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1. WHITE PAPER:
Big Data &
Predictive Analytics;
THE CHALLENGES AND OPPORTUNITIES FOR ALL.

2. INTRODUCTION
ABOUT THIS DOCUMENT
UNLOCKING THE POWER
OF PREDICTIVE ANALYTICS
DEFINING PREDICTIVE ANALYTICS
THE FIVE KEY UNDERSTANDINGS
WITH BIG DATA AND PREDICTIVE ANALYTICS
WHAT IS POSSIBLE TODAY?
THREE PREDICTIONS
FOR THE FUTURE
QA
APPENDIX; INDUSTRY ATTENDEES
OF THE SYDNEY AND MELBOURNE EXECUTIVE BREAKFASTS
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14
Contents

3. 02
Big Data remains one of the most significant
opportunities for business, but also one of the
most poorly leveraged. In numbers, only 0.5 per
cent of data is ever actually analysed, meaning
that organisations are not only wasting precious
resources collecting and storing data, they are
missing out on the significant insights that data
can provide back into the business.
What businesses are good at is the capture of
raw data. Studies have found that 90 per cent
of the world’s data has been created in just the
past two years, and the rate of data collection
is not slowing down – we are expecting an
increase of 4,300 per cent of yearly data
production by the year 2020.
This rate of data creation – and capture – has
the potential to exasperate the overwhelming
challenges that businesses are facing in data
analysis. Businesses are aware of the need to
scale their investments into Big Data analysis.
A Wikibon forecast expects the global Big Data
market to grow from $18.3 billion in 2014 to
$92.2 billion in 2026 – a compound annual
growth rate of 14.4 per cent. IDC, meanwhile,
predicts that through 2020, spending on cloud-
based Big Data and Analytics technology will
grow 4.5 times faster than spending on on-
premises solutions.
Perhaps the most clear indicator of the raw
demand for data solutions can be found in the
venture capital space, where Big Data startups
picked up 11 per cent of all tech venture capital
in 2015. Big Data is not just a trend – it’s
equal parts pain point and opportunity, and
organisations are keen to resolve the former
and capitalise on the latter.
Introduction
“Artificial Intelligence, or more correctly Machine
Learning, is a powerful new technology that
must be understood by every company. The
potential impact that AI/ML will have on
businesses is significant and will enable
companies to provide new services and drive
greater insights more quickly. Every executive
must understand this opportunity and what
impact it could have on their operations.”
–David Thodey, Chair CSIRO

4. 03
This white paper looks at where
the biggest data opportunities are,
and how businesses can capitalise
on these by better understanding
what solutions will be of benefit for
their business.
Unico in collaboration with Odgers Berndtson engaged senior enterprise
executives from a wide cross-section of industries during two business
roundtables in October 2016. Please see the attendance list in appendix one.
The insights from these discussions are captured throughout the document.
From page 14 you will find a Q A section, in which the industry attendees
highlighted their key questions around the implementation of and use of
predictive analytics and data analysis.
About this document
This white paper was generated to
capture real world experience and
thinking in data use and analysis.

5. 04
A KPMG study found that for CEOs, Big Data
and analytics was a top-3 investment priority.
Two thirds of CEOs, according to the report,
were concerned that their organisation wasn’t
being disruptive enough, and this concern was
in significant part driving interest in data; CEOs
saw it as a path in developing new products
and services, while realising greater savings
and efficiencies.
For the CFO, Big Data offers the opportunity
to develop a broader understanding of a
businesses’ ever more complex corporate
governance responsibilities. For these
executives, moving from a retrospective an
intuition-driven decision making process, to
one based on data, will help the business
be more proactive and holistic in the way it
handles its economics.
The CMO benefits too. Currently, marketers see
predictive analytics as a holy grail to their work.
A Forrester Consulting study found that 89 per
cent of B2B marketers had identified predictive
analytics as being critical to their roadmaps
in 2016, and that 78 per cent of them see B2B
marketing as expanding to deal acceleration
from demand generation. From that statistic it’s
easy to deduce that predictive analytics will play
a key role in a critical redefinition of marketing
in many businesses.
Unlocking The Power
OF PREDICTIVE ANALYTICS
Most members of the C-suite community now
regard Big Data and analytics to be a critical part
of their role into the future.
“It is apparent that boards and executive teams are under enormous
pressure to land on a ‘future’ operating model for their businesses.
A robust and well tested strategic plan that is dynamic and data
driven is essential for the new digital era and therefore the demand
for executives that understand this data driven world is significant
and will only continue to grow especially when we consider how
wrong we can be if we don’t use all the tools at our disposal.”
–Paul Rush Partner, Odgers Berndtson

