Business analysts improve how a company works: they gather requirements, map processes, and translate what stakeholders need into specifications a team can build. Data analysts extract insight from the data itself: they query it with SQL, analyze it with statistics, and turn it into dashboards and findings.
That’s the textbook split, and it still holds. What’s changed since we last rewrote this page is the middle. A growing share of job postings, and a growing share of the people using our platform, sit in a hybrid seat: someone who owns the business question and can get a validated answer from the data without waiting on anyone. We’ll cover both classic roles, the hybrid one, the data scientist next door, sourced salaries, and what AI is doing to all of it.
Business analyst vs. data analyst at a glance
| Business analyst | Data analyst | Data scientist | |
|---|---|---|---|
| Focus | Processes, requirements, and the gap between what the business needs and what systems do | The data itself: extracting, cleaning, analyzing, and reporting it | Building predictive and statistical models, often from scratch |
| Typical deliverable | Requirements document, process map, user stories, business case | Dashboard, SQL analysis, report, A/B test readout | Trained model, experiment design, algorithm, research findings |
| Core tools | Excel, Jira, Visio, Confluence, SQL (increasingly) | SQL, Excel, Python or R, Tableau or Power BI | Python, R, scikit-learn, XGBoost, cloud ML platforms |
| Technical depth | Moderate: reads data, rarely builds pipelines | High on data; moderate on modeling | High on both statistics and engineering |
| Reports to | Product, operations, or IT leadership | Analytics, BI, or a business function (marketing, finance) | Data science or engineering leadership |
| Salary range, US (see sources below) | Glassdoor total pay: about $85K to $138K. BLS proxy (management analysts): median about $102K | Glassdoor total pay: about $72K to $122K. BLS proxies: $79K to $89K medians | Glassdoor total pay: about $124K to $201K. BLS median: about $120K |
| Entry path | Business, finance, or operations degree; often from a domain role (ops, product, consulting) | Analytics, statistics, economics, or CS degree; bootcamps; often from a reporting or BI role | Statistics, math, or CS degree, often graduate level; from research or engineering |
What does a business analyst do?
A business analyst is the person who figures out what the business actually needs before anyone builds anything. In a typical week, that means interviewing stakeholders, documenting a current process and its pain points, writing requirements or user stories, and sitting between the business side and the technical side so both understand each other.
The output is rarely a chart. It’s a requirements document, a process map in Visio or Lucidchart, a backlog in Jira, a business case with costs and benefits. The BA’s core skill is translation: taking “our onboarding is too slow” and turning it into a set of changes a product team can ship and a way to measure whether they worked.
BAs do use data, and the good ones use it constantly. They’ll pull a report to size a problem, check a dashboard to validate a claim, or work with an analyst to get a cut of the numbers. What they usually don’t do is build the query, the pipeline, or the model themselves. That’s changing at the edges, which we’ll get to.
The business analyst skills that matter most, in our experience working with hundreds of them: structured thinking, stakeholder management, clear writing, enough SQL to check your own assumptions, and a healthy skepticism about what people say they want versus what they’ll use.
What does a data analyst do?
A data analyst owns the data. They write the SQL, clean the extract, build the dashboard, run the cohort analysis, and answer the question “what actually happened” with numbers rather than opinions.
The deliverables are concrete: a Tableau or Power BI dashboard, a weekly report, an ad hoc analysis in a notebook, an A/B test readout with confidence intervals. Most of that work lives on the reporting side of the BI vs predictive analytics line, which is exactly the line that’s starting to move. Data analysts are usually the people closest to the warehouse, which means they’re also the people who know where the bodies are buried in the data model.
The technical bar is higher than the BA’s on data handling (SQL is non-negotiable, Python or R is common, statistics is expected) and lower than the data scientist’s on modeling. A data analyst can run a regression. They typically don’t build and deploy a production churn model from scratch. Again: typically. Keep reading.
For a fuller breakdown of the role, including seniority levels and day-to-day responsibilities, see our guide to data analyst roles and responsibilities.
