Applying machine learning to the workplace - Quora … · The case for applying machine learning to...
Transcript of Applying machine learning to the workplace - Quora … · The case for applying machine learning to...
1
Applying machine learning to the workplace
…signalling a better way forward
A Quora Consulting Discussion Brief
Authored by John Blackwell
© Quora 2017
2
Table of contents
Introduction .................................................................................................................................................................... 4
The case for applying machine learning to the workplace ..................................................................... 4
HR, IT, Property, & management cultures working in unison ............................................................... 4
Talent challenges ..................................................................................................................................................... 4
Global pressures, local challenges .................................................................................................................... 5
Sensors adding to ‘big data’ ............................................................................................................................... 5
‘Big data’ challenge ................................................................................................................................................. 5
Merging workplace analytics with machine learning ................................................................................ 5
Challenging workplace times .................................................................................................................................. 6
VUCA ............................................................................................................................................................................ 6
What does VUCA mean? ...................................................................................................................................... 6
Managing in a VUCA world ................................................................................................................................. 7
What’s needed of leaders ................................................................................................................................ 7
Workplace challenges ................................................................................................................................................ 9
Productivity ................................................................................................................................................................ 9
A positive scenario ............................................................................................................................................. 9
Talent ......................................................................................................................................................................... 11
Reasons for leaving ......................................................................................................................................... 11
Reasons for staying ......................................................................................................................................... 12
3
Lack of career progression ........................................................................................................................... 12
Sustainable engagement .............................................................................................................................. 13
Applying machine learning to the workplace ................................................................................................ 14
Big data and the workplace .............................................................................................................................. 14
The role of sensor technologies ..................................................................................................................... 15
What is the circadian rhythm? ......................................................................................................................... 16
What are chronotypes? ................................................................................................................................. 17
What is machine learning ...................................................................................................................................... 18
Workplace Excellence Platform® ................................................................................................................... 20
The machine learning process ......................................................................................................................... 20
Conclusion ................................................................................................................................................................... 22
About the author – John Blackwell .................................................................................................................... 23
About Quora Consulting ........................................................................................................................................ 23
4
Introduction
The case for applying machine
learning to the workplace
The premise of this briefing paper
considers the immense complexities and
interdependencies challenging our
modern workplaces and how best they can
be addressed.
Alongside the challenges of where to
effectively locate business operations and
how to create today’s workplace for
tomorrow’s labour force is the accelerating
technology landscape, the problems posed
by the shifting talent market, and the need
to wrest far greater levels of productive
output from the workforce.
It’s clearly an imperative for business
leaders to understand this vast spectrum
of parameters to refine continuously the
performance of their business operations.
This is evidently becoming a ‘big data’
challenge and one that can be supported
by precision workplace analytics alongside
the application of machine learning.
This is not machine learning taking over
from human decision making but the
analytics providing a significant
supplement to workplace decision making.
HR, IT, Property, & management
cultures working in unison
It has long been recognised that, to
deliver sustainable, enduring
transformation of work practices and to
optimise workplace performance demands,
all the business support function siloes -
HR, IT, Property, plus management culture
– need to work in unison behind common
metrics, goals and objectives that sustain
momentum for the entire workforce.
Yet these functions speak vastly different
languages, which makes it tremendously
difficult if not impossible to break down
the barriers and work in unison.
Talent challenges
Often quoted by CEOs as the biggest
challenge facing organisations is a war for
talent, both against direct competitors,
and other sectors often seen as offering
more attractive career prospects. An
organisation’s people have a direct impact
on how well it can respond to the
changing economic times.
Organisations clearly struggle to ensure
that their key people are engaged and do
not leave in search of better opportunities
and management struggles to ensure their
people have the tools and motivation to
perform at their optimum.
93% of CEOs recognise the need to change
their strategy for attracting and retaining
talent but there remains an enormous gulf
between intention and action – 61% of
CEOs have not yet taken the first step.
5
Global pressures, local challenges
When one combines this with the
pressures of a global market, and the
continual need to control costs, it really
brings to life a VUCA - volatile, uncertain,
complex, ambiguous environment that
makes managing an organisation and its
workforce hugely formidable.
