business analyst vs data analyst

Business Analyst vs Data Analyst: what’s the difference?

In a word, the difference is scope. Business analysts (BAs) collect requirements to improve individual work flows or launch single products, whereas data analysts extract insights from data to understand process efficiency, market potential, customer behavior, financial performance, and more for an entire department.

While BAs may use descriptive and diagnostic data analysis to validate requirements, data analysts deep dive using descriptive, diagnostic, predictive, and prescriptive techniques on every aspect of the business. In short, a business analyst’s scope is smaller than a data analyst’s.

This article will drill down on the differences and similarities so that you can understand them and make a decision about which role would be best for you.

Fundamentally different roles: business analysts and data analysts

Comparing business analysts and data analysts is like comparing hybrid leaf blower to a generator. A leaf blower uses a motor just like a generator, but it is smaller and used for a specific purpose. A generator has a much larger, more complex motor that serves many purposes. Yet they both are parts of a secure home.

In the same way, business analysts use some data analysis skills, but for a specific purpose. Their goal is to collect requirements to improve processes or launch new products. Like the hybrid leaf blower does not always use its gas motor (moving to electric), business analysts do not always use data analysis techniques.

Analogies aside, business analyst and data analyst are fundamentally different roles. They serve two different purposes in the company.

Business analysts typically within teams to analyze processes or to develop new products for market. They do so with a set of specific steps to realize the goal. Those steps include:

  • Requirement elicitation and analysis from all stakeholders
  • Requirements interpretation
  • Balancing internal and external priorities for project
  • Problem solving given the parameters provided by stakeholders and the market
  • Proposing alternatives to decision maker ideas
  • Working with data analysts to understand the market
  • Communicating decisions and innovating

Whereas data analysts often work on projection, clustering, classification, and other regressions for an entire department. They use a large set of data analyst skills to extract insights for many purposes. For example, a data analyst working in the finance department might:

  • Collect transactional data requirements such as user ID, currency, time of transaction, and other data fields in an e-commerce company
  • Collect accounting data in a data warehouse to allow for quick extraction and manipulation by financial planning & analysis teams
  • Perform clustering analysis on marketing campaigns on known customers in a Luxury database to understand and exploit financial potential of each
  • Clean comparable transaction data received from third party provider to understand asset valuation range
  • Perform regression analysis on revenue data to project future income

In other words, the two roles are fundamentally different. Here’s a table to better explain:

ItemBusiness AnalystData Analyst
PurposeCollect requirements and ensure their implementation in new processes or new productsUse data to extract insights on a number of different topics
ScopeIndividual project or productDepartment-wide
SkillsRequirement elicitation & analysis, persuasionRegression analysis, time series analysis, clustering, classification techniques, and other deep data skills
Business Analyst vs Data Analyst Differences Table

Why do business analysts get confused with data analysts?

If you ask this question to any business or data analyst, they will probably laugh. The reason there is confusion between the two roles is, from the outside, they appear very similar.

If you work with either role, you know what I mean. Both of them ask questions about requirements, both of them seem to work independently, both of them have an influence on operations without actually building anything, both of them use data to arrive at their conclusions, and both of them are good communicators.

It’s easy to see why people confuse them. Here’s a table of their similarities:

ItemBusiness AnalystData Analyst
Use of RequirementsYes, for process change or product launchesYes, for data collection
Work IndependentlyYes, to maintain integrity of analysisYes, because only one person needed for a given analysis
InfluenceYes, because they communicate management decisionsYes, because they provide insight no one else can
Communication SkillYes, to understand, refine, and transmit requirementsYes, to communicate complicated finding simply
Use of DataYes, for market testing purposesYes, in every type of analysis
Business Analyst vs Data Analyst Similarities Table

It’s clear that the two roles are different, but this table shows that they have many apparent similarities.

I think the most important of them, though, is their use of data. Obviously data analysts use data all the time, but what about business analyst? Let’s examine this more closely.

How does a business analyst use data?

In short, a business analyst uses data to diagnose and describe the efficiency of company functions and to understand markets for new products.

While we usually don’t talk about it, there are two main types of business analysts, or BAs, (and a third type you can learn about here): process analyst BA and product development BA. Each one uses data differently.