6. 05
Once an organisation has collected and
centralised its data, it is then ready to
have predictive analytics applied to it, in
order to derive key pieces of information.
It is important that organisations do
this step; without the analysis applied
to the data, the ROI in collecting and
storing the data is non-existent and all
the data will be good for is searching.
It’s in the analysis that the value of big
data is unlocked.
Predictive analytics specialises in
making use of unstructured data, or
what we generally refer to as ‘Big Data.’
Structured data makes use of fixed fields
– spreadsheets or relational databases,
for example. Unstructured data includes
photos, webpages and digital documents;
it’s the data that isn’t meaningful when
placed into neat boxes and categories.
The concept of Big Data can also be
understood in two ways. It could be
related to the number of samples
or observations that exceed certain
threshold; or it could be the number of
dimensions exceed certain number, even
Because predictive analytics includes automated processes, applications of the
technology often have strong Machine Learning capabilities built in, in order to
automatically “learn,” adapt, and update as new data is collected.
But Machine Learning is not always going to generate the best results from the data,
either. If the data is highly linear, then an investment in Machine Learning can be
wasteful and inefficient. For this reason it’s important to fully understand the nature of
the data before developing the algorithms with which to analyse it.
if the number of samples is relatively
small. For an easy example, researchers
might have DNA genes data consisting of
more than 10,000 genes per sample, but
have less than 100 samples. This is still
Big Data. In general, we classify small
data as having equal to or less than 15
attributes, medium data of between 15 –
25 attributes, and Big Data as more than
25 attributes.
There are two primary challenges in
leveraging Big Data; the first is that
unstructured data is proving very difficult
to leverage by organisations, , and with
around 80 per cent of all data being
unstructured, tools and services that help
organisations make sense of it all, such
as predictive analytics, are immeasurably
valuable. The other challenge, however, is
that for machine learning or data mining
algorithms, the high dimensionality
of many examples of big data is a
significant technical challenge that needs
to be overcome - often through custom
algorithms - before an organisation will
be able to derive meaningful and accurate
insights from the data.
Predictive analytics can be
used to:
• Predict future trends/events
• Identify patterns
• Identify casual relationships
between things
• Image recognition
• Text mining and processing
Defining
PREDICTIVE ANALYTICS

7. 06
UNDERSTAND THAT NOT
ALL DATA IS EQUAL
The sheer weight of data being created means
that it’s simply impossible to give every byte
of it equal weighting. To make effective use of
data, and the solutions that are implemented
to leverage it, enterprise needs to focus on the
nature of the business and the kind of data it
will derive best value from, and then identify
how it captures and applies analytics to the
datasets. In effect, we need to get back to
basics and understand and redefine Big Data
before we can start to work with it.
Data can be split into three distinct categories:
structured, unstructured and semi-structured.
Structured data is what people typically
visualise when they think of data; it looks like
numbers in a spreadsheet. This is relatively
simple to analyse.
Unstructured and semi-structured data is
more difficult to analyse, and might take more
unusual forms such as:
• Any digital image has data behind it which
can be analysed.
• Word documents full of words that can be
analysed with text processing algorithms.
This is harder to analyse, but forms most of
what we term ‘Big Data’, and is where most
information is actually contained.
Five Key Understandings
WITH BIG DATA AND PREDICTIVE ANALYTICS
Capitalising on the opportunities that predictive
analytics enables requires business to take
a measured, five-prong approach to the
planning, rollout and subsequent management
of technology solutions:
01