Sensors adding to ‘big data’
Adding to this volatile mix, the
availability of sensors for monitoring light
intensity and spectrum, sound amplitude
and direction, air quality, temperature,
odour, and occupant location and activity
together with wearable biometric
technologies that offer the potential for
monitoring restlessness, boredom, and
stress, as well as poor posture or too much
screen time, all of which can be integrated
into environmental systems, this means
that workplaces are becoming sources of
massive ‘big data’
‘Big data’ challenge
There is no question that the quantities
of workplace data now available are
indeed large, but that’s not the most
relevant characteristic of this new data
ecosystem.
‘Big data’ refers to data sets that are so
large or complex that traditional
approaches are inadequate to deal with
them. It draws on predictive analytics, user
behaviour analytics, or certain other
advanced data analytics methods that
extract value from data and offer
tremendous opportunities for capture,
analysis, data curation, visualisation,
querying, and updating.
Analysis of data sets can find new
correlations to spot business trends,
identify talent undercurrents, employee
demographics, productivity and
behavioural performance, and work space
design.
The very depth of information, expertise
and data across the traditional HR, IT, and
Property functional siloes offers vast
potential for creating new, more
productive work ecosystems but there is
the ever-present risk of becoming lost in
the wrong ‘big data’, in other words
“…solving the wrong problem really well…”.
Merging workplace analytics
with machine learning
Using our cloud-based Workplace
Excellence Platform®, we have long been
the proponent of employing sophisticated
analytics to understand how an
organisation interacts to create NPV work
practices and workplaces models.
However, with the arrival of ever-greater
‘big data’ adding to decision complexity,
we have now substantially enhanced our
Workplace Excellence Platform with
machine learning to apply intelligence to
transforming, shaping, manipulating, and
merge your workplace data to visualise
different solutions in real-time
This briefing paper considers better ways
forward for workplace decision support.
6
Challenging workplace times
It is beyond question that our workplace
environments are becoming increasingly
less predictable and increasingly difficult
to manage, and this isn’t solely confined to
the challenges posed by the rapidly
changing nature of work.
VUCA
Nothing sums up the current workplace
environment better than the acronym
VUCA – Volatility, Uncertainty, Complexity,
Ambiguity – it serves to neatly describe
the convergence of four distinct types of
challenges that would normally demand
four distinct types of response. The term
summarises the different characteristics
and different approaches needed for each
scenario to describe the far more
challenging world in which organisations
operate.
What does VUCA mean?
Volatile – because things change
rapidly, but not in a predictable way,
Uncertain – because major changes
happen so quickly and so fast that we
cannot read them. The past is no
longer an accurate predictor of the
future,
Complex – because there are so many
different things happening all at the
same time with so many moving parts
and so many people involved,
Ambiguous – because the 'who, what,
where, when, why, and how' questions
what we used to pose no longer can
be answered by what we know today.
“In a VUCA world, the workplace is no
longer a straightforward place where we
merely turn up for work and then pop
back home again after a job well done”
Figure 1 - describing the characteristics and different approaches
required in a VUCA world
7
VUCA serves to neatly describe the
convergence of four distinct types of
challenges that would normally demand
four distinct types of response. Figure 1
summarises the different characteristics
and different approaches needed for each
scenario.
Volatility reflects the speed and turbulence
of change. Uncertainty means that
outcomes, even from familiar actions, are
less predictable. Complexity indicates the
vastness of interdependencies in globally
connected economies and societies. And
ambiguity conveys the multitude of
options and potential outcomes resulting
from them.
In a VUCA world, the workplace are no
longer straightforward places where we
merely turn up for work and then pop
back home again after a job well done. It
is a world typified by complex and ever
changing environments where we expect
our workforces to continually adapt.
Managing in a VUCA world
Leaders simply have no choice but to
step up to the challenge of managing
complexity. They need to set in place
more co-operative and integrated ways of
working to create a mindset that takes up
the new challenges.
VUCA leadership is a condition that calls
for many penetrating, challenging, open-
ended, analytical questions.
At the heart of the challenge is that people
are messy. By this, we do not mean that
people are physically messy – leaving work
environments strewn with mould-laden
coffee cups and part-eaten sandwiches –
but that every single one of the 7.3 billion
people that currently inhabit our planet
are entirely unique. Each has their own
individual wants and needs, preferences,
nuances, and peccadilloes. Each has
different sensitivities to noise,
temperature, light, odours, and
participation with colleagues. Each has a
unique response to restlessness, boredom,
fatigue, and stress, which in turn alters
their rhythm of productivity.
