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Applying machine learning to the workplace

…signalling a better way forward

A Quora Consulting Discussion Brief

Authored by John Blackwell

© Quora 2017

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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

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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

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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.

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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.

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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

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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.

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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

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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.

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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.

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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

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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

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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.

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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.

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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

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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

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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

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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

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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: info@quoraconsulting.com

Web: www.quoraconsulting.com

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