What is a business data analyst? (the hybrid role)
A business data analyst is exactly what the name says: a business analyst’s ownership of the question, combined with a data analyst’s ability to answer it directly. The title shows up in job postings from marketing ops, revenue ops, finance, and customer success teams, and it’s the role we see most often among the people building predictions on our platform.
The role rose for a practical reason. Companies got tired of the handoff. A BA writes the question, waits two weeks for an analyst to pull the data, gets an answer to a slightly different question, and starts over. Collapsing the two into one seat removes the queue.
What it needs from the BA side: domain knowledge, stakeholder credibility, the instinct to ask “so what would we do differently if we knew this?” What it needs from the DA side: SQL, comfort with a warehouse, the ability to read a validation result and know whether to trust it. What it does not need, increasingly, is Python or a statistics degree, because the platforms now do the modeling work.
Glassdoor’s US total-pay range for the title “business data analyst” runs from about $89K to $140K as of 2026, which is above the plain data analyst range and roughly in line with business analyst pay. The market is paying for the combination.
And where does a data scientist fit?
A data scientist builds the models. Where a data analyst describes what happened and a business data analyst asks what to do about it, a data scientist designs the algorithm that predicts what happens next, runs the experiments that prove it works, and often engineers the pipeline that keeps it running. The technical floor is the highest of the three: Python or R at a software-engineering level, statistics at a graduate level, and familiarity with ML frameworks and cloud infrastructure.
The old distinction was simple: analysts look backward, scientists look forward. That line is blurring from both sides. Analysts now produce forward-looking predictions with automated platforms, and data scientists spend more time on infrastructure, experimentation, and problems that need custom modeling. If your company has data scientists, they’re valuable for the hard, novel problems. They’re no longer the only path to a churn model.
Pay reflects the technical bar. BLS puts the median data scientist wage around $120K (May 2025 OEWS), and Glassdoor’s 2026 US total-pay range is roughly $124K to $201K. BLS also projects data scientist employment to grow 34% from 2024 to 2034, among the fastest of any occupation.
Who earns more? Salaries with sources
Short answer: business analysts typically out-earn data analysts at the median, data scientists out-earn both, and the hybrid role sits at or above the BA line. The numbers, with the caveats that make them usable:
Bureau of Labor Statistics (May 2025 OEWS data, released May 2026). BLS doesn’t track “business analyst” or “data analyst” as occupations. The closest proxies:
- Management analysts (the standard BA proxy): median hourly $48.97, which annualizes to about $101,900. Mean annual wage $113,790. The May 2024 Occupational Outlook Handbook figure was $101,190.
- Operations research analysts (a common proxy for quantitative data analysts): median hourly $42.76, about $88,900 annualized. Mean annual $99,730.
- Market research analysts and marketing specialists (a proxy for marketing-side data analysts): median hourly $37.87, about $78,800 annualized.
- Data scientists: median hourly $57.80, about $120,200 annualized. Mean annual $126,800.
Glassdoor (US, total pay including bonus, mid-2026). Self-reported, skews toward people who report, but large samples:
- Business analyst: average $107,516; typical range $84,685 to $137,916 (about 130,000 submissions).
- Data analyst: average $93,500; typical range $72,244 to $122,168 (about 22,500 submissions).
- Business data analyst: average $110,932; typical range $88,799 to $139,907.
- Data scientist: average $156,745; typical range $123,803 to $201,067 (about 57,700 submissions).
Two things to keep in mind when you read these. BLS numbers come from employer payroll and exclude bonuses, so they run lower and cleaner. Glassdoor numbers include total compensation and self-selection, so they run higher and noisier. Anchor on the range, not the point. And location matters more than title: the same “data analyst” posting pays very differently in Columbus and San Francisco.
On growth: BLS projects management analyst employment up 9% and operations research analysts up 21% from 2024 to 2034, both well above the 3% average for all occupations.
