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”.
A lot of new joiners in analytics teams across companies face a serious problem especially while describing the outcomes. Do 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 partial to be blamed. They are taught to play with the software. It could be coding or work 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 inquiries 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, the 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 the 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 in the data.
 Cognizant Research Center, Hyderabad;Sanjay.firstname.lastname@example.org
 Insurance Information Bureau of India, Hyderabad; email@example.com