8. 07
NOT ALL ‘ANALYSIS’ IS EQUAL
(AND PEOPLE ARE A CRITICAL
PART OF THE PROCESS)
There is a difference between analysis and
analytics. Analysis unlocks the true value of
data by extracting meaningful insights from
what has been collected through the analytics
strategy. To put it simply: Data analytics without
analysis is just data.
It is important to have a human element
involved in all analytics strategies to properly
extract the insights behind what the data
is telling us, and to hold the data analytics
ROBUST ALGORITHMS
Evolving technologies such as we’ve seen
in Hadoop, parallel processing, and cheaper
storage techniques, are combining to make
the collection and storage of data easy for
businesses of all sizes. However, data of that
size and scale also requires advanced and
robust algorithms to be able to leverage the
insights in a productive and efficient manner.
algorithms to account. Machine Learning and
robust algorithms are certainly able to take the
busywork out of a data scientist’s role, however,
it is important that the role not be made
redundant; instead the role of a data scientist
should transition to something more strategic to
the business, and data scientists in the future
will need to have a better understanding of the
value and purpose of data at all other branches
of the business’ operations.
Most critically, organisations need to consider
developing bespoke algorithms that speak to
the data that is important to their business.
Algorithms and their formulas need to be able
to cut through the “noise” of the sheer mass
of data out there, and collect (and analyse)
only the relevant economic, locational and
behavioural data sets.
Five Key Understandings
WITH BIG DATA AND PREDICTIVE ANALYTICS (CONT.)
02
03

9. 08
Five Key Understandings
WITH BIG DATA AND PREDICTIVE ANALYTICS (CONT.)
WE NEED TO UNLOCK THE
VALUE OF MACHINE LEARNING
If the data is non-linear, it needs to have
Machine Learning applications applied to be
able to generate meaningful insights over
the short, medium, and long term. Machine
Learning is, basically, the function that helps
technology get better with time.
Machine Learning is important to the analysis
of large quantities of unstructured data, as we
find in Big Data. Solutions that have robust
USING DATA TO PREDICT OUTCOMES
The key purpose of the collection and analysis of Data is to identify trends and opportunities, and to
use that information to gain competitive advantage to better position the business.
It’s easy to fall into a trap whereby an organisation draws data into the business and then extracts
insights looking backwards; at what it already understands. Properly configuring the Big Data
insights strategy from the outset is essential to commercial success.
Machine Learning processes built into them
are able to extract value from the multitudes of
sources that input data into an organisation, and
do so without human supervision. Furthermore,
Machine Learning applications become better
the more data that is fed into them. With this
in mind, effective Data Analytics projects in
the future will implement Machine Learning
processes as standard.
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10. 09
Three predictions
FOR THE FUTURE
What does the future hold for Big Data and
predictive analytics? Based on the predictions
and trends for the current state of the Big Data
industry, we can expect three key themes to
emerge over the coming years:
BIG DATA IS ACCELERATING
INTO EVEN BIGGER DATA
The first trend, as noted earlier in this paper,
is that Big Data is only going to become
more overwhelming in terms of how much
is captured and stored, both in terms of
structured and unstructured data.
This presents challenges. When businesses
as a whole are currently only analysing 0.5
per cent of the data coming into the business,
whatever challenges they are facing in
increasing that percentage will be exasperated
as the organisation brings in more and more
data. At the same time, a greater understanding
of what can be done with data, as well as
01 improved tools, Machine Learning, and analysis
strategies, there is also going to be much
greater datasets to play with, which provide in
turn much better quality data to draw insights
out of.
We can also expect to see an increase in
complex data analysis being done in real time,
assisted by automation and Machine Learning.
Previously this has been difficult to achieve, but
now it’s possible to generate actionable insights
out of your data as it comes in; for example,
model parameters can be updated in real time
as data becomes available.