They access over 10 billion devices that are
responsible for generating global mobile
data traffic of 10.8 exabytes per month.
What’s needed of leaders
Penetrating questions that ferret out
nuance. Challenging questions that
stimulate differing views and debate.
Open-ended questions that fuel
imagination. Analytical questions that
distinguish what you think from what you
know.
The only thing you know with certainty
about your strategy is that it’s wrong.
People are messy – not physically messy
– but every single one of the 7.3 billion
population are individual. All with
individual wants and needs, preferences,
nuances, and peccadilloes.
8
The complexity in VUCA is centred on
dynamic relationships in which similar
inputs may yield vastly different outputs.
It is critical to know which forces are
positive, which are negative, and which
could go either way.
Persistent probing will help you discern if
it’s off by 5 per cent or 95 per cent before
events swiftly reveal the answer to you.
A common mistake made by managers
and executives is trying to oversimplify the
VUCA challenges. They seek to deny the
uncertainty and complexity and apply old
formulaic solutions in the hope they will
hold good. Typically, we find that
managers and executives are obsessed
with the 'keep-it-simple' mantra that
allows them off the hook – to rush towards
what they deem a solution when in fact it
is nothing more than a stopgap holding
position.
A key organisational task is not to design
the most elegant structure but to capture
individual capabilities and motivate the
entire organisation to respond co-
operatively to a complicated and dynamic
environment.
Success will come not from over-
simplifying problems, but by working in
new ways with each other to master
complexity, live with ambiguity, ride
volatility, and enjoy uncertainty.
A key organisational task is not to design
the most elegant structure but to
capture individual capabilities
9
Workplace challenges
Productivity
Despite constant advances in software,
equipment, and management practices to
try to make organisations more efficient,
actual economic output is merely moving
in lock step with the number of hours’
people put in, rather than rising as it has
throughout modern history.
Productivity is one of the most important
yet least understood areas of economics.
Over long periods, it is the only pathway
toward higher levels of prosperity.
Data from the Office for National Statistics
(ONS) has shown that output per hour in
the UK is 19 percentage points below the
average for the rest of the major G7
advanced economies by late 2016, the
widest productivity gap since comparable
estimates began in 1991.
Even with years of hindsight, business
leaders and economists remain unclear
why productivity rises or falls. During the
2008 financial crisis, labour productivity
actually increased slightly. Was this
because employers laid off their least
productive workers first? Because
everybody worked harder, fearful for their
1 “Times up for IT and Property Directors” research
report authored by John Blackwell, published by
Quora Consulting 2016
jobs? Or was it a measurement problem
as government statistics-takers struggled
to capture fast-moving changes in the
economy?
That’s a long way of saying we don’t know
for sure what is going on right now, or
how long it will last. But the possible
answers range from utterly depressing to
downright optimistic.
In our recent research study, titled “Times
up for IT and Property Directors”1 we
identified the aptly named “interruption
science” as a key factor. This details the
sheer number of times today’s worker is
interrupted. The greatest casualty of our
mobile, high-tech age is attention – and by
implication, productivity. By fragmenting
and diffusing our powers of attention, we
are undermining our capacity to thrive in a
complex, ever-shifting world.
A positive scenario
Think about a business that is investing
for the future. It hires a bunch of people
and opens new offices and builds new
factories. But while it is doing all that stuff,
its actual productivity is quite low. It has a
lot of people working a lot of hours, but
very low economic output until its
operations are fully up to speed.
Data from the Office for National Statistics
(ONS) has shown that output per hour in
the UK is 19 percentage points below the
average for the rest of the major G7
advanced economies by late 2016, the
widest productivity gap since comparable
estimates began in 1991.
10
Maybe, businesses are adding employees
in preparation for the future, but it will
take time for their investments to pay off
in terms of gross domestic product.
There’s a recent precedent for that pattern.
In the late 1990s, the stock market was
booming and companies were making
huge investments in staff, equipment, and
information technology. But reported
productivity growth was actually below the
long-term trend. Then it began rising in
the early 2000s.
But here’s one piece of evidence that the
pattern of the 1990s is not what is
happening today. Business investment
spending on equipment, intellectual
property and structures is low relative to
the size of the economy. You would
expect those numbers to be higher if this
was just a productivity lull as the economy
waits for big investments in the future to
pay off.
Still, there could be enough going on
below the surface of those overall
numbers that the optimistic case remains
plausible.
