Share and intensity of work current AI systems can materially affect.
SOC 15-2051
Data Analysts AI displacement risk
Routine reporting, data cleaning, and dashboard refreshes are highly exposed to automation. Analysts who frame business questions, validate model output, and translate findings into decisions remain strongly augmentable.
Likely potential for exposed tasks to move to software after workflow integration.
Exposure depends on how the role is scoped. Report-production analysts face more displacement pressure than analysts who own stakeholder framing, metric definitions, and decision quality.
Distribution
Where Data Analysts sits across 620 tracked roles
Displacement pressure 46 — higher than 76% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
30 O*NET task statements matched to SOC 15-2051. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $120,230 (May 2025, US national). The latest BLS row matched SOC 15-2051.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Data Analysts
SOC 15-2051 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 46/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Data Analysts
The current evidence import matched 30 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks30
SOC15-2051
- Core task / ID 21823
Analyze, manipulate, or process large sets of data using statistical software.
- Core task / ID 21828
Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
- Core task / ID 21837
Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
- Core task / ID 21829
Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
- Core task / ID 21836
Recommend data-driven solutions to key stakeholders.
- Core task / ID 21831
Identify business problems or management objectives that can be addressed through data analysis.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Generate standard reports
Exposure 84, automation 62%, augmentation 58%.
O*NET evidence: Generate standard or custom reports summarizing business, financial, or economic data f... (ID 16150)
Clean and prepare raw data
Exposure 78, automation 55%, augmentation 60%.
O*NET evidence: Clean and manipulate raw data using statistical software. (ID 21826)
Build dashboards and visualizations
Exposure 66, automation 38%, augmentation 72%.
O*NET evidence: Create graphs, charts, or other visualizations to convey the results of data analysis u... (ID 21828)
Frame business questions
Exposure 32, automation 10%, augmentation 48%.
TaskExposureAutomationAugmentation
Generate standard reports8462%58%
Clean and prepare raw data7855%60%
Build dashboards and visualizations6638%72%
Frame business questions3210%48%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateAnalytics Engineer
Training horizon: 4-8 months. Skill overlap 74. Wage preservation signal 106.
- Learn data modeling tools
- Own metric definitions
- Document pipeline quality checks
adjacent role
ModerateData Quality Specialist
Training horizon: 3-6 months. Skill overlap 70. Wage preservation signal 98.
- Build validation rule sets
- Track recurring data defects
- Partner with engineering on fixes
Comparison guides
Compare the next move before you commit
4-8 months
Data Analysts to Analytics Engineer
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Data Analysts into Analytics Engineer.
3-6 months
Data Analysts to Data Quality Specialist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Data Analysts into Data Quality Specialist.
What the AI risk score means for Data Analysts
The displacement pressure score for Data Analysts is 46. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Generate standard reports carries 62% automation pressure, while Build dashboards and visualizations carries 72% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $120,230 (May 2025, US national). Employment context: Large and growing analytics workforce. Typical education: Bachelor's degree common.
Wage vulnerability is 28, while transition feasibility is 78. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Report production is exposed
- Strong augmentation upside
- Domain context protects senior roles
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Data Analysts, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
SQL and Python analysis
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Data validation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Dashboard storytelling
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Prompted exploration
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Analytics Engineer, such as learn data modeling tools.
- By 90 days, compare internal openings and external postings for Analytics Engineer or Data Quality Specialist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Data Analysts
Will AI replace Data Analysts?
Routine reporting, data cleaning, and dashboard refreshes are highly exposed to automation. Analysts who frame business questions, validate model output, and translate findings into decisions remain strongly augmentable. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Data Analysts work are most exposed to AI?
Generate standard reports and Clean and prepare raw data show the strongest automation pressure in this model. Build dashboards and visualizations and Clean and prepare raw data are better treated as AI-augmented work.
What should Data Analysts learn next?
Start with SQL and Python analysis, Data validation, Dashboard storytelling. The most practical adjacent paths in this model are Analytics Engineer and Data Quality Specialist.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureMcKinsey Global Institute / 26 July 2023Generative AI and the Future of Work in AmericaO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics