Business Analyst vs Data Scientist

To understand the answer to this question, let’s start by looking at what they have in common. A Data Scientist and a Business Analyst rely heavily on data to perform their research, analyzing it for meaningful patterns, often with the intention of applying their insights to some problem. But each approaches that goal in a different way, or with a different scope or level of expertise.

A Data Scientist is singularly focused on data and what it can tell us. Data science, however, is even more far-reaching and ambitious than data analysis, looking not only at what the data says but also what it implies. That is, Data Scientists use advanced statistical techniques to understand causality, and even make recommendations on future actions. Data science is also not limited to business alone; it applies in a wide range of fields, and doesn’t necessarily try to inform specific decisions – for example, by modeling the spread of a contagious disease, a Data Scientist might help epidemiologists predict its future growth, without necessarily making any recommendations on what to do about it.

But in nearly every case, data science is about digging into large sets of data. In this way, data science is in one sense more general than business analysis – because it applies to many other fields of research besides business – but in another sense, data science is more specialized, as it’s focused more squarely on what data mining can yield, and less on the kinds of business insights that can be derived from other methods, or what data-based insights mean when applied to the context of different conceptual models.

While business analysis includes a great deal of data analysis – and may in fact be said to be predicated on data analysis – it considers a broader context for that data: a Data Analyst is highly specialized in their ability to manipulate data, which is definitely a crucial skill for a Business Analyst, but a Business Analyst also looks at the way that data fits into an organization’s larger operations – including aspects that aren’t necessarily captured by large sets of data, such as organizational structure or workflow protocols. In effect, a Data Analyst is a pro at turning data into meaningful insights, while the Business Analyst sees how those insights can effectively be implemented in the real world.

Data Scientists have more education and a higher degree of specialization, and so they typically command a higher salary than Business Analysts. As in most fields, though, there’s quite a bit of variance in salaries, depending on your level of experience, and the city, company, and sector you’re working in.

In a sampling of three salary reporting sites (Glassdoor, Indeed, and Neuvoo), we found that Business Analysts working in large urban areas like Los Angeles, New York, or Toronto can expect an average salary of roughly $86,000, $87,000, and $71,000 respectively, while a Data Scientist working out of the same three locations can expect an average salary of about $132,000, $137,000, and $101,000, respectively.

In other words, when speaking about the two fields as a whole, data science has a salary premium of roughly 50 percent. But it’s important to note that, even within each of these designations and geographical areas, salaries are distributed along a wide bell curve that can span tens of thousands of dollars – so a more experienced Business Analyst could expect to earn more than a Junior Data Scientist.