To use one example, engineers at several
companies are hard at work trying to
perfect driverless cars. At present, they are
a sap on productivity – they put in many
thousands of hours of work with no
economic output to show for it. But if
successful, their work could radically
increase the organisation’s productivity in
the decades ahead.
Apply the same across a wide range of
sectors – industrial goods, pharmaceuticals
and medicine, financial services firms – and
there’s optimism for a positive scenario.
That’s the scenario we should all hope is
occurring. Slow productivity growth now
is just a down payment on a much brighter
future.
11
Talent
With hiring and turnover levels on the
rise, employers are now experiencing
challenges with both attracting and
retaining employees, especially top
performers, and high-potential employees.
Adding to the challenge is that many
employers don’t understand the important
reasons that employees join and stay with
a company, according to our research
report titled “Creating today’s place for
tomorrow’s talent”.
Nearly half of employers (48 per cent) said
talent attraction activity has increased
compared with last year. For 15 per cent,
hiring activity has significantly increased.
Additionally, more than a third (35 per
cent) indicated that employee turnover
was rising. Nearly three quarters of
respondents are experiencing problems
attracting top performers (74 per cent) and
high-potential employees (69 per cent), an
increase from two years ago. Further,
more than two thirds reported difficulty
retaining high-potential employees (68 per
cent) and top performers (66 per cent).
With talent mobility on the rise, employers
need to understand what employees value
if they are to succeed in attracting and
retaining employees.
Unfortunately, our research reveal a
significant disconnect between employers
and employees.
Reasons for leaving
While employers recognise the
importance of compensation and career
advancement as key reasons employees
choose to join and stay with a company,
Studying 2,400 people, this report
identified that there’s been a significant
shift in labour market activity in the last
12-24 months
For the top talent – the talent that
organisation strive so hard to attract –
the two main reasons cited for leaving
an organisation are; workplaces
inadequately optimised for productive
work, and dull managers.
Figure 2 - 'C-suite view of attracting, recruiting, and retaining staff compared to 12 months
ago
12
they don’t place the same importance on
other top attraction and retention drivers,
job satisfaction, or a key retention driver,
trust and confidence in senior leadership.
The report revealed that, for the top talent
– the talent that organisation strive so hard
to attract – the two main reasons cited for
leaving an organisation are; workplaces
inadequately optimised for productive
work, and dull managers.
Reasons for staying
Interestingly, perceptions of job
security are the second most important
reason they join a company and the fourth
most important reason they stay.
Employees ranked trust and confidence in
senior leadership as the third most
important reason they stick with a
company. However, employers did not
rank any of these factors as key attraction
and retention drivers.
Unsurprisingly, less than half of employees
think their company does a good job when
it comes to attracting and retaining the
right workers. Only 36% said their
organisation hires appropriate highly
qualified employees, while 32% said their
employer does a good job of retaining
talented employees.
Lack of career progression
Our study also revealed that many
employees feel blocked in their current
position. A third of employees (31 per
cent) said they would need to leave their
organisation to advance their careers.
Even worse, the same percentage (31 per
cent) of employees who have been
formally identified as high potentials by
their organisation said they would need to
leave their organisation to advance their
careers. From the employer perspective,
less than half of respondents (49%) believe
they are effective at providing traditional
career advancement opportunities, while
25% said that compared with last year,
career advancement opportunities are
improving.
Figure 3 - Views on attracting and retaining staff
13
Organisations continue to miss the mark
when it comes to career development.
Given how important career advancement
opportunities are to employees, the fact
that so many employees, and especially
high potentials, feel stuck should serve as
a wake-up call to employers to review their
career development programs. Employees
will have more opportunities to seek
employment elsewhere as hiring activity
continues to increase, and employers will
be on the lookout for high-potential and
top-performing employees.
Sustainable engagement
Our study found that leadership is the
top driver of sustainable engagement (i.e.
the intensity of employees’ connection to
their organisation). However, less than
half of employees (48%) agree that senior
leadership is effective.
The importance of leadership can’t be
overstated. Employees are more likely to
remain at their companies if they have
trust and confidence in their senior
management and leaders.
14
Applying machine learning
to the workplace
Clearly, the modern workplace is awash
with data, and if correctly interpreted, this
data can substantially help improve the
quality of life and productivity of
employees.
The challenge of trying to interpret
increasing volumes of ‘big data’ across the
workplace cannot be underestimated.
