19 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You split time between planning surveys, running or supervising data collection, and cleaning the results. Mornings often mean checking schedules, calibrating instruments, and briefing recruiters or field staff.
Afternoons are for entering or importing data into Excel or Access, running basic checks in SPSS, and joining short Microsoft Teams meetings with the project team. Some days include site visits to verify measurements or to document operations for reports in Word and PowerPoint.
Expect heavy use of Microsoft Excel for data cleaning and simple summaries, Microsoft Access for storing structured survey records, and IBM SPSS Statistics for statistical checks and analysis. You’ll also draft proposals and reports in Word and PowerPoint and coordinate work through Microsoft Teams and Project.
If your role involves field survey instruments, you’ll still bring results back into these systems for calibration logs, documentation, and visual summaries.
The U.S. Bureau of Labor Statistics reports 8,290 employed in this occupation. Median pay is $69,460 per year, the lowest tenth earn about $39,260, and the top tenth earn about $130,860 (BLS).
Those are national figures; local pay varies with industry, experience, and whether you do specialized field work like underwater or marine surveys.
Compared with a market research analyst, Data Operations Analysts focus more on the technical collection, data processing, instrument calibration, and operational documentation rather than only interpreting consumer trends. You’ll run procedures and fix sampling or nonresponse problems.
Compared with a pure field surveyor, you still do field tasks like operating instruments and recording measurements, but you also spend significant time in databases, SPSS, and project documentation, and may write proposals to win projects.
AI can help draft survey questions, summarize findings for PowerPoint slides, or clean obvious formatting issues in Excel. Always treat AI output as a draft: verify numbers against raw data in Access or SPSS and check wording for bias or ethical problems.
Never use AI to replace consent processes, interpret sensitive survey responses, or make sampling decisions without a human. Keep documentation of choices and ethical approvals in Word and your project logs.
Learning to clean and validate data in Excel and SPSS will change your day-to-day productivity fastest. Be comfortable importing/exporting CSVs, running checks for duplicates, missing data, and basic cross-tabs in SPSS.
Add one practical skill: instrument calibration and documentation. Knowing how to calibrate electronic surveying instruments and record those measurements reduces field errors and stops wasted re-surveys.