Managers are
wondering why person A isnt 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 Lets take 2 examples of the problems that this causes
Performance
Think about performance reviews which are subject to much
re-evaluation now: 48% HE Execs think performance review weak in
driving value48% think they are weak in encouraging ongoing
feedback 58% think weak in driving engagement with employees58%
think weak efficient use of timeWhy 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 feedbackOne off interventions rather than
ongoing
Engagement surveysBig moral questions
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 its 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 changeThere 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
teamand to explain why my friend and colleague
team
Ok so we have this concept of the Team. We talk about it so
much, but its mostly just anecdotes. One thing Ive become
interested in is to what extent can focusing on teams help make the
dream of people analytics a reality.
But since were at an analytics conference, let me use analytics
to illustrate why measuring teams is better than measuring
individuals, or whole organisations.
Heres 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. Theres 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,
were going to ask how do we predict the action of a single
bird?
Its 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.)
Heres 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 were 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 lets apply some rules.
Rule 1, all birds must travel at the same speed. Already its
easier to predict the movement of each bird, but not the flock.
Rule 2, all birds must stay close to their neighbour. Now its
very easy to predict the movement of each bird, and even the flock,
but it doesnt 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.
Heres 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. Were 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
were 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.
+a collection
+
However, we mustnt make the mistake of thinking that a
collection of individual results counts as measuring the
network.
a network
Its 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.
Can we predict the performance of sales people if we: treat them
as individuals?treat them as networks?
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.
Does personality impact sales performance?
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. Its 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. Heres the data:
Openness, r=-0.29Conscientiousness, r=0.05Agreeableness,
r=0.14Extraversion, r=-0.20
These are all graphs of performance, measured in revenue
generated, on the y-axis (its 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.
50 people, 1 year of performance datar = 0.25, p = 0.14Emotional
Stability
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 were theres a high degree of uncertainty
on this result. So, theres clearly other factors that have a bigger
impact on performance.
Does relationship quality impact sales performance?
So the second metric we looked at was: Does relationship quality
impact sales performance?
+
=values+tolerance
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 youre
interested in this come and find me after the talk.
50 people, 1 year of performance datar = 0.41, p = 0.015
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 were more certain of
this.
BA
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, lets say Tom and I are a two person team and I have
an average relationship with him.
dynamic relationship qualityCB
A
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.
r = 0.52, p = 0.001
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 isnt 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 shouldnt be measured in isolation. Im 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.
MassachusettsInstitute ofTechnologySandy Pentland
Whats interesting is that similar results about the importance
of relationships and teams are being found by other leading
researchers. Heres 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 didnt
know what they were talking about. That suggested that the key to
high performance lay not in the content of a teams 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.
communication patterns between people
accounted for >50% of the variance in performance
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.
Thats 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 were on.
dreamnightmareorBuild trustInvolve peopleFocus on teams
So thats 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 youll get dream results from your workforce
analytics.
[email protected]@saberr.com