11. 10
Three predictions
FOR THE FUTURE (CONT.)
AUTOMATION WILL DEMOCRATISE
DATA, AND MAKE IT VALUABLE
TO ALL WITHIN THE BUSINESS
It will become important that all people within
the organisation, from CEO to marketing and
on to HR, will be able to use data in their
role. Gartner’s now-infamous prediction that
an organisation’s marketing team will spend
more on technology than the IT team by 2017
stems in no small part from the expected
increase in spending on data.
DATA SCIENTISTS WILL BECOME
ONE OF THE MOST VALUABLE
RESOURCES IN COMPANIES
Australia is facing a skills shortage as
data scientists become more and more in
demand. This is going to push up wages and
movement between jobs, and given that data
scientists will be in demand across most
industries, businesses will need to develop a
strategy to obtain – and then retain – the data
scientist talent.
Third parties might be a solution, with
organisations gaining access to data scientist
skills by engaging with a trusted partner for
building the analytics strategy. For others, 457
visas or outsourcing overseas might be the way
to go.
02
03
Equally, SMEs can expect to have access
to increasingly sophisticated tools for data
analysis, as these technologies become more
established in the market. In order to assist
individuals and businesses make use of the data
across the organisation, and to compensate for
a lack of internal skills in SMEs, automation will
become a more prominent tool that businesses
invest in as past of their Big Data spend.
Data scientists will be in high demand because
they will provide businesses with competitive
advantage. There will be off-the-shelf analytics
solutions available, but businesses will know
that if their rival uses the same analytics that
they are, there will be no competitive advantage.
Instead, these businesses will turn to their data
scientists to develop bespoke applications that
will give them access to data insights that their
competition does not have.

12. 11
QA
How much time do you spend
setting up the framework - what
are the timelines?
Why bespoke and not
off-the-shelf?
Across two senior executive roundtable
events held in Melbourne and Sydney
in October 2016, business leaders were
engaged in a discussion on how Big
Data and predictive analytics can be
applied to their own businesses and
business practices. These questions
were a key focus for enterprise:
The time frames involved in setting up
and executing an algorithm can vary
substantially, and can be anything from
a couple of weeks to months in duration.
Factors that can affect the rollout time
include; the front end and the kind of
There are advantages to both bespoke
and off-the-shelf solutions. If your Big
Data goal is simply to be able to search
and extract data, then there are off-the-
shelf solutions that can comfortably meet
those needs, from a variety of vendors.
The added benefit of these solutions
is that they are significantly more cost
effective to get up and running than
bespoke solutions.
Bespoke solutions have the advantage of
being highly customisable; for example,
reports that are to be generated, the
amount of data that needs to be input,
and whether there needs to be a period
of collecting new data in order to test the
strength of the algorithm.
locational data is often important, and
often difficult to derive real understanding
from with an off-the-shelf solution.
Of even more interest to a lot of
organisations is that bespoke solutions
provide genuine competitive advantage.
You can be fairly certain that if you’re
using an off-the-shelf solution, then so
too is your competitors. But the insights
delivered through a bespoke solution
are likely to be to the benefit of your
organisation alone.

13. 12
QA
(CONT.)
How do you embark on a proof
of concept for one of these
things when it can be especially
difficult to convince large
organisations to make these kinds
of expenditures?
How should the data transfer be
interpreted? Is it something that
can be technology-driven, or is
human involvement important at
this stage of the analysis cycle?
How do I deal with concerns
within my organisation
that staff fear of their
jobs as a consequence of
Big Data analytics?
Businesses are vaguely aware of the
need to invest in ‘big data’ already, so for
data scientists or IT executives to sell up
into management or the board, there is
already a base awareness there.
The ability for predictive analytics to
allow an organisation to get on the front
foot with its competitive strategy is the
There is absolutely the need to have
human eyeballs looking at various points
in the analytics cycle. Automation and
machine learning is very effective in
collecting data and creating meaningful
insights out of it. However, applying
those insights to real-world scenarios
requires a human understanding of the
data as well.
It’s true that the insights generated by
Big Data algorithms might highlight the
redundancy or inefficiency within some
roles within the organisation, however,
we see Big Data as a net creator of
jobs, and those staff would have the
opportunity to take on new roles within
the organisation.
For example, we created an algorithm
for one organisation, and over a period
of time the team managing it grew to
include a head data scientist, and three
people working underneath him. Because
salient point that needs to be made. Being
able to refine margins, staffing numbers,
inventory held, and so on based on real-
time information is a compelling business
case as organisations look to clamp
down on unnecessary wastage in their
spending, and find new and effective
ways to reach customers.
It’s also important to have skilled data
scientists looking at the insights, in order
to understand whether abnormalities
are signs of new trends, or otherwise
whether the algorithm needs updating to
meet changing dynamics in the market.
the insights generated from big data
tend to be so valuable, organisations
generally like to properly resource
their analytics teams.
Getting the staff on board with a Big
Data analytics strategy does require a
change management strategy to come
from the executive team, in order to
get everyone on board, but over the
longer term the career opportunities that
these technology solutions enable will
be compelling for staff at all levels of
the organisation.

