Intelligence Brief Technology Sector
Data Analyst
Data analysts answer questions with data. They pull and clean data, build reports and dashboards, find patterns, and explain what the numbers mean so the business can act. It's often the most accessible entry point into …
- $73,000
- Median salary
- 23%
- Projected growth
- 63/100
- Difficulty
- Bachelor's (field flexible)
- Min. education
Executive Summary
- Data Analyst scores 60/100 (C+), reflecting a balanced profile relative to other careers.
- Median salary of $73,000 reflects moderate earning potential.
- Projected growth of 23% significantly outpaces the national average of 4%.
- AI resilience score of 52 indicates moderate disruption risk — core human elements remain, but routine tasks face automation pressure.
Data Analyst scores 60/100 — C+. The strongest dimension is remote potential (90/100), followed by job growth (81/100). The biggest challenge: salary (37/100).
Research Insights
- Conditional
Future-proof
Data Analyst is conditionally future-proof (63/100). The career offers solid fundamentals but faces moderate AI disruption risk that professionals should monitor. Strategic upskilling in technology domain expertise can strengthen long-term positioning.
Score 63 /100 - Limited
Social Mobility
Data Analyst has limited social mobility potential (36/100). The combination of below-average earning potential makes this a challenging path for upward economic mobility. Consider alternative paths in the Technology field that offer stronger returns on educational investment.
Score 36 /100 - Solid
Long-Term Outcomes
Data Analyst offers solid long-term outcomes (60/100), with a scorecard grade that reflects above-average overall value. The career provides stable earning potential, but professionals should actively manage career development to maximize long-term trajectory.
Score 60 /100
Economic Importance
Data analysts play a crucial role in interpreting complex data sets to inform business decisions across industries. Their insights drive efficiency, optimize operations, and enhance customer experiences, making them integral to competitive strategies in a data-driven economy.
Role Analysis
What a Data Analyst Does
Data analysts answer questions with data. They pull and clean data, build reports and dashboards, find patterns, and explain what the numbers mean so the business can act. It's often the most accessible entry point into the data field — less modeling-heavy than data science, but built on the same foundation of SQL, spreadsheets, and clear communication.
The role fits curious, organized people who like finding the story in a spreadsheet and don't need a graduate degree to start. It's also a launchpad: many analysts grow into data science, analytics engineering, or business strategy. The work rewards people who pair technical skill with business sense — knowing which question is worth answering is half the job.
A Day in the Life
- Write SQL queries to pull and join data
- Clean and validate datasets
- Build dashboards and recurring reports
- Analyze trends and answer business questions
- Present findings clearly to stakeholders
- Define and track key metrics
Compensation Structure
By Experience Level
- Entry level (0-2 yrs)
- $55,000 - $72,000
- Mid-career (3-6 yrs)
- $75,000 - $95,000
- Senior / lead (7+ yrs)
- $100,000 - $130,000+
By Company Size
| Company | Base | Bonus | Equity | Total |
|---|---|---|---|---|
| Small business / Startup | $55,000 - $72,000 | $1,000 - $5,000 | $0 - $5,000 | $56,000 - $82,000 |
| Mid-market | $75,000 - $95,000 | $2,000 - $10,000 | $0 - $10,000 | $77,000 - $115,000 |
| Large corporate | $80,000 - $100,000 | $3,000 - $15,000 | $0 - $20,000 | $83,000 - $135,000 |
| Enterprise / Public company | $90,000 - $130,000 | $5,000 - $20,000 | $0 - $30,000 | $95,000 - $180,000 |
Compensation typically increases with company size, as larger organizations often have more resources to allocate to data roles. Startups may offer lower base salaries but potentially higher equity options, while large corporations provide more structured compensation packages.
Outlook · 23% growth
Growth of about 23% is much faster than average as nearly every organization tries to make better use of its data. Because it's an accessible entry point, the role is competitive — but it's also one of the clearest on-ramps to higher-paid data and analytics careers.
Career Pathways
The trajectory to Data Analyst varies by entry point and specialization. Below are the most common paths, typical timelines, and advancement probabilities.
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Traditional Path
Obtain a relevant bachelor's degree → Gain experience through internships → Apply for entry-level analyst positions → Progress to mid-career roles → Target a senior analyst role- Timeline
- 3-5 years
- Advancement probability
This path is effective due to structured education and clear career progression within organizations.
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Skill-Based Entry
Self-study data analysis tools → Build a portfolio with real projects → Network and seek entry-level opportunities → Transition to mid-career roles → Aim for specialized positions- Timeline
- 2-4 years
- Advancement probability
This approach benefits from practical experience and showcases skills through real-world applications, although networking is key.
