Career Paths in Business Analytics – Plan your Next Best Role in the Data Science World

Introduction

“Data Scientist: The Sexiest Job of the 21st Century” is one of the most popular Harvard Business Review (HBR) articles and has inspired tons of people to pursue their careers in the field of analytics. One of the main themes of this article published in HBR was the trend of growing jobs in the analytics industry.

The exact same inference was predicted by IBM recently saying that the number of US data professionals will increase from 364,000 to 2.72 million by 2020!

And that has come to pass this year.

Unanimously, across the industry, we are seeing a surge in Business Analytics job openings, but do all these jobs need the exact same skill set? I have received a number of queries focused on what are the possible career trajectories in the analytics industry. These queries usually come from people seeking a break in the analytics domain or people already working in the industry and are looking for a deeper role.

In this article, we will look at the major roles available in the analytics industry. I will also propose a framework to think about your career in the space of business analytics.

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Table of Contents

1. About the Analytics Market
2. What does a business analytics professional do?
3. Reporting roles
4. Intermediate analytics roles
5. Strategy roles
6. Data Scientist Roles

About the Analytics Market

Let me start with a few lines/data points that were published in a McKinsey report on Big Data (May 2011) :

The United States alone faces a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.

Pay attention to the words “with the skills to understand and make decisions based on the analysis of big data”. The industry will require a lot of big data and machine learning experts and needs even more (about 10x) people who can make decisions based on the analysis, even though they might not be experts on Big Data or Machine Learning.

These roles will primarily be strategy roles and product management roles that can define new challenges for their analytics specialists to solve. We will contrast these strategic roles with data scientist roles later in this article. First, let’s try to understand how diverse this industry really is.

If you plot a word cloud of all the articles related to analytics (sample shown in the image below), you will see all kinds of words popping up, including statistics, computer programming, strategy, planning, reporting, etc. The field of business analytics is extremely diverse and people with both analytical skills and business acumen are sought after in all industries in numerous and diverse roles. Thinking about your career with so many possible options can be overwhelming, and you might feel like you’re losing a sense of whether you are making progress in your career or not.

What does a business analytics professional do?

The word “business analytics” perfectly summarizes every type of job we categorize under business analytics. “Business” emphasizes the importance of business understanding, and “Analytics” refers to the importance of statistics, computer engineering, and operation research in this type of role.

An analytics professional can ultimately work in a very strategy-oriented role or can work as a very specialized deep learning scientist. The former role has a stronger component of business, while the latter role has a much stronger component of analytics. Obviously, your role generally has a trade-off between these two components and you can switch between roles that have different proportions of the two components. The value which you create for yourself is a positively correlated function of business understanding and analytics. Mathematically speaking:

Value = function (Business understanding , Analytics)

With this understanding, I have plotted various roles in our industry in a cross-tab graph below:

Obviously, the above graph is my personal understanding of the industry and the position of each role in this graph can certainly be debated. The main idea which I want you to focus on is the diversity of roles you can take in the business analytics industry and the variation in the path you can take from your current role. Let us first try to understand each of the 5 highlighted boxes above regarding the category of roles.

Reporting Roles

Between 2000 and 2012, this was the major category of roles for business analytics professionals. The role was mainly concerned with “What (event) happened” rather than “Why did it (the event) happen”. However, most of these roles have evolved in recent times after companies automated a lot of these processes, and machine learning became popular. However, there are still a lot of roles that will have more than 50% work on reporting and the rest of the role in answering the question – “Why did the event happen?”.

This is a good role for starting your career in the analytics industry. But in the long run, you should take initiative and move into a role focused on either “What’s happening now?” i.e., business Intelligence/dashboard, or focused on “What’s going to happen next?” i.e., predictive analytics.

Intermediate Analytics Roles

This is the type of role I started my career with. The majority of Economics/Statistics/Computer science graduates will begin their journey with these roles. This is an optimum combination of business and analytics. It’s a great way to understand the best of both worlds.

The roles in the intermediate analytics field are also quite diverse. One extreme role in this category will be focused on Business Intelligence trying to solve “What is happening now?”. The other extreme in this category will be highly business-focused roles like Product Pricing, where you are required to create a lot of business scenarios and finding the optimum price for the products your company is selling.