14. 13
QA
(CONT.)
Is it true that some machine
learning algorithms ignore
clusters of data that it doesn’t
understand or are new?
How do you determine
weights for inputs into the
analytics algorithms?
What is the difference between
Machine Learning and
Deep Learning?
This can happen, and this is why it is
important to have skilled data scientists
looking at abnormalities in clusters of
data to determine whether the algorithm
needs to be adjusted.
The short answer is that you shouldn’t
be manually assessing weightings. The
mathematical algorithm should be
robust enough within the parameters
of defining the relationship between the
thing that you want to forecast, and the
explanatory variables.
Deep Learning is “the analysis and
learning of massive amounts of
unsupervised data, making it a valuable
tool for Big Data analytics where raw
data is largely unlabelled and un-
categorized.” Effectively, it is much the
same thing as Machine Learning, with
some variations in how the algorithm
is applied.
A good algorithm will also throw up red
flags when the clusters of abnormal data
are significant enough.
With bespoke Big Data analytics projects,
the mathematics team will approach
the problem with the understanding of
the outcomes, or the narrative that the
analytics will create for the business. The
weightings will be built into the equation
with that goal in mind.
Businesses should not be relying on a
single form of algorithm for their insights.
With two or three different algorithms
working on a single big data problem, the
insights being derived from the analytics
team will be more rounded and, therefore,
beneficial. Organisations should therefore
be investing in both Machine Learning
and Deep Learning analytics solutions.
“The success of data analysis relies heavily on the relevance
and quality of the source data and inputs, domain expertise
is critical. Best practice analytics brings together business,
science and technology to create a powerful differentiator.”
–Michael McKeon, Business Development Director,
Unico Enterprise Services

15. 14
Appendix; Industry Attendees
OF THE SYDNEY EXECUTIVE BREAKFASTS
ANDY HEDGES
ANTHONY LAU
CAMERON GARRETT
CHRIS EARNSHAW-NEES
DAVID BIGHEL
JASON JUMA-ROSS
KAREN LAWSON
KYLE BUNTING
MICHAEL MCKEON
MICHELLE ZIVKOVIC
PAUL RUSH
PETER BINKS
RICHARD MCMANUS
TIM SCHNEIDEMAN
Leadership Innovation Global Executive
ALAUD
Macquarie Group
Artis Group
ASX
Facebook
Slingshot Accelerator
TPG Telecom
UNICO
Directioneering
Odgers Berndtson
Crystal Bay Capital
Richard McManus
Hewlett Packard Enterprise

16. 15
Appendix; Industry Attendees
OF THE MELBOURNE EXECUTIVE BREAKFASTS
ADAM KYRIACOU
ANTHONY DEEBLE
ANTHONY MAGUIRE
BEN CHESTERMAN
DAVID ROBINSON
GENEVIEVE ELLIOTT
HAMISH COLEMAN
LARRY HOWARD
LOUISE HIGGINS
MARK GILBERT
MATTHEW PERRY
MICHAEL ALF
MICHAEL MCKEON
MICHELLE FITZGERALD
PAUL RUSH
PETER GAIDZKAR
RAPHAEL OWEN
RICHARD NEESON
ROBERT TURNER
Odgers Interim
Val Morgan
Telstra
Telstra
Assetic
Vicinity Centres
Vicinity Centres
Bluescope Steel
NOVA Entertainment
Telstra
DuluxGroup
KPMG Enterprise
UNICO
City of Melbourne
Odgers Berndtson
Reapit
Val Morgan
Original IT
Assetic