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Consulting Path
Start in a consulting role → Develop data analysis skills on projects → Move to a dedicated data analyst position → Ascend to strategic roles → Aim for senior leadership in analytics- Timeline
- 5-7 years
- Advancement probability
This track leverages consulting experience, allowing for diverse exposure to different industries and data challenges.
Common Credentials
- Google Data Analytics Certificate
- Tableau / Power BI certifications
Skill Stack
The Data Analyst skill set operates across four layers. Differentiator skills (marked) are the competencies that most strongly predict advancement to this role.
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Foundation
- SQL
- Excel / spreadsheets
- Basic statistics
- Data visualization fundamentals
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Intermediate
- Data cleaning techniques
- Intermediate statistics
- Business analysis
- Communication skills
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Advanced
- Advanced statistical methods
- Programming in Python or R
- Complex data visualization
- Predictive analytics
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Differentiating
Differentiator- Data storytelling
- Strategic thinking
- Machine learning basics
- Cross-functional collaboration
Scorecard Analysis
Our proprietary scorecard evaluates careers across five dimensions from BLS wage and growth data, O*NET work context, and standard education requirements. The blended difficulty score reflects the combined challenge across all metrics.
Below-average earning
Exceptional job growth
Moderate education barrier
Excellent remote options
Less competitive
Career Difficulty Score
63/100
Data Analyst offers exceptional job growth, excellent remote work potential and a less competitive field.
AI Resilience Assessment
Our AI Resilience score estimates how likely a career is to be disrupted by artificial intelligence. Scores are based on a category baseline adjusted by keyword analysis of job duties. A score of 70+ means low automation risk; 50\u201369 means moderate risk; below 50 means high risk.
- Core analytical and problem-solving skills transfer well to AI-augmented workflows.
- AI can handle routine reporting, data aggregation, and first-pass analysis, freeing time for higher-value work.
- Risk factor: Entry-level coding and testing tasks face direct competition from AI code generation tools.
AI Verdict
Data Analyst faces moderate disruption risk. While AI will automate routine components, core responsibilities still require human oversight, strategic thinking, and interpersonal skills. Upskilling in AI collaboration tools is recommended for long-term career stability.
Risk Factors & Failure Modes
Understanding where professionals stall or fail to reach this role is as important as knowing the path. Below are the most common bottlenecks.
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Inadequate understanding of business objectives can lead to irrelevant analyses.
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Failure to master core analytical tools may hinder job performance.
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Poor communication skills can result in misinterpretation of data findings.
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Neglecting to stay updated on industry trends can lead to outdated practices.
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Insufficient attention to detail may result in errors that compromise analysis quality.
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Limited networking opportunities can stifle career advancement in a competitive job market.
Data Analyst Archetypes
There is no single profile for a Data Analyst. Professionals reach this role through different backgrounds, each bringing distinct strengths and limitations.
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The Business Insights Analyst
Typically comes from a business administration background, focusing on translating data into actionable insights for stakeholders.
Strengths
- Strong communication skills
- Deep understanding of business operations
- Ability to present data visually
- Proficient in SQL and Excel
Weaknesses
- Limited technical programming skills
- May overlook deeper statistical analysis
- Less focus on data science techniques
Best fit: Best suited for mid-sized companies looking to optimize operations or improve marketing strategies.
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The Technical Data Specialist
Often has a background in data science or mathematics, concentrating on data cleaning and preparation for analysis.
Strengths
- Expertise in programming languages like Python or R
- Strong statistical analysis skills
- Attention to detail
- Ability to work with large data sets
Weaknesses
- May struggle with communicating findings to non-technical stakeholders
- Can be overly focused on technical details
- Limited business acumen
Best fit: Ideal for tech companies or analytics firms that require in-depth data manipulation and analysis.
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The Visualization Guru
Usually comes from a design or data visualization background, specializing in creating intuitive dashboards and visual reports.
Strengths
- Proficient in data visualization tools like Tableau or Power BI
- Strong aesthetic sense for design
- Ability to tell a story with data
- Good communication skills
Weaknesses
- May lack depth in statistical analysis
- Can prioritize aesthetics over accuracy
- Limited programming skills
Best fit: Works best in marketing firms or organizations focused on data storytelling and customer engagement.
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The Strategic Data Advisor
Generally has experience in strategy development, combining data analysis with business strategy insight.