Big data and the workplace
To succeed in business, you need a good
instinct when it comes to making
important decisions. Humans are natural
pattern seekers and problem solvers, while
machines are fantastic at performing
billions and trillions of calculations per
second. Big data analytics amalgamates
the human methods of problem solving
across inconceivable volumes of data,
aggregating it at high speed, and
returning useful insights in a meaningful
way.
Once you've fed in some data, analytics
does everything your instinct would do. It
interprets the available data, predicts
what’s going to happen, and makes a
decision based on its prediction. Like us,
analytics improves with experience.
Analytics exists to support your business.
A great business decision utilises both
human input and advanced analytical
tools. This two-pronged approach is the
key to becoming a market-leading
business.
You’d be hard pressed to name a sector or
industry that hasn’t yet been affected by
big data analytics. Retailers analyse past
purchases to predict future ones; scientists
map complex climate changes; companies
weigh consumer opinion based on social
media engagement. And in the
commercial building industry, data related
to space occupancy is informing
companies as to which spaces are being
actively used and which spaces might be
ripe for consolidation.
But beyond helping companies to
determine how much space is needed for
their employees, can big data help provide
more enjoyable and more productive work
environments?
Analysis of big data can give you a
better picture of the state of your
operations. Use descriptive
analytics to paint a narrative of your
historic data and discover what is
happening in your business right
now.
Analysis of big data can be used to
generate forecasts and make
predictions, giving you and your
team an understanding of the
important components of your
business and your employees.
Analysis of big data can be used to
recommend the optimal course of
action, justifying a decision based
on quantitative reasoning alone, and
support decision investments.
15
The office must become a more inviting
destination. For decades, if people wanted
to do their work, they were required to
commute to a centralised office or HQ.
Today, employees can vote with their feet,
working where, when and how they would
like, challenging the old notion that
employees do all their work in one
location. The workplace has undergone
remarkable changes in recent years as
mobile and increasingly connected devices
enable people to work virtually anywhere.
Our research has identified that individual
offices are unoccupied 77 per cent of the
time, dedicated workstations are
unoccupied 60 per cent of the time, and
conference rooms are often too big or
too small for the actual groups that use
them.
The data suggests an increasing demand
for smaller, social spaces to allow people
the chance to informally connect and
easily use their mobile tools such as
laptops, tablets, and smartphones. Simply
put, in an era when people can function
anywhere, most of us seek out desirable
spaces to meet with our most trusted
colleagues, and we need spaces that help
us connect with each other and with our
work. The data now available can help
create not only more efficient office
spaces, but also more desirable and
effective ones.
Analysis of big data exists to support your
business. A great business decision utilises
both human input and advanced analytical
tools. This two-pronged approach is the
key to becoming a market-leading
business.
The role of sensor technologies
Sophisticated sensor technologies
can contribute to a healthier and happier
workforce by tracking the way offices are
used and adjusting them automatically.
Used properly, the technology could turn
offices into places that employees choose
to be in for their overall wellbeing.
By constantly monitoring environmental
conditions – critical factors such as LUX
(light) spectrum, temperature, humidity, air
quality and odours, sound amplitude and
direction, CO2 levels, the way space is
being used and even employee's
emotional and physical wellbeing – offices
will be able to react automatically to actual
user needs. This represents an amazing
shift in design thinking. Sensors will
enable workspaces to continually alter for
maximum efficiency, adjusting
temperature and lighting levels, and make
changes when workers are getting bored
or frustrated.
Figure 4 - sensor monitoring data
Individual offices are unoccupied 77 per
cent of the time, dedicated workstations
are unoccupied 60 per cent of the time,
and conference rooms are often too big
or too small for the actual needs
16
This means that the focus of office design
will shift from maximising available space
to responding to the individual people
inside it. The past focus of space and
building management has missed the
greatest opportunity of all – to directly
monitor the needs of the occupants, not
just the function of the space.
Sensors for monitoring light intensity and
spectrum, sound amplitude and direction,
air quality, and occupant location and
activity can be integrated into the office
infrastructure to provide the detailed
information necessary for the
environmental systems to react to actual
user needs. Indeed, modern LED office
lighting systems can respond to the body’s
natural circadian rhythms.
However, with the advent of wearable
technologies, biometric sensors can also
provide insight into less obvious factors
like restlessness, boredom, and stress, as
well as poor posture or too much screen
time.