A process analyst BA uses data to examine the efficiency of internal processes, while a product development BA uses data to determine if there is a market for a new product idea, often by analyzing focus group data.

The best part? There are only a few kinds of well-known descriptive and diagnostic analysis they use. Because of this, we can pinpoint almost exactly how different business analysts are from data analysts in their use of data!

The types of data analysis BAs use can be found in this table:

Type of Data AnalysisProcess Analysis BAProduct Development BA
Diagnostic Analysis (establish correlations)1. Regression AnalysisN/A
Descriptive Analysis1. Mean, Median, Mode
2. Standard Deviation
1. Qualitative Analysis
2. Idea Pattern Analysis (typically from focus group data)
Table on Types of Diagnostic and Descriptive Analyses used by Process Analysis and Product Development BAs

The exact methodologies behind each of these techniques is outside the scope of this article. The goal here is to show the difference between business analysts and data analysts.

(Note aside: to learn more about regression analysis, you can check out this article. And for qualitative analysis and idea pattern analysis, this article.)

Nevertheless, we need to understand how BAs use these techniques differently than data analysts. If you want to discern which role would be best for you, this is critical to give you a clear understanding.

How does a data analyst use data?

Data analysts use data in diagnostic, descriptive, predictive, and prescriptive ways. Here’s table to help you understand the scope:

Type of Data AnalysisData Analyst
Diagnostic1. Regression Analysis
2. Geographic drill-down
3. Transactional drill-down
4. Classification (Naïve Bayes)
5. Outlier detection
Descriptive1. Mean, median, mode (central tendency)
2. Standard deviation (dispersion)
3. Percent of total (distribution)
Predictive1. Moving Average
2. Simple Exponential Smoothing (time series)
3. Seasonal Smoothing (time series)
4. ARMA (time series)
5. ARIMA (time series)
Prescriptive1. Clustering (optimization)
2. Optimizations (optimization)
Shortlist of Data Analysis Techniques used by Data Analysts

As you can see from the shortlist, a data analyst uses many more data techniques than a business analyst. This is why I used the analogy of a hybrid leaf blower to a generator. The business analyst uses only a small portion of data analyst toolkit, and for a very specific purpose.

Data analysts, on the other hand, exploit all the techniques listed above to extract insight for any departmental needs.

Summary Table: Difference Between Business Analyst and Data Analyst

We’ve covered a lot of information in this article, and I know it can feel overwhelming to read all of these technical terms.

Don’t worry, you don’t need to understand everything from the start. The important thing here is to understand the difference between each of these roles, and not to mix them up (especially if you’re deciding on which to become, which we’ll cover below).

Take a minute to review this summary table of the differences between business analysts and data analysts. I think you’ll understand each point better now:

ItemBusiness AnalystData Analyst
PurposeEnsure requirements are implemented in process changes and product launchesExtract insights from data for projects across the department
ScopeIndividual projectsDepartment wide
SkillsRequirement elicitation and some data analysisData requirement collection and complete data analysis
Working styleIndependent work and to remain unbiased and unilaterally influence project resultsIndependent work to ensure analytical rigor and provide impartial opinion
Use of DataLimited to regression, standard deviations, descriptive stats, and qualitative analysisEncompasses whole range of analytic techniques to diagnose, describe, predict, and prescribe phenomenon on all project of a department
Table Summary of Differences between Business Analysts and Data Analysts

Can a data analyst become a business analyst?

Since business analysts use only a small portion of data analysis skills, it seems like a data analyst could easily become a business analyst, right? It’s not that simple.

In short, yes, a data analyst can become a business analyst if s/he is willing to learn additional business analyst skills. The most important of these skills would be asking good questions and knowing when to insist.

We’ve mentioned requirement elicitation and analysis multiple times in this article, but what does it mean? To put it simply, it’s another way of saying “talking to decision makers, asking them hard questions, and pushing back when you think they’re wrong until you end up with an actionable result.”

This is a very difficult skill to master since it requires constant risk taking. You have to essentially defy managerial direction in order to truly understand what they, and the end user of the project, want to and will use.


For example, for a long time car buyers said they would not want automatic gears because they would be less in control of the car. Big car company managers heard this and refused to change.

But those same customers said they wanted more places to hold items because it was too hard to reach for something while managing the gears.

