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EDITOR’S QUESTION
WILLEM CONRADIE,
PRINCIPAL CONSULTANT:
BIG DATA AT P
BT GROUP
T
he world is abuzz with the Artificial
Intelligence (AI) hype. In fact,
Gartner estimated that the global
business value of AI will reach US$1.2 trillion
in 2018; this is more than the GDP of most
countries in the world.
With Gartner categorising the sources of
AI business value as enhanced customer
experience, new revenue, and cost reduction,
one has to wonder why the world is not
‘falling head over heels’ for AI and using it to
the business’s advantage to stay relevant as
pressure grows in the competitive landscape.
However, given that AI is mostly about
relinquishing control to an autonomous
entity that acts and makes decisions without
any human intervention, businesses remain
hesitant to invest in AI, given that they are
cautious of the ‘unknown’, or they simply
just don’t know how to go about introducing
AI practically.
With Machine Learning (ML) being one of
AI’s focal points, understanding data, for
the practical and successful implementation
of AI, has become more critical than ever.
This needed focus on data in turn means
that businesses are seeking to invest in the
data science role and ironically the rise of
data science is in fact leading the practical
introduction of AI into the corporate world.
While data science is not AI itself, it is top
of mind for every ‘data’ driven business
because it is the data scientists that actually
‘teach the artificial engine to become
intelligent’ through statistical descriptive,
predictive and prescriptive models.
Yet, the challenge in the local market is that
the data scientist skill set is very rare and for
many corporate businesses simply doesn’t
exist. In fact, the ‘unicorn’ data scientist
is rarely found, and if found, is generally
unaffordable to most.
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This reality is forcing businesses to employ
graduate, or lesser experienced, data
scientists straight out of universities.
However, this affects a business’ ability
to implement the data science needed in
an operational environment for sustained
benefit (often referred to as the ‘last mile’ in
data science).
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In fact, very few businesses succeed at
deploying ‘sustained’ data science as while
graduate data scientists are highly educated
for their trade, they tend to not have the
necessary experience to deploy the data
science operationally.
So how can businesses achieve sustained
data science given these realities?
Sustained data science requires solid
governance, architecture and data
engineering processes, where the value of
data science lies in the complete end-to-end
life cycle, which includes the ‘last mile’.
This is all about good data science
governance, linked to the data science
process, business and technical architecture,
model management, model performance
“
DATA SCIENCE
IS FOR MANY AN
ACCEPTABLE WAY
OF INTRODUCING
AI INTO THE
CORPORATE
WORLD.
monitoring, systems monitoring and
business continuity strategy trust, which is
often the most difficult element.
Considering this – and given the realities
linked to data science in the local market
– data science is for many an acceptable
way of introducing AI into the corporate
world, given that it is typically performed
by a person and with that there remains an
element of trust.
The other option in this regard would be to
succumb to the data scientist challenges and
as a result ignore AI completely and hope the
hype dies down. However, the lost opportunity
of this outlook could be substantial.
Businesses that introduce AI, despite the
perceived risks and associated challenges
and who succeed, will get the first bite at the
US$1.2 trillion pie. This is where good data
science governance practices play a vital
part. It’s difficult to eliminate all the risks of
AI in corporate environments, but it is very
possible to manage it.
INTELLIGENTCIO
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