This opens the possibilities that sensors
would even be able to monitor emotions,
and even may monitor heart rate, gaze
direction, facial temperature, skin moisture,
skin temperature, and brain waves to
gauge if the user is focused on intense
work, is recharging, or is frustrated.
Used in combination – and possibly even
fully integrated into new office furniture –
these systems will help create workplaces
that can adjust, both physically and
environmentally, in response to the
conscious and unconscious behaviour of
the people inside them.
What is the circadian rhythm?
Early to bed, early to rise … doesn't seem
to have the same meaning these days, if
you consider the way in which our
circadian rhythms shape our behaviours
and patterns, both day and night.
Circadian rhythm patterns, also known as
chronotypes, are something we are all
born with, and these can vary greatly from
one person to the next.
Business is beginning to wake up (pardon
the pun) to the proverb no longer being
relevant, given the wide differences in their
employees' rhythms and this is starting to
change the way the workplace is designed.
Research into circadian rhythms continues
to show that the more that businesses
ignore their employees' patterns, the more
it may end up costing them in productivity
overall.
Sensors for monitoring light intensity
and spectrum, sound amplitude and
direction, air quality, CO2 levels,
occupant location and activity can be
integrated into the office infrastructure
to provide environmental systems
information to react to actual user needs
17
What are chronotypes?
Everyone has an internal circadian
rhythm. Chronotypes are the
identifications of these rhythms and the
two that most people are familiar with are
the morning lark (or early bird) and the
night owl.
While these can be difficult to define, night
owls tend to go to sleep much later and
rise later in the morning, while morning
larks are early to bed and early to rise.
Chronobiology research has found that
there are many differences between night
owls and morning larks, even beyond their
preferred time to doze, it has found the
influence how we go about doing
business, or even living our lives.
Figure 5 - Circadian rhythm. These are the physical, mental and behavioral changes that follow a roughly
24-hour cycle, responding primarily to light and darkness
18
What is machine learning
Data can hold secrets, especially if you
have lots of it. With lots of data about
something, you can examine that data in
intelligent ways to find patterns. And
those patterns, which are typically too
complex for you to detect yourself, can tell
you how to solve a problem.
This is exactly what machine learning does.
It examines large amounts of data looking
for patterns, then generates code that lets
you recognise those patterns in new data.
Applications can use this generated code
to make better predictions. In other
words, machine learning can help you
create smarter workplace applications.
For example, suppose you want to
understand the workplace configurations
for optimum productivity across several
diverse operations?
What’s the right approach for doing this?
One option is to get a few smart people
together in a room and think about it, then
come up with a generic layout. This is
probably the most common approach and
it may work or it may not – in truth you’ll
probably not find out.
But if there’s data available about
the problem you’re trying to
solve, you might instead use that
data to figure out an effective
solution. For example, suppose
you’re trying to find the best
workplace layout, and all you
have to work with is the historical
data shown in Figure 6.
The good thing about having so little data
is that you might be able to find a pattern
just by looking at it. The bad thing about
having so little data is that the pattern you
find is likely to be wrong.
Given the data in Figure 6, for example,
you might decide that support team
occupancy is poor, but there’s every
likelihood that the decision probably isn’t
correct.
With more data, your odds of finding a
more accurate pattern get better, but
finding that pattern will be more difficult.
For instance, suppose you have the set of
location data shown in Figure 7 to work
with.
With this much data, it’s immediately
obvious that our first guess at a workplace
configuration may possibly not be right.
Looking at the broader data set in figure 7
suggests the original view that ‘support’
was not effectively using workspace can’t
be right.
Occupancy levels Location Average age Acceptable
Purchasing 64% Belfast 47 Adequate
Legal 66% London 53 Acceptable
Finance 58% London 48 Acceptable
Support 45% Glasgow 35 Good
Operations 50% London 44 Marginal
Marketing 44% London 37 Good
Commercial 46% London 42 Acceptable
Research 64% Birmingham 36 Poor
HR 42% London 41 Poor
IT 72% Belfast 37 Unacceptable
Planning 48% London 43 Acceptable
Communication 46% London 39 Acceptable
Occupancy levels Acceptable
Purchasing 64% Adequate
Legal 66% Adequate
Finance 58% Marginal
Support 45% Poor
Figure 6 – with just a small amount of data, it’s
hard to find accurate patterns
Figure 7 – More data can help in finding better patterns
19
When considering the broader perspective
of the ‘working styles’ for each function, a
fresh view surfaces. But, if ‘support’s
utilisation of workspace wasn’t ideal,
what’s the right answer?
