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Business Analyst vs Data Analyst: What’s the Difference?

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.

Business 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.

Industry expertise

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.

Analysis techniques

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.

Analytics process

Business and data analysts must be adept at data collection, data cleaning, and data mining.

Data visualization

Companies often expect analytics candidates to be able to create compelling data visualizations such as dashboards using tools such as Tableau.

Soft skills

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.

How does the day-to-day of a real business analyst differ from a data analyst?

As we’ve said above, many companies will use data analyst and business analyst job titles interchangeably, but if you look at enough job postings, you can start to gain an understanding of how life as a business analyst at a larger company might differ from the life of a data analyst. Let’s look first at this job posting from the New York Times for a data analyst:

Data Analytics vs Data Science

There is often confusion between data analytics and data science due to the considerable overlap between the two fields. While it can be difficult to fully capture the nuances of the relationship between data analytics and data science, as job titles and data operations may vary greatly between companies and industries, we can generally differentiate the two as follows: 

Data analytics involves using statistical analysis, tools, and techniques to extract insights from data. Data science, on the other hand, involves using scientific methods, algorithms, and systems to develop new tools and techniques and new ways to gather data for analysis. Data science often involves a combination of data analytics, machine learning, and other related techniques to analyze and understand complex data sets, while data analysts often employ methods and tools developed by data scientists.

Analyst, Data & Insights — New York Times

New York, NY

As part of the Data and Insights Group (DIG), you will join a large community of talented analysts who partner with Product, Engineering, Design, and PMO teams across the business. In this role you’ll work with our Home Team to support the real-time decisions that editors need to make regarding NYT’s homepage. We are looking for an Analyst with strong database skills and the ability to translate data into insights for stakeholders across product teams and the newsroom. The ideal candidate will be curious and a collaborative team player. They are someone who understands the value of contribution to a greater whole and the importance of analytic delivery.


  • Writes SQL to pipeline and analyze big data

  • Develops dashboards to expand access to data and analytics

  • Runs A/B Tests and provide scope and analysis

  • Develops best practices for experiment design, including advising on hypothesis generation, sample size, and counter metrics

  • Collaborates with internal stakeholders to understand the business and develop data-driven insights that are both strategic and operational

  • Democratizes data and insights through skillful data structuring and use of visualization tools

Minimum Qualifications

  • 1+ years of experience working with data teams to deliver reporting and analysis or a quantitative degree

  • Proficiency in SQL and experience working with relational databases

  • Collaborative mindset, strong curiosity, and an excitement for learning new skills

  • Excellent analytical reasoning and problem-solving skills

  • Enthusiasm for working with team members from different backgrounds and contributing to inclusive culture

Desired Qualifications

  • Familiarity with Google BigQuery, AWS, or other big data environments

  • Comfort with version control (Github, code review)

  • Experience with data visualization tools such as Mode, Tableau, or Looker

  • Experience applying statistics to strategic problems

  • Experience with A/B testing

  • The ability to communicate cross-functionally - written, visually and verbally - in order to present key insights to team members, partners and stakeholders

The annual base pay range for this role is between $80,000.00 and $95,000.00

As you can see, a data analyst in this position wouldn’t be working on bigger business problems — maximizing profits, reducing costs, etc. — so much as optimizing a particular product, the New York Time’s homepage. Still, the NYT wants candidates to be able to understand the business and use this understanding to inform their data-driven insights, so they will need some business acumen. At the same time, the analyst will be collaborating with internal stakeholders who will have this kind of expertise — so while they will need to be able to communicate about business issues in meetings with some savviness, this won’t be the analyst’s focus. Instead, they spend most of their time employing their skills in SQL to build tools and perform analysis that will support product improvement.

Let’s turn now to a posting for a business analyst at American Family Insurance.

Business Analyst — American Family Insurance

Boston, MA

The business analyst provides analytics services to the business, develops new insights, and understands the business performance based on data and statistical methods. They also analyze business results, external market dynamics and other data sources to assess trends and develop actionable insights and recommendations to management via an understanding of the business model and the information available for analysis. They typically use data, statistical and quantitative analysis, explanatory and predictive modeling, and fact-based management to drive decision-making.


  • Works with business partners in the development and delivery of key performance analysis and reporting.

  • Builds and maintains various models used to project business results; enhances the model where there is business value in adjusting the models.

  • Ensures analysis and reporting accuracy and integrity and explains performance drivers and provides insights into performance trends.

  • Creates, executes and maintains forecasting models; speaks to the underlying assumptions and inter-workings of the model used to create the forecast.

  • Communicates findings to various stakeholders and leadership with recommendations for actions to address business changes, trends, and issues.

  • Evaluates, designs, tests and maintains data/analytics systems and makes recommendations for new tools and system enhancements.

  • Develops, tests, and deploys complex automated reporting solutions and automated decision analytics to replace manual business processes.

  • Develop and defines standard analytics and reporting governance and communication and ensures adherence to standards.

  • Provides business analysis, tool, and technique expertise.


  • Demonstrated experience providing customer-driven solutions, support or service

  • Solid knowledge and understanding of forecasting techniques or statistical analysis or data modeling or data mining

  • Demonstrated experience utilizing software tools to query and report data

  • Demonstrated experience with a variety of standard reporting software packages and best practices for report deployment processes

  • Demonstrated experience communicating/presenting complex and independent concepts and unbiased fact-based decision-making and financial performance

  • Demonstrated experience developing complex data sets for wide-spread use

The annual base pay range for this role is between $78,400 and $125,600.

As you can see, this role still requires considerable experience in statistical analysis and techniques such as data mining and modeling as well as skill in using software for this analysis, but it also requires far more business and industry expertise than the data analyst role at the New York Times. American Family Insurance wants candidates to have a deep understanding of their business model and external market dynamics and be able to speak to the company’s financial performance. A successful candidate would likely spend considerable time programming new tools, but also considerable time speaking in technical terms about what those tools are showing about the health of the business and what improvements could be made to the business itself.

Perhaps one of these positions intrigues you: what should you do next? In the next section, we’ll give you some ideas for how to start breaking into business and data analytics.

What Types of Jobs are There in Data Analytics?

We’ve explained the difference between business analytics and data analytics and given you some real-world examples so that you can see the difference for yourself — with the caveat that, in the end, there’s a lot of overlap between the work of business analysts and data analysts. We’ve also examined this overlap: the common types of analytics, expertise, and skills needed to work in these fields.

If having read all of this, you’re interested in a career as a business analyst or data analyst — or both — and want to learn more, we’d recommend checking out the guides below, which will provide further information on what you can expect from a career in analytics and the educational opportunities that can put you on this career path.

Data analytics

Business analytics