Read enough online, and it starts seeming like the hardest part of data-driven decision making isn’t regression analysis, unsupervised learning, or predictive analytics, but rather simply distinguishing between the many overlapping job titles and disciplines. From how some sites characterize the relationship between a business analyst and a data analyst, for example, you’d think there were clear-cut, centrally mandated distinctions.
But here’s the dirty secret: what one company considers data analytics vs business analytics (or business analytics vs data science, for that matter) is pretty arbitrary. Many data analyst job descriptions could just as easily bear the job title “business analyst,” and vice versa. So while these terminological squabbles might loom large as you are looking to land a job, once you’re actually working one, you’ll probably find they quickly fall away.
Testifying to the similarity between these roles are the roughly equivalent average salaries for each. In the US, the average salary for a data analyst is $82,207, while the average salary for a business analyst is $79,770. For reference, the average American earned $58,260 in 2021.
This all isn’t to say that you shouldn’t try to understand the basic contours of each discipline and which skill sets and end goals are emphasized in each, especially if you’re considering educational pathways and future careers. A short course, bootcamp, or master’s program in data analytics might very well put you in a different position than one in business analytics would. And a data analyst position might similarly end up playing to different strengths than a business analyst position. Understanding how educational providers and potential employers use these terms — even if it’s just how they use them — can help you make the best decision for you.
In this article, we’ll give you our understanding of the nature of the difference between business analytics and data analytics, before diving into specifics to show you how these differences translate into the responsibilities and qualifications of real-world data analysts and business analysts. At the end, we’ll suggest some ways to move forward if you’re interested in a career in data or business analytics.
What’s the difference between business analytics and data analytics?
We’ll start from the outside and move in: what’s the difference between business analytics and data analytics? In our view, this question isn’t so much about how the two differ in terms of the kinds of analytics, expertise, and skills required, but rather how the two emphasize elements of a fairly common set of kinds of analytics, expertise, and skills differently. We’ll introduce this set presently, and then discuss these different emphases.
Types of business and data analytics
There are four main types of analytics employed in both business and data analytics: descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics.
Descriptive analytics: what happened?
Descriptive analytics entails analyzing historical data to understand what has happened in the past. This type of analysis is often used in retail to track inventory and sales.
Diagnostic analytics: why did it happen?
Diagnostic analytics entails analyzing data to determine the reason behind a particular event or phenomenon. In marketing, this type of analysis can be used to understand why some campaigns are more successful than others.
Predictive analytics: what will happen in the future?
Predictive analytics entails analyzing data to make predictions about what might happen in the future. Manufacturing companies often use this type of analysis to predict future buying patterns and manage their supply chains accordingly.
Prescriptive analytics: what should we do next?
Prescriptive analytics entails analyzing data to determine the best course of action to take in the future. This type of analysis is becoming increasingly important in logistics, as companies strive to deliver goods as efficiently as possible.
Required expertise for business and data analysts
Though the degree to which they are important differ, both business analysts and data analysts benefit from having business expertise and industry expertise.
It’s important for analysts to have at least a basic understanding of business fundamentals in areas like operations, marketing, sales, finance, strategy, and/or human resources. This is crucial not just to be able to align analysis projects with business goals, but also to be able to effectively work on multifunctional teams and cultivate buy-in from various stakeholders.
It’s also often important for analysts to have or develop industry expertise. Knowing the ins and outs of a particular industry can help analysts develop more efficient approaches to the data they have available to them and more effective, meaningful insights from this data.
Skills for business and data analytics
To work in data or business analytics, you must possess a diverse skill set. Though exact requirements will vary depending on industry, seniority, and responsibilities, key skills generally include:
Business analysts and data analysts must be able to use Excel for basic data analysis and Structured Query Language (SQL) to store, manipulate, and retrieve data from databases. Increasingly, analysts are also proficient in R, a statistics-specific programming language, and/or Python, a general-use programming language.
Whether for descriptive analytics, diagnostic analytics, predictive analytics, or prescriptive analytics, there are a host of statistical analysis techniques that data analysts must master, including regression analysis, factor analysis, cohort analysis, cluster analysis, and time-series analysis.
Business and data analysts must be adept at data collection, data cleaning, and data mining.
Companies often expect analytics candidates to be able to create compelling data visualizations such as dashboards using tools such as Tableau.
In addition to technical skills, analysts should also possess strong teamwork, leadership, critical thinking, and communication skills.
Machine learning skills
For advanced analysis, some analysts will employ machine learning methods using libraries like Pandas or Keras.
What’s the upshot?
As we noted above, these types of analytics, expertise, and skills are common to business analysts and data analysts, with the real difference coming in how they are emphasized. This is particularly the case at larger companies, where responsibilities can be more specialized.
At a big corporation, you might see a business analyst focus more on developing insights that would directly contribute to business goals: increasing the company’s revenues, decreasing its costs, expanding its market, or entering a new one. Often, they will have to present their findings to managers and executives. In this case, business and industry expertise will become particularly important.
The work of a data analyst at a big corporation might instead be more siloed, focusing on analyzing and improving a narrow part of day-to-day operations, a specific product, or finding ways to measure employee sentiment, for example. In this case, business and industry expertise might not be as important as an ability to effectively analyze large amounts of data in novel ways.
At smaller companies, the difference between the work done by business analysts and data analysts can become more difficult to discern, in large part because smaller companies might only be able to hire generalists who can deal both with day-to-day operations and bigger-picture business problems. In this case, the analyst — whether a business analyst or a data analyst — would need to wear many hats, a great learning experience, no doubt, but a potentially difficult one.
In the next section, we will move on from the theory to examine what the subtle differences in the work of a data analyst and a business analyst look like in practice.