The analytics-driven CFO
Using advanced predictive analytics
to drive financial success
3 of 5
Barriers to becoming
an analytics-driven
CFO
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5 Article series: The analytics-driven CFO
Using advanced predictive analytics to drive financial success
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Barriers to becoming
an analytics-driven CFO
There are no barriers that can’t be overcome
on the journey to an analytics-driven finance
function. In fact, many barriers are only human
perceptions and can be overcome rapidly with a
shift in mindset.
Facts do not cease to exist
because they are ignored.
Real barrier: Labour – skills & knowledge.
But not how you may think.
Traditionally, a CIO would design a technical architecture
based on the organization’s needs. Once designed each
element of the technology stack would be chosen, software
and system purchased and the right people employed to
implement and run it.
This is no longer the case; today there are literally
thousands of open source solutions to do the job of
analytics and that number is growing all the time. With the
unbelievable pace at which open source software is being
developed, deployed and delivered, the CIO will need to
change their thinking to remain ahead of the curve.
—Aldous Huxley
So the limiting factor will be the people able to drive
outcomes from analytics. Rather than the technology
dictating the people you employ, as has been the case until
now; it will be the reverse. People will join organisations
and bring open source expertise and experience with them.
And this will determine how and what is used to answer the
analytics hypotheses of the business. Of course, these skills
can be learned but that takes time.
CFOs will need a combination team of finance, business,
science and analyst to best service their business. Largely,
the CIO’s team will provide the technical capability (analytics
production) while the CFO’s team must be equipped for
analytics consumption. The CFO is there to action the
insights provided and shape future outcomes by designing
actions that will shift, consolidate or enhance the outcome.
CFO
Analytics consumption
kk The human element of consuming and actioning
predictive insights.
CIO
Analytics production
kk Generating insights through techinical capability
Figure 4: To function effectively both CFO and CIO must work closely to generate and action analytics insights
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Perceived barrier:
Privacy and security in the cloud
Perceived barrier:
The rare data scientist
Many would say that privacy and security around cloud
are a barrier to analytics. We no longer believe that this
is the case. We’re getting better at managing security and
privacy all the time and cloud is becoming the preferred
option for many transactional and non-core systems. New
data structures are changing how we store information and
debunking the privacy/security myth.
There is much chatter about a lack of data scientists in the
market. Our view differs. The simple fact is, data science is
just science within a new discipline. Rather than chemistry,
biology or physics, it’s science applied to data. A scientist
will design an experiment or test a hypothesis. The same
goes for data science. We work out what we want to achieve,
i.e. our hypothesis, and then we run experiments, with a
control group, and develop predictive scores.
In the financial world, block chain is a terrific example
of a secure data structure that supports a continuously
growing list of transactional records to be protected against
tampering and revision in a distributed database. The
essence is data structure blocks - which hold batches of
individual transactions. Each block contains a timestamp
and information linking it to a previous block. Bitcoin is
one of the most well-known ‘permissionless’ block chain
examples. You don’t need to sign up, log in or apply for
access; you can just go ahead and join in.
This ability to completely decentralize while helping with
data security is going to revolutionize a number of security
mechanisms globally.
This isn’t new; rather than running an actual physical
experiment as with traditional chemistry, physics or biology,
we run it over data. So, anyone with a general science
background will be able to adapt to data science if they
so choose.
What is perhaps more interesting is the trend already to
specialization within data science domains. Cyber, retail next
best offer and financial data scientists are already a reality.
It’s about focusing on a specific value outcome and honing
deep skills in that one area.
In the CFO’s world, specialists will include those that can
drive value from the predictive insights generated off the
P&L and balance sheet. Shaping actions to improve outcomes
will become an art form and a deep area of expertise.
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Work closely with the CIO
to ensure the technical
capability or analytics
production is meeting the
needs of the organization.
Design your team to
consume analytics –
that is developing and
implementing the actions
that will change future
outcomes.
Understand the differing
roles required and train your
existing team (or recruit
new members) to deliver
in a fact-based decision
environment.
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The analytics driven CFO
1
Getting value from
data and analytics
2
The key to success for the
analytics-driven CFO
3
Barriers to becoming an
analytics-driven CFO
4
Three steps to becoming an
analytics-driven CFO
5
Driving change to an
analytics-driven culture
EY | Assurance | Tax | Transactions | Advisory
About EYC3
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