Coriander Leaves, Curry Leaves, Statistics & Analytics…
Analytics is booming in India. There is a lot of demand in every sector to embed data mining into our everyday work. Companies are scrambling for skill sets which would guide them in their business; however, they are not able to find candidates with the right skill sets in India. In a recent survey by LinkedIn, it has been shown that for the past 2 years Analytics has been the most sought after job and stands top among the top 15 hottest jobs across the globe. There has been a monumental rise in the number of employers who look for Data Scientist and Analytics professionals.
This trend was not prevalent at the turn of the century (2000-2003). And the few companies which started their analytical businesses have sold their companies to bigger ones and made millions of dollars. The need for analytics actually started by 2005 when companies involved in Insurance and Banking sector started hunting for Statistics qualified graduates in Colleges and Universities. Until then students did not show much interest in Statistics and Analytics. But suddenly the number of students venturing into the study of this subject grew exponentially.
Now, when we talk of Analytics, the usual terms associated with it are Statistics, Data Mining Skills and people who worked in data, etc. Do you think we never had professionals who worked in data related activities in the past? Sure we did. We had several experienced professionals who worked in data, drawing charts, deriving inferences from the trends and graphs, etc. In particular, people who worked in databases had sufficient knowledge of generating reports and drawing inferences. They knew how to merge multiple tables and extract valuable information from raw data.
There is also another reason for the boom in data related work. In early 2000s, many companies which captured data at every level did not bother to store them. They simply discarded the data as they felt they were useless. Also, the storage cost was higher during those days. But the scenario changed. Since 2005, many companies are able to store large volumes of data in cost effective devices and were also able to extract valuable information that would help them in decision making. So the primary purpose of all sorts of Analytics is forecasting and decision making. It is also worth mentioning that much of our work in Analytics was towards post implementation of the statistical model, tracking reports and budget allocation that were built surrounding the analysis which was derived from historical data.
The Current Situation
Therefore, the skill sets which employers are looking for are much more than just reporting and drawing inferences from graphs and charts. We need skill sets to segment the population, build mathematical models that would eventually facilitate budget allocation, targeting the right shoppers for promotional campaign, identifying high-risk customers, increase sales through cross-sell and up-sell targeting, unfolding the characteristics of current customers through profiling, building retention model to avoid churning of profitable customers, etc. The solution to all these problems arises from the large historical transactional data.
We also understand that statistics plays only a small part in this big problem solving process surrounding the data. Spending considerable time in understanding the problem statement and preparing the data are more important tasks than the application of Statistics and building models. Though the amount of Statistics work is less, it is nevertheless a daunting task for qualified professionals. This subject has not been recognized for its real worth. Only now, the mind-set is changing.
In conclusion, it can be said that Statistics has been used in every part of business across different verticals, but unfortunately, valued less. Actually, it adds flavour to our analytical work even though we intend to spend only a small amount of time, very similar to the coriander leaves and curry leaves that enhance flavour to the various dishes that we prepare but are less honoured during our assessment of the food.