Can you train your business analysts to become data scientists?

We're all sitting on mountains of data, and having a few data scientists in the company is great - but there's not enough talent in the market. Can you train your own staff?
23 July 2018 | 664 Shares

Data science is becoming critical to every business. Source: Shutterstock

Come to think of it, your team of business analysts (BA) is well suited to analyze and dissect data for you.

In fact, if you can provide the right environment and the right incentives, they can quickly transition to become the data scientists (DS) your company will need in the near future.

According to IBM, by 2020, the number of jobs for all data professionals in the United States will increase by 364,000 openings to 2,720,000. Creating a great demand for such talent, and a significant shortage in the market.

Hence, if you prepare ahead and train your staff, the investment will boost employee morale and also ensure you have the talent you need for future business needs.

What makes a BA a good DS?

Well, typically, a business analyst is well suited to be a data scientist simply because they’re well versed with the business, possess certain analytical capabilities, and are equipped with an understanding of data.

In fact, business analysts often have expert knowledge of their industry, which is vital to understanding and analyzing data.

Say you run a restaurant franchise, your team of business analysts usually know the ins and outs of your industry and company. Hiring an outsider will mean additional training which can be avoided if you groom your own data scientists from within the existing team.

Further, business analysts in today’s day and age are already familiar with a number of database management systems and data analysis tools.

Whether its Microsoft Access, Microsoft Excel, or Tableau, it is likely your business analysts will have the necessary skills in most instances, or be better equipped to learn new ones which aren’t very different from the existing tools you already use.

Finally, and most importantly, business analysts have strong communications skills and possess the ability to relay complex information to management.

That is the very skill that’s hardest to teach most data scientists who’re more quantitative and less verbal or visual. However, previous experience as a business analyst can help future data scientists better explain their findings in words or visuals using graphs and charts.

Does everyone want to become a data scientist? Source: Shutterstock

Training your BAs for the future

Once you’ve decided to invest in training your business analysts, you need to ask how many of them are inclined to undergo the training necessary to become data scientists.

Not everyone will be willing, and you need to make sure that they understand that it’s okay to want to continue to be business analysts. Your company will continue to have a need for them, and hence, a place for them will always remain.

Finding the few members of the team who’re happy to train to be data scientists is the first and biggest step since the actual training will take a toll on them and require a rather long-term commitment.

Here are some of the steps that trainers recommend that business analysts go through in order to make the transition to data scientists:

#1 | Statistics

One of the first things data scientists need to do is understand data and imagine how it’ll respond to different types of statistical functions.

As a result, getting a grip on statistics is a great first step to becoming a data scientists for business analysts.

#2 | Machine learning

In today’s world, with the mountains of data at our disposal, it’s difficult to crunch the numbers and make sense of unstructured data without using algorithms.

As a result, machine learning and artificial intelligence have come to play a significant role in data science – and new entrants in the field must familiarize themselves with what the technology can do for them.

#3 | Programming

In order to become a data scientist who can add value to existing projects, programming is paramount.

In fact, most data science teams welcome practitioners who can bring a new programming language to the table as each language has capabilities of its own.

Some of the top programming languages data scientists use include R, SAS, Python, SQL, Java, and SPSS among many others.

#4 | Pilot projects

As the old adage goes, practice makes perfect. If you’re training your staff to become data scientists, ensure you have a few exciting projects to help test the new skills the team will acquire.

Doing so will not only help you get the ball rolling on your organization’s data science projects but also ensure the team has time to find out any gaps in their knowledge and fill them while there’s still time.

#5 | Get involved

The last item on the list is really a habit. New entrants to the field of data science can benefit immensely from joining online forums that discuss data problems and debate on different ways to overcome challenges.

Doing so will not only strengthen the individual’s confidence but also help mould them into professionals constantly thinking about exciting data problems and feeling like they’re part of a community.