business
‘‘
TALKING
problem. By focusing on the cause and effect
of the problem and its solution, it is easy to
see other factors that can be modelled along
with the primary problem and its solution.
Data scientist teams that work with
businesses to find solutions to their
problems, are often cross functional teams
that leverage each other’s skills at various
points of time during a project. Hence the
nature of the team is sometimes fluid and
sometimes changing based on the demands
of the project as it progresses.
Islam Zeidan, General Manager UAE and
MEAD, Teradata
software applications for end-users to
painfully navigate through.
Other than finding solutions and reducing
complexity, data scientists also focus on
two other objectives. One is building and
planning for failure as a natural part of the
process of finding a solution. Data scientists
learn from failures but because the processes
are of much smaller scope, their impact
on time and cost is also minimised. This is
referred to by data scientists as AnalyticOps.
solution based on the business expectation
of the end-customer. The objective is not
to build complex models and complex
solutions increasing the challenges faced
by end-customers.
By finding a solution to an end-customer’s
business challenges, data scientists can
be enablers in their financial productivity.
Even a 1% saving in direct costs can add to
substantial profits at the end of the year.
These improvements can range across any
aspect of the operations, from reducing the
amount of paper used, to how much time
employees spend in lifts, to how employees
gain access to information technology
resources, to downtime of industrial
machinery, and how the fleet is deployed in
a supply chain.
The list is endless. Data scientists, therefore,
focus on being successful in finding answers
to problems rather than rolling out complex
www.intelligentcio.com
Irrespective of the progress and setbacks, the
target is always to add to the bottom line of
profitability of the business, by optimising
costs and processes or gaining insights into
what the business and its customer’s need.
The other objective is to broadbase the
efforts to find a solution to a particular
problem into other areas as well. Often
while looking for a solution to one problem,
solutions for other areas can be also be
discovered along the way.
So, while data scientists may have a particular
objective and may be fixated on finding that
solution, in the shortest possible time, there
are other opportunities and discoveries that
may present themselves, that can be tackled
along with the primary objective.
Data scientists focus on the problem in
its entirety and the adjacent ecosystems
that influence the parameters defining the
Take the example of Amazon’s revolutionary,
counterless, check out process: Amazon
Go. To enable a checkout-less consumer
experience, Amazon had to first address the
challenge of digitally monitoring the stock
items on its shelves. Also required was the
movement of stock keeping units to the
consumer with a particular login. Building
geofencing applications to take care of these
requirements was a key part of rolling out
the Amazon Go, retail check out experience.
While focusing on tackling the bigger
problems, solutions for many of the smaller
problems seem to fall into place, much faster
and easier, than if they were attempted
singularly, one by one. n
“
THESE
IMPROVEMENTS
CAN RANGE
ACROSS ANY
ASPECT OF THE
OPERATIONS –
FROM REDUCING
THE AMOUNT
OF PAPER USED,
TO HOW MUCH
TIME EMPLOYEES
SPEND IN LIFTS.
INTELLIGENTCIO
23