Managers decided one day to put out an automatic car, since it would solve customers real problem, not the one they thought they had. And automatic cars took off.

Learning to ask good questions as a data analyst

Business analysts have to learn to ask the right questions and interpret their responses in order to send off the right requirements to their production teams. Data analysts are familiar with needing to gather the right data, but for an entirely different purpose.

Typically, management or other decision makers give them a question to answer. They then collect more than the needed information. If for any reason they cannot collect, they explain it and find a work around.

Business analyst don’t have that luxury. Requirements are both the input and the output in their workflow, so they need to nail it on the first go.

So yes, data analysts can become business analysts, but they need to learn the important soft skill of asking good questions.

Other skills to learn: documentation

Though they’re easier to pick up, there are some documentation skills are also critical for data analyst who want to become business analysts:

  • Clarity, concision, and precision
  • Speak the language (vocabulary)
  • Interpretation & Reading Between the Lines
  • JIRA and confluence knowledge
  • Use of pictures

You can learn more about documentation skills in this article.

Should I be a business analyst or a data analyst?

In a nutshell, you should be a data analyst if you prefer complex problem solving and do not like confrontation, whereas you should be a business analyst if you like working through challenges with others and don’t want to spend most of your time in analysis.

With that said, tasks are not the only criteria for choosing a job. You should also consider stress, career mobility, salary, and others. Below is a table comparing business and data analyst jobs on multiple criteria.

The numbers I’m using here are based on my own experiences and from a small survey of people working in these jobs in companies I’ve been a part of.

CriterionBusiness AnalystData Analyst
People orientationAppx. 75% people orientedAppx. 25% people oriented
Analytical orientationAppx. 25% analytical oreintedAppx. 75% analytical oriented
Stress levelsHighMedium
Career mobilityMediumMedium – High
Working hoursHighMedium
Barriers to entryMedium(Very) High
SalarySee next section!See next section!
Comparative table on Business Analyst and Data Analyst Job Criteria

The choice of which job is best depends on your personal inclination towards these criteria. Personally, I like analytics just as much as I like people, I’m resilient to work stress, and I don’t mind working long hours.

However, I’m ambitious and want to move up the ladder, and I like learning so the barriers to entry are less of an obstacle. And of course, I prefer a higher salary like the next guy.

This generally means I would lean towards a data analyst role. But it’s hard to say without a view on salary, so let’s take a look at average salaries in different locations next.

Business Analyst Salary vs Data Analyst Salary

In the United States, the average annual business analyst salary is $80,036, whereas the average annual data analyst salary is $75,102. But these numbers may not be representative. Let’s look at some areas in which these two roles are very common: big cities.

In New York City, a business analyst makes $86,307, while a a data analyst makes $74,143. On the other coast in San Francisco, California, a business analyst makes $73,952, while a data analyst makes $100,836 — a big difference.

In Seattle, WA, a business analyst makes $84,705, while a data analyst makes $78,331.

Let’s turn to the south. In Tampa, Florida, a business analyst makes $84,408, and a data analyst makes the same! In Austin, Texas, business analysts make $72,520, while data analysts make $68,690.

In the midwest, it’s a similar story. In Chicago, a business analyst makes $87,325, while a data analyst makes $75,130. Here’s a visualization:

Visualization of Business and Data Analyst Salaries (all data in text and visualization taken from


While business analysts use a portion of the data analyst toolkit, they remain a fundamentally different role. Business analysts are more people-oriented than data analysts and are focused on executing singular projects, whether its launching a new product or improving an inefficient process internally. They make more money in most big cities in the United States, and they just about the same career mobility as data analyst.

On the flips side, data analyst dig deeper in their analysis and touch a larger variety of projects across their assigned department. Due to the analytic nature of the role, data analysts have lower stress levels. Both business analysts and data analysts must be excellent communicators, just for different reasons.

Data analysts who wish to become business analysts often need to learn how to ask good questions in order to understand the narrow requirements needed on individual projects. Otherwise, the only other skill to learn is business analyst-style documentation.

At the end of the day, comparing business analyst and data analysts is indeed like comparing a hybrid leaf blower to a generator. The one resembles the other, but has a more acute purpose. Like the leaf blow and the generator are essential to a well-established home, so too are business analysts essential to the healthy functioning of a company.