This article was first published in the Analytics India Magazine on June 23rd, 2015; Co-author: Sanjay Fuloria (Cognizant Research Center, Hyderabad)
Big Data Talent Gap is a serious problem. Recognizing
it, Universities are introducing courses on Analytics and Big Data. This has
resulted in a larger supply of people who are well versed with data
manipulation, handling and running codes. But, it has created a gap of another
type.
In an article on “Three Problems All Data Scientists Experience”, Drew Farris of Booz Allen Hamilton Inc. writes that “…problems go beyond technology and machine learning and are broadly encountered regardless of the task at hand: interpreting the problem, sourcing the data, and describing the outcomes”.
In an article on “Three Problems All Data Scientists Experience”, Drew Farris of Booz Allen Hamilton Inc. writes that “…problems go beyond technology and machine learning and are broadly encountered regardless of the task at hand: interpreting the problem, sourcing the data, and describing the outcomes”.
A lot of new joiners in analytics teams across
companies face a serious problem especially while describing the outcomes. They
fail to understand what their effort will lead to? This effort could be the
software code they are working on or the project module that is assigned to
them. Why only new joiners? Even employees with 6 to 7 years of experience find
it difficult to look at the big picture. The institutes where they learn these
analytics’ techniques are partially to be blamed. They are taught to play with
the software. It could be coding or working on the dime a dozen graphic user
interfaces that are available. They understand how to handle data, get the
results and interpret the data. How this interpretation would lead to business
gains or efficiency gains is not clear to them!
A simple example could be a segmentation exercise
where the collected data is used to segment customers into various groups.
These groups could be divided demographically or by using the customers’
choices and preferences. Once this segmentation is done, each segment can be
profiled both on the basis of demographics and choices. Up to this point, all
analytics greenhorns would do a perfect job. The next step is where
complications arise. When they present this to the client, the client enquires
about the usage of this exercise. They do not have an answer to this. If they
can tell the client how each segment can be uniquely targeted using specific
marketing campaigns and what amount of efficiency gains they would achieve, the
client would be delighted. If this is done correctly, apart from the short term
gain of client appreciation, they can expect long term career growth
opportunities.
With so much of data available through various
sources like smartphones, internet and social media sites, the requirement for
experienced analytics professionals is bound to grow. The beauty of the
situation is that this data availability is only going to increase with the
advent of internet of things. In internet of things, devices will talk to each
other with an app on your smartphone helping you to switch on your television
and air conditioning just before you enter your home. A stage will come when
the data of your home arrival times can be analyzed and the app will trigger
the switching on of your devices
automatically without you even tapping it.
We also keep hearing of big data silos across data
stores within the same organization. This happens because people with skills in
data analytics do not understand which problem can be solved using the unified
data. If they can be exposed to such problems and solutions, a lot of data can
be unearthed from data warehouses and used productively.
There is an urgent need for institutions teaching
analytics courses to equip their students with the ability to look at the
larger business problem and then use their data skills to solve that. Instead
of starting with the data, they should start with the business problem and
while working on it they should not miss the woods for the trees. This can be
done easily when the focus is on the business problem and not on the data.