The majority of roles strike a more optimal balance between knowing the business and working with cutting edge tools like Deep learning in Decision Management/Risk analytics/ Fraud analytics. Most of these roles are involved in automated decision making. For instance, you might be tasked with creating an algorithm that can accept or reject credit card applications based on the customer risk profile, or that can select customers that have a high propensity to opt-in for a cross-sell offer of an insurance product. All these business problems require you to create predictive models on bulk customer profiles and rank them based on some business metrics.

If you are in this group, almost all your options will be open. You can now choose to move to a more strategic role or you can choose to become a data scientist. In case you don’t know where to go next, a good way to find your fit is to take a role on the border of the two boxes. For instance, if you want to take a strategic role in the future, you can test your fit by taking a role in a P&L based intermediate analytics role like Product Pricing. There are a few more roles like portfolio analytics that you can choose in order to get a hang of strategic roles. Note that you might have to live without data science techniques like deep learning if you choose to move ahead on the path of strategy roles.

On the other hand, if you want to test your fit as a data scientist, you can take up business embedded data scientist roles rather than pure data scientist roles. This way you don’t need to lose your grip on the business before you move onto the path of research-oriented roles.

Apart from the above two paths, you have one more way to find a good trade-off between business and analytics – Tech Product Manager roles. But such roles are not easily available in the industry. Data science is primarily used by companies to find the competitive advantage over other firms by building data-backed strategies.

Tech firms like Google and Facebook use analytics not only to build strategy but also to create products. For instance, Google Instant search is a tech product that uses machine learning to give search results. These tech firms look for people with a skill set in both business P&L and machine learning to design such products. If you choose to move ahead on this path, you should not only apply to the big tech giants but should also look for product manager roles in niche skill companies like NICE, Aspect, or Interactions.

Strategy Roles

You might have heard about an important economy principle – “no economic profits in a competitive market”:

The existence of economic profits attracts entry, economic losses lead to exit, and in long-run equilibrium, firms in a perfectly competitive industry will earn zero economic profit.

If all the businesses are in a perfect competitive market, how do they make any money? If you are an economics student, you will know the answer well. All the successful businesses are built on inefficiencies in the market, hence there’s no “perfect competition”. The role of a strategist is to identify these imperfections and nurture them to run a successful business. For big firms, we have strategists on both the corporate level as well as the business level.

Corporate strategy is when you work on a corporate level answering questions like “What is the right business portfolio for your company?”, “To reach this portfolio, what new businesses do you need to acquire/invest/grow/shut down?”,  “What is the right organization structure for your business that will foster synergies in operations and other domains?”. For instance, if you work for Wells Fargo’s corporate strategy, you will build a strategy to acquire or close businesses like investments/retail banking/credit cards; you will also work on creating global operations to eliminate operational costs of individual businesses, etc.

Business strategy is linked more to a particular line of business. While the corporate strategy might be more focused on the expense side of things at the corporate level, business strategy is a lot more focused on maximizing the net revenue. For instance, Wells Fargo Credit Card strategists might be focused on maximizing the revenue coming from their card customers. A lot of operations might be a shared asset across all lines of businesses of Well Fargo, like call centers, chat centers, branch premises, etc. Hence, these expense heads are better optimized on the corporate level rather than the business level. The distribution of responsibilities might differ across companies, but mostly both business and corporate strategists work hand in hand.

Both these roles will require you to estimate benefits from changes in a product’s features, process change, and technology investments by creating various business scenarios and computing Net Present Value of different investments. Analytics professionals are well suited for such roles because of their grasp of numbers and deep understanding of the latest technology that will be used to create a competitive advantage. Analytics professionals who started their careers before 2010 currently make up a big proportion of the population in strategy roles.

Data Scientist Roles

Coming to the most fascinating role for most people looking to get into data science. The data scientist role is a position for specialists. You can specialize in different types of skills like speech analytics, text analytics (NLP), image processing, video processing, medicine simulations, material simulation, etc. Each of these specialist roles are very limited in number and hence the value of such a specialist is immense. This is why we are seeing such a high demand for data scientists these days.

For you to excel in these roles, you need to keep yourself updated with the latest tools and technologies. You should also invest on training yourself in relevant languages and have the skill to explain your complex models in simple terms to clients and businesses. You can always move back to the strategy side in case you feel the need to grasp business concepts.

End Notes

The career paths mentioned in this article are based on my personal experience and a number of discussions I had with successful professionals in various analytics fields. With all the resources available online for FREE, you can easily migrate to any desired role with the right strategy. I hope this article helped you in defining your career trajectory.

If you have any ideas or suggestions regarding the topic, do let me know in the comments below!

Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.