Maybe a combination of factors come
together to indicate good use of
workspace? The truth is that the pattern
the data supports is this. The combination
of location, age, workstyle, and function
paint a different picture and suggest that
the HR function based in London needs
further investigation. With some time, you
may probably have figured this out, since
the data you can work with isn’t very large.
But suppose you have not just ten records
to work with, as in Figure 7, but ten million.
And suppose that for each record, you
have not just the five columns of data
shown in Figure 7, but 60 columns and
furthermore, the data is being updated in
real time. There’s probably a useful
pattern hidden in that data for
determining which locations are effective,
but you’ll never figure it out by manually
looking at the data.
Instead, you have to use analytical
techniques, approaches that are designed
for finding patterns in large amounts of
data.
This is exactly what the machine learning
process does. It applies analytical
techniques to large amounts of data,
looking for the best pattern to solve your
problem. It then generates an
implementation scenario that can
recognise that pattern. This is referred to
as a model, and it can be called by
applications that need to solve this
problem.
And while location utilisation is a basic
example, machine learning is applicable to
much more than this. This can be used to
predict an organisation’s or an individual
operation’s optimum productivity; enhance
quality of life for team members; indicate
technology deployment strategies; suggest
the most advantageous new office
location, and even help shape work space
configurations or anything else where lots
of historical data is available. Because
machine learning helps predict the future,
20
it’s often included in the broader category
of predictive analytics. All that’s needed is
the data, machine learning software to
learn from that data, and people who
know how to use that software.
Workplace Excellence
Platform®
The latest enhancements on our
Workplace Excellence Platform® is a cloud
service that helps people execute the
machine learning process. As its name
suggests, the Workplace Excellence
Platform considers the entirety of
workplace interventions, from HR, IT,
property, management cultures along with
workstyle considerations and the full
spectrum of sensor data to help filter
optimum workplace scenarios – both for
today and going forward.
Using sophisticated analytics, the
Workplace Excellence Platform models the
interactions between HR, IT, Property/
Facilities, and management cultures to
create entirely new work practice and
workplace design scenarios typically inside
three to five weeks irrespective of the size
of organisation.
The machine learning process
Machine learning starts with data—the
more you have, the better your results are
likely to be. Because we live in the big
data era, workplaces are now awash with
data. Having lots of data to work with in
many different areas lets the techniques of
machine learning be applied to a broader
set of problems.
Once machine learning has the right data,
it can move on to searching for the best
way to solve the problem they’re working
on, for instance such as suggesting
optimal workplace configurations.
To do this, machine learning uses
algorithms to work with the data, typically
applying statistical analysis such as a
regression, along with two-class boosted
decision tree and multiclass decision
jungle.
Please don’t be put off by these complex
algorithm terms, like most technologies,
machine learning has its own specialised
jargon, terms that can be a little confusing
from the outside. The goal is simply to
determine what combination of machine
learning algorithm and data generates the
most useful results and generate a model.
Figure 8 - the cloud-based Workplace
Excellence Platform® employs sophisticated
analytics to model entire workplaces to Net
Present Value level inside 3-5 weeks
Please don’t be put off by complex
algorithm terms, like most technologies,
machine learning has its own specialised
jargon, terms that can be a little
confusing from the outside. The goal is
simply to determine what combination
of machine learning algorithm and data
generates the most useful results and
generate a model.
21
The algorithm implemented by the model
itself provides a solution that actually
solves a problem. Models are accessed by
applications to answer questions such as
“what is the optimum workspace
configuration for productive work”, “which
office locations offer access to the right
talent pipeline”, “which enabling
technologies will support the team with
the optimum productive outcomes”, etc.
Machine learning algorithms are used only
during the machine learning process itself.
It’s also important to understand that a
model typically doesn’t return a yes-or-no
answer. Instead, it returns a probability
between 0 and 1. Deciding what to do
with this probability is usually a business
decision.
However, once it’s deployed, a model
implements algorithms for recognising
patterns. And where did that model come
from? It was derived from the data – your
data. Rather than putting a few smart
people in a room and letting them invent a
way to solve a problem, machine learning
instead generates effective scenarios from
data.
