Prevailing Myths About Analytics Career

While reading to many blogs and question answer in many communities, what I keep encountering is the common question which is actually a myth about an Analytics Career. There are many aspirants may be dropping the idea of becoming analytics experts. So here we will discuss and try to find out the truth. So let us start. Here are some the myths I gathered to take on them:

You need to be an engineer to start a career in Business Analytics:

The truth is that you don’t. All you need is the ability to think structurally and comfort with number crunching. As long as you can put structure to unstructured problems and perform back of the envelope calculations, you are as good as any analyst out there. Having said that, companies prefer people from a quantitative background as they are expected to be better with numbers. By quantitative background, I mean people from any of these disciplines: Engineering, Economics, Maths, Statistics, Physics or MBAs with graduation in these fields.

Analytics is about working with large datasets / Companies work with big data day in and day out:

This rosy picture is far from reality in most of the Organizations. Experts estimate penetration of big data to be in low single digit percentage among Organizations. Most of the time analytics team work on specific problems, which may or may not involve large datasets. The requirement of the role is to be able to put structure across unstructured problems and be able to use numbers to understand business and the changes required in strategy.

You need to be a programmer:

I was a good C++ programmer when I started my career in Analytics. Sadly, none of those skills have been utilized in last 7 years and might not be utilized in future. You only need to learn to program for the tool you use for your analysis (e.g. SAS, R, SQL etc.), but you don’t need to be a good programmer beforehand to learn these. Also, most of these tools have a Graphical User Interface (GUI), which you can start using without knowing programming.

Learning Analytics is all about learning a tool (SAS / SPSS / another tool):

A tool is just a tool to perform Analysis. It can not perform analysis on its own. You need to understand the fundamentals required for performing analysis like:

What are the things you need to keep in mind while performing Regression?
What can you infer from the coefficients and outcome of t-tests?
How do you prove or disprove a business hypothesis?

Once you understand these, applying them through any tool can help you start your journey of Analytics.

It’s difficult to find a job:

In fact, it is the other way around. Analytics industry is struggling with attrition and shortage of talent. According to the McKinsey Global Institute: “By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” If you have the right skills, you will be highly sought after (at least in the current market conditions). Not just that, around the world, any business analyst attracts $100000 to $150000 per year with an average experience of 3 years.

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