In the past, the terms business analyst and data scientist have sometimes been used interchangeably, and indeed, in a small company, the lines between the two sorts of jobs may blur.

But as more and more companies look to big data for business insights, they are shifting from relying on business analysts to predict what the future of a business might look like, and moving towards using data scientists and machine learning to interpret data and predict trends.

What’s the difference, you might ask?  While the end result of these two jobs is often similar, a business analyst and a data scientist use different tools to get there. In general, data scientists have much greater technical expertise, especially in computer programming, systems engineering, and statistics.

Business analysts, by their very nature, rely on intuition and have human biases that are starting to be seen as flaws that put them at a disadvantage compared to the cold hard facts that data scientists can produce.  In addition, business analysts are often concerned with the single truth of what did happen in the past, while data scientists are working in a much more fluid version of what might happen in the future.  Wired magazine, among others, has predicted that data scientists will supplant business analysts in the coming years.

So what’s a good business analyst to do?  Transitioning to become a data scientist is a definite possibility.

Compared to other professions, business analysts do have some distinct advantages if they want to transition to become a data scientist. For instance, a business analyst often:

• has expert knowledge of their industry, which is vital to understanding and analysing data
• is already familiar with data analysis using spreadsheet and database tools
• and has strong communications skills and the ability to relay complex information to a layperson.

However, to transition successfully, a business analyst will need to follow a few steps to upgrade her skills and resume:

1. Brush up on your statistics. Data science requires a good deal of math, especially statistics, so if it’s been a while since your university level statistics course, make sure you get a refresher.
2. Get a crash course in machine learning. Understand what it is, how it works, and how people are using it. Machine learning is the engine that drives much of the data analysis happening today.
3. Learn to code. If you don’t already have a coding language under your belt, it’s time to learn. If you have some coding experience, take your skills to the next level with another relevant language. Data scientists who can build their own systems and algorithms are vital.
4. Get some real-world practice. Whether that means developing a side project at your current job or putting together a passion project in your spare time, most companies want a data scientist with experience.
5. Join the community. It’s OK to be the new kid at the table, but you definitely want to join the conversation. Join some websites and forums and follow some of the thought leaders in the industry to stay on top of new trends and ideas.

In my opinion, a business analyst is the perfect candidate to transform her skills into a data scientist.  Those who take a proactive approach to improving their existing skills and going after any they may be lacking won’t have to worry about layoffs or being replaced; their industry expertise will go a long way to making up for a learning curve acquiring new skills.

What do you think? Will data scientists replace business analysts? Or will both professions be needed in the coming years?  I’d be interested in your thoughts in the comments below. 

Read other Bernard Marr articles.

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