When you have lots of data to work with,
this is a very effective approach for
suggesting new work practice and
workplace scenarios.
Figure 9 – The machine learning process starts with your raw data and ends up with a model derived
from that data.
22
Conclusion
The idea of machine learning has been
around for quite a while. Because
workplaces now have so much more data,
machine learning has become useful in
more areas. Yet unless the technology of
machine learning gets more accessible, we
won’t be able to use our big data to derive
better solutions to problems, and thus
create more effective workplace scenarios.
Over the last three years, our cloud-based
Workplace Excellence Platform® has
already proven itself by identifying up to
20 per cent reduction in project time and
up to 30 per cent increase in output per
head for more than seventy-five clients.
The veritable explosion of big data
currently stemming from our workplace
ecosystems is only set to increase in terms
of uncertainty and complexity … and not
to mention, massively increase in real-time
volume. All of which will place intense
pressure on workplace professionals – the
HR, IT, and Property/Facilities professionals
charged with making daily decisions and
investments to enhance work practice
productivity.
A primary goal is to make machine
learning accessible for workplace
professionals. This cloud service can help
a broad range of professional people play
a bigger role in bringing machine learning
into the mainstream workplace. Going
forward, expect data-derived models to
become more common components in
work practice and workplace design.
Most people already realise that this is the
big data era – it’s too obvious to ignore.
Less obvious but perhaps just as important
is this – the rise of big data means that this
is also going to be the machine learning
era.
Over the last three years, our cloud-
based Workplace Excellence Platform®
has already proven itself by identifying
up to 20 per cent reduction in project
time and up to 30 per cent increase in
output per head for more than 75 clients
23
About the author – John Blackwell
John is one of the top 100
global influencers in the
workplace field and is widely
recognised as the world’s
foremost thought-leader on the
changing nature of work and
effective business operation.
Drawing on a 35-year board-level career
with IBM and MCI, John implicitly
understands that opportunities for
innovation and investment must
continually balance the need to act
quickly.
John is a prolific author with more than
110 titles to his name, including;
A Mandate for Change
Managing Uncertainty
The Workplace of the Future
Challenging Perceived Wisdom
Smartworking
Unleashing Creativity, Flexibility, &
Speed
These and many more of John’s reports
can be downloaded from his online library.
A Fellow of the Chartered Management
Institute and a visiting fellow at three
prestige universities, to-date John and his
colleagues at Quora has inspired more
than 350 organisations to innovate new
work practices.
Working together, John and Quora
provide answers to problems that stifle
change, dismantle barriers, and overcome
corporate inertia to create effective new
work practices.
About Quora Consulting
Quora is a unique business consultancy
and provider of strategic solutions whose
forte is inspiring business leaders to
transform workplaces and work practices
through precision analytics and compelling
methodologies.
Our analytics help organisations focus
limited resources on critical decisions. We
provide frontline leaders with Net Present
Value clarity to ensure effective investment
decisions for;- attracting and retaining
talent; determining space configuration
and location; deploying technology
innovations; enhancing staff productivity;
and making fluent social, ethical, and
environmental decisions.
Our newly released Workplace Excellence
Platform has migrated our analytics,
methodologies and metrics to a cloud-
based platform. This offers organisations
an unequalled
opportunity to track
change metrics & KPI
progress in real-time
together with
simulating workplace
investments prior to deployment. We also
offer modules for automated space
utilisation assessment and similar.
For the first time, organisations can
fluently integrate internal and external
data to predict future workplace
behaviour, events, and demands.
24
A Quora Consulting Report
Authored by John Blackwell and published by;
Quora Consulting
Henley-on-Thames
Oxfordshire RG9 5LX
United Kingdom
Tel: +44 (0)1491 628654
e-mail: [email protected]
Web: www.quoraconsulting.com
Legal statement
Quora Consulting, its staff, advisory board, and any sponsors of Quora are not to be held responsible for any losses, expenses or any
claims arising out of any reliance on the information contained in this publication.
Whilst every effort has been taken to verify the accuracy of this information, Quora cannot accept any responsibility or liability for
reliance by any person on this publication or any of the information, opinions, or conclusions set out in this publication.
No copyright or intellectual property is transferred or should be assumed and all images, photographs, and trademarks remain the
property of their respective owners. No rights exist to reproduce this publication in any form or media in part or whole.
£200