Strengths
- Strong business sense
- Ability to align data findings with strategic goals
- Excellent communication and presentation skills
- High adaptability
Weaknesses
- Might lack technical skills in data manipulation
- Can be overwhelmed by data complexities
- May have slower execution speed due to strategic considerations
Best fit: Best suited for consulting firms or large corporations focused on strategic growth.
Decision Intelligence
Beyond the numbers: assessing fit, risk, and realistic expectations for this career path.
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Personality Fit
Individuals who are analytical, detail-oriented, and enjoy problem-solving thrive as data analysts, while those who prefer creative or unstructured environments may struggle.
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Risk Tolerance Required
This career typically offers a moderate risk/reward profile, with steady demand but potential for challenges in data validity and interpretation.
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Work-Life Reality
Data analysts often experience a balanced work-life intensity, usually maintaining standard office hours with occasional project deadlines that can increase pressure.
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Cognitive Demands
The role requires high cognitive demands, including the ability to tolerate ambiguity and engage in systems thinking to interpret complex data.
Feeder Degrees
Data Analysts come from a variety of educational backgrounds. Below are the most common degrees held by professionals in this field, ranked by median salary.
- 1Data ScienceBachelor's 4 years OnlineTop schools: MIT, Stanford University, UC Berkeley$108,020Median36%Much faster than average
- 2MathematicsBachelor's 4 yearsTop schools: MIT, Princeton, Harvard University$104,280Median8%Faster than average
- 3StatisticsBachelor's 4 years OnlineTop schools: Stanford University, UC Berkeley, Carnegie Mellon$104,110Median30%Much faster than average
- 4Business AdministrationBachelor's 4 years OnlineTop schools: University of Pennsylvania, University of Michigan, UC Berkeley$76,850Median6%Faster than average
Source Schools
Institutions whose degree programs appear most frequently among the top-ranked programs for the degrees that feed this career path.
Institutions With Strong Outcomes
Institutions with meaningful programs in Technology, Sciences, Business, ranked by median graduate earnings 10 years after enrollment.
- 1 Massachusetts Institute of Technology MA · 96% graduate $143,372 Median earnings
- 2 Harvey Mudd College CA · 93% graduate $138,687 Median earnings
- 3 University of Health Sciences and Pharmacy in St. Louis MO · 69% graduate $137,047 Median earnings
- 4 Albany College of Pharmacy and Health Sciences NY · 68% graduate $131,426 Median earnings
- 5 California Institute of Technology CA · 94% graduate $128,566 Median earnings
- 6 Massachusetts College of Pharmacy and Health Sciences MA · 63% graduate $125,557 Median earnings
Where Data Analysts Get Hired
Graduates who become Data Analysts frequently land at employers like Amazon, Microsoft, Apple and Google. Each profile below shows the schools that feed it, the degrees that lead there, and its current hiring momentum.
Amazon
Technology · Technology
Microsoft
Technology
Apple
Technology
Technology
Dell
Technology
Adobe
Technology
Methodology & Data Sources
Salary and growth data sourced from the Bureau of Labor Statistics Occupational Employment and Wage Statistics (OEWS) and Employment Projections program. Education requirements and work context derived from O*NET. AI Resilience scores are proprietary, based on category baselines adjusted by keyword analysis of job duties against current AI capability benchmarks. Pipeline probabilities and compensation by company size are modeled estimates synthesized from executive compensation surveys and industry research. Degree and school outcome data sourced from the U.S. Department of Education College Scorecard and Opportunity Insights. Editorial intelligence sections (archetypes, risk factors, decision intelligence) are research-based assessments, not predictive models.
Data Behind This Page Updated 2025
Source datasets
Methodology
Careers are scored on five normalized axes — salary, job growth, AI resilience, education barrier, and competition — each on a 0–100 scale, with composite Future-Proof, ROI, and breadth verdicts.
See the full methodology and weights →Confidence notes
- Salary and growth figures come from federal Bureau of Labor Statistics data — administrative wage records and official projections, not surveys.
- AI-resilience scores are computed from O*NET task and work-context data, applied consistently across every occupation.
- Every measure is normalized to a fixed 0–100 scale, so careers are directly comparable.
Limitations
- BLS wage data reflect national medians; actual pay varies widely by region, employer, and experience.
- Job growth is a 2023–2033 projection, not a guarantee — labor markets shift with technology and the economy.
- AI-resilience is a directional estimate of automation exposure, not a prediction that any role will or will not be automated.
- Pipeline and compensation-by-company-size figures are modeled estimates, not measured outcomes.
