Authored by Professor S. Sadagopan. Director – IIIT Bangalore. Prof. Sadagopan is one of the most experienced academicians on the expert panel of UpGrad & IIIT-B PG Diploma Program in Data Analytics.
These days, whenever I visit any campus, I am often asked one question – “Can I double my salary by enrolling in an Analytics course?”
This question is popular in academic institutes and software factories, alike. Mostly, it comes from engineering students (who are still in college) but is also often asked by those who have gained 4-5 years of work experience within the IT industry. I also get a smaller number of such queries from working professionals in other sectors – accountants, lawyers, doctors and design professionals. My answer is simple. YES – provided you meet some requirements. Let me elaborate.
The Computer Age
Analytics is today what computing was in the 60’s and 70’s when many of us entered the job market. The “computer” was a newfound tool that attracted the attention of scientists and engineers across the spectrum
Every day, some new use was found for the machine whose basic capability was adding a long list of numbers incredibly fast, without error, many times; later, an ability to store and retrieve a large set of numbers reliably and accurately, was added; much later, an ability to transmit and receive a large set of numbers extremely fast (at the speed of light), reliably and at affordable cost, got added.
This, in turn, led to diverse applications:
Accurate and fast census, so that census data can be used for economic planning
Precise weather prediction, so that disasters can be better managed
Library automation, so that book procurement and books issue/return can be efficient
Payroll to improve wage settlement accuracy and fast disbursal of payslips
Inventory control to optimize storage/stock-out costs
Scheduling to improve operational costs of air-crafts/trains/buses
Product mix to optimize the production/distribution/storage costs
Media planning to maximize the reach for a given advertisement budget
The ability to creatively use this “newly discovered toy” – the computer – to create unusual value was the newfound opportunity; it was quickly rewarded by unusual job/research opportunities across the board. Analytics, today, is in a similar situation.
Make Way For Data Analytics
- The maturity of mathematical models that Google/Facebook/Twitter and Amazon/Flipkart use,
- The opportunity provided by anytime, anywhere connectivity and near-infinite computing power provided by mobile networks and cloud,
- The near-universal access provided by smart devices (smartphones and tablets)
…all offer unusual possibilities and boundless opportunities.
- The identification (who?) and precise location (where?) made possible by device identity and GPS, can be used to target goods & services (what?) with a precision unparalleled in human history.
All this explains the huge wave of interest in Analytics, everywhere.
Reaping The Analytics Harvest: What Will It Take?
A larger issue that is often forgotten by students/young professionals is that a salary from corporation X is just part of a fee that the corporation realizes from its end customer (another corporation, government or end user) for a value that Corporation X created; and nothing else.
As the old saying goes, “money does not grow on trees.” Unless a working professional in a corporation (individually or collectively) creates such value, there cannot be a question of getting a salary (let alone “double my salary”) in a sustained manner.
Value creation can only happen when you have:
- A mindset to create value;
- The knowledge to create value and;
- Skills to realize the value
In the earlier era, to succeed in computing, you needed a computing mindset; similarly, to succeed in data analytics you need an analytics mindset. Students and young professionals would be well advised to ponder over the trio:
- An ability to make sense out of a set of numbers that are seemingly unconnected, an eye for patterns, sometimes an ability to visualize numbers/patterns that use the phenomenal amount of information available today; in other words, an analytics mindset.
- Over the years, a lot of analytics knowledge has evolved – statistical methods, optimization models, pattern recognition techniques, machine learning, artificial intelligence, etc. The student/professional wanting to enter the Analytics field would be well advised to master this knowledge base from Analytics courses that are now available from many institutes/universities /online platforms. In addition, for working professionals, the individual knowledge of his/her domain – manufacturing, sales, finance, service, agriculture, healthcare – would greatly help in articulating the ‘analytics value creation.’ (Check out the UpGrad-IIIT B PG Diploma in Data Analytics Program
- Finally, mastering a whole range of tools – like SAS, SPSS, R Programming Language, Excel extensions – and many more tools that will get created in the next decade, (the way programming and database tools evolved in 60’s & the 70’s) is going to be extremely crucial, as these are the skills which can enable one to realize the above-mentioned value.
If the students/working professionals who keep asking me this question “will I get double my salary if I do an Analytics course?” ask themselves whether they have the mindset for, and mastery of, the knowledge & tools; they will get the answer to the question themselves.
Also, it is important not to forget that one is rewarded for the value one creates the knowledge; simply possessing the knowledge does not help in creating the value. Getting the No. 1 rank in an Analytics course does not guarantee success; internalizing the knowledge and applying the acquired knowledge well is the key.
My best wishes for getting the result of the “double my salary” question or even triple!
Professor Sowmyanarayanan Sadagopan is the Director of IIIT-Bangalore. These are his personal views. He can be reached at email@example.com
Latest posts by Prof. S. Sadagopan (see all)
- How Can I Double My Salary? Data Analytics is your Answer - January 4, 2017
- Decoding Easy vs. Not-So-Easy Data Analytics - December 14, 2016
- Computer Center turns Data Center; Computer Science turns Data Science - May 11, 2016