Turn a question into evidence someone can use · Career guide
Data Analyst
The analytical work is part data preparation, part interpretation, and part explaining what a result can and cannot support.
Explore the ordinary work, then decide what to test for yourself.Everyday work
The ordinary assignment.
Imagine a team asking why fewer visitors completed registration. An analyst first defines the question: which period, which step, and what counts as completion? For the business intelligence branch, O*NET describes querying data, generating reports, maintaining dashboards, and identifying trends. A small table might reveal where the change occurred. The harder work is checking that definitions and records make the comparison meaningful.
Next, the analyst may revise a dashboard, explain a result to managers, or ask which decision they need to make. O*NET includes documenting report specifications, supporting existing tools, testing an output against requirements, and synthesizing evidence for recommendations. The work moves between questions, data, checks, and communication. A chart can show a pattern; it cannot by itself establish cause or choose an action.
Trade-offs
What the work asks of you.
Making messy information usable can be satisfying; it rarely produces a clean answer every time. A missing field, shifted definition, or small sample can change a conclusion. O*NET emphasizes accuracy and attention to detail for business intelligence analysts. The responsibility is to explain uncertainty and correct a report when its assumptions fail.
“Data analyst” is a broad title, not a distinct BLS Outlook Handbook category. This guide uses O*NET Business Intelligence Analysts as a proxy for reporting work. Its wage and employment panel uses Data Scientists data. BLS says data scientists also develop and validate models, which may exceed a reporting analyst’s scope.
Getting started
Ways into the work.
O*NET places this business intelligence proxy in Job Zone Four, indicating considerable preparation; it does not set a universal credential for data analyst jobs. Start with a reproducible analysis: define a question, inspect a source, clean inconsistencies, calculate a result, and explain its limitations to someone new to the data.
Move between records and a plain-language decision. O*NET includes databases, spreadsheets, dashboards, and stakeholder communication, so show both calculation and explanation. Keep a data dictionary and notes on missing records. A vacancy supporting operational reporting, market intelligence, research, or predictive modeling can require different preparation. Compare its tasks before choosing a course or treating a tool list as the job.
Pay and opportunities
Where to check the numbers.
O*NET’s business intelligence tasks describe one slice of analysis, while the displayed labor-market figures belong to Data Scientists. BLS measures that broader occupation, including model development. This guide therefore makes no pay or growth claim for the title “data analyst.”
Locally, distinguish dashboard reporting from research or model development. Check the decisions the work supports, available data access, and whether the analyst owns definitions or produces reports. Compare listed pay and credentials among vacancies with similar duties.
AI and the work
Tasks, tools and judgment.
AI can help draft a query, chart description, or investigative question. You still need to verify fields, calculations, and claims. In the experiment, reproduce the answer without trusting a generated explanation. The judgment in this proxy role is whether evidence fits the question and whether a reader can act without being misled.
Try a small practice task
Use a small public or self-created dataset to answer one concrete operational question.
- 01
Write the question, define the measure, and note which records or fields you will use.
- 02
Check for missing or inconsistent values, then make one table or chart that answers the question.
- 03
Give a short recommendation with one limitation; ask a reader what conclusion they took from it.
Afterward, ask yourself: Did you prefer cleaning the evidence, finding the pattern, or helping another person interpret it—and where did you feel least confident?
Try a guided fictional case →These original exercises explore one part of the work. They are not graded assessments or complete simulations of the occupation.
Make this useful to you
Keep the thought.
Choose a next step.
Not a score or a career verdict. Just your reasons to look closer—or move on.
Notes stay in this browser only. Avoid sensitive personal information. Saving notes also adds this role to your saved paths.
Evidence and limits
Where this guide draws from.
There is no single BLS OOH “data analyst” profile. O*NET Business Intelligence Analysts is a deliberate proxy for business reporting tasks; other data-analyst jobs may map elsewhere. O*NET shows Data Scientists wage and employment data on that page, so this guide omits a generic data-analyst wage and growth figure. The day and experiment are illustrative editorial scenarios, not practitioner testimony.
- O*NET OnLine: Business Intelligence Analysts (15-2051.01) ↗Reporting, dashboards, data interpretation, work context, and preparation; task proxy for one data-analyst branch.
- BLS Occupational Outlook Handbook: Data Scientists ↗Shows the broader occupation behind the O*NET wage and employment data and its additional modeling duties; not used for a data-analyst pay claim.
O*NET® occupational information is adapted under CC BY 4.0. Ikigain wrote the explanations and exercises; the U.S. Department of Labor has not approved or endorsed them. U.S. sources describe broad roles, while tasks, credentials, pay and opportunities vary by place and employer. This is an editorial guide, not individualized career advice.
Saved paths and work-sample notes stay in this browser. Fictional exercises are for exploration, not professional assessment.