How AI is changing both roles
Will AI replace business analysts? Or data analysts? Our honest read, as a company that builds AI tools and employs both:
AI is compressing the mechanical middle of both jobs. For data analysts, that’s the query writing, the dashboard scaffolding, the first pass at cleaning. Large language models are already good at generating SQL from a plain-English question and explaining a chart in a paragraph; we wrote about the practical boundaries in how LLMs and data analytics work together. For business analysts, it’s the first draft of the requirements document, the meeting summary, the process map. None of that was ever the valuable part of the job. It was the toll you paid to get to the valuable part.
What’s left, and what’s getting more valuable, is judgment. Knowing which question matters. Knowing the data well enough to spot when an automated answer is wrong. Knowing the business well enough to say “yes, that’s a real signal” or “no, that’s the Q4 promo.” AI makes the work faster; it doesn’t know what your CFO will ask next Tuesday.
The bigger shift is that prediction has moved from a data scientist’s specialty to an analyst’s tool. The example we know best is the one that happened to us.
Our own sales team had 2,300-plus qualified leads and a manual, partly subjective grading system that couldn’t tell a hot lead from a warm one. So they built a predictive lead scoring model on our own platform, with data from Salesforce, HubSpot, and PostHog site events. The team that did it was our analytics and revenue operations function, not a data science group. It took 12 days from zero to production. The model scores every new lead within a day of creation on its probability of reaching a second call, and writes an A/B/C grade to the Salesforce record.
The results: top-graded leads reached a second call 32.3% of the time versus 15.1% under the old grading, a 3x lift, and Closed Won rates went from 2.9% to 8.3% for the same tier. Among the highest-scoring leads, precision was 44.8% against a 10.5% baseline. Dror Katz, our VP of Data and Analytics, summed it up: we built the platform so customers wouldn’t have to rely on guesswork, and applying it to our own pipeline was the obvious move. Read the full case study.
That’s an analyst-run predictive model with a measured business outcome, built in less time than most teams spend debating whether to start. Across our customers, the person who owns the predictive model is far more often an analyst, analytics manager, or ops lead than a data scientist. Prediction is becoming an analyst skill.
AI is removing the ceiling that used to sit between analysts and the forward-looking questions, and the roles are growing into the space that opens up. The people who take advantage of that are becoming what our guide to the AI data analyst describes: analysts who own predictions, not just reports.
How to choose (or switch)
If you’re deciding between the two, here’s the question that settles it faster than any skills quiz: when a stakeholder brings you a problem, do you want to fix the process or find the answer in the data?
Choose a business analyst if you like people, process, and translation; if you’d rather run the discovery workshop than write the query; if you’re coming from operations, product, or consulting and want to formalize what you already do.
Choose a data analyst if you like the data more than the meetings; if you want to be the person who can prove it with numbers; if SQL feels like a tool rather than a chore; if you’re coming from a reporting, finance, or research role.
Aim for the hybrid if you want the best pay-to-technical-depth ratio. Learn SQL well. Learn one warehouse. Learn to frame a predictive question (entity, outcome, time window) and to read a validation result. Then pick up a platform that turns that question into a deployed model. You’ll be the person in the room who can both ask “which customers will leave next quarter” and produce the list.
Switching is common and usually runs BA to DA or DA to hybrid. BA to DA means adding SQL and statistics, plus a lot of practice on real company data. DA to hybrid means adding stakeholder ownership and a prediction skill set, which is mostly a matter of volunteering for the forward-looking question nobody else wants to own.
If you want to see what an analyst-run prediction looks like on your own data, book a demo. We’ll take one business question you already have and show you the deployed model by the end of the call.
Which is better, business analyst or data analyst?
Who earns more, a data analyst or a business analyst?
Can a business analyst become a data analyst?
Will AI replace business analysts?
Omer is a Data Scientist at Pecan AI with an M.Sc. in Industrial Engineering, bringing a strong analytical foundation built across multiple industries. He combines structured problem-solving with cross-disciplinary collaboration to drive meaningful impact through data.
