FINAL WORD
“
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.
solutions for other areas can be also be
discovered along the way.
Islam Zeidan, General Manager UAE and
MEA, Teradata
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
software applications for end-users to
painfully navigate through.
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
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.
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
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.
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 customers’ 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,
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