ISCO 2529 · O*NET 15-2051.00 · codes unverified
Data analyst
Among the most assisted occupations here and among the more exposed. Asking the right question survives; producing the answer largely does not.
A large share of the work is moving. The remaining core is narrower and more senior than it used to be.
Scale, labels and thresholds are provisional. This is open question Q-02.
15 of 20 in the launch set
How the score is made
Four declared inputs, never collapsed into one number. Assistance is kept apart from outright automation, because a tool that makes you faster and a tool that replaces you are not the same event. Physical work counts as shelter only to the extent robotics cannot reach it.
The full method is written out, including what it cannot tell you.
Note on this occupation Placeholder values. The ISCO mapping here is poor: analysts are spread across several units. This is the granularity problem again.
- Assisted
- 88
- Done outright
- 70
- Physical
- 4
- Robotics reach
- 30
Higher is generally good news. It means more of the work gets faster without the work going away.
This is the number most people mean when they say exposure. It is kept separate from assistance on purpose.
Protective only for as long as robotics cannot reach it. Read this one next to the robotics number, never alone.
Low reach turns physical work into shelter. High reach turns it into the main exposure.
placeholder Working Paper 140, Generative AI and Jobs 2025 index O*NET 31.0 Anthropic Economic Index Pin one release data 0.1-placeholder
What changes
Tasks moving to machines, in whole or in part.
- Writing queries and building routine reports.
- Cleaning, joining and reshaping data.
- Producing the standard chart pack.
What stays human
Tasks that need a person, and why.
- Knowing which question is worth answering.
- Recognising when a result is too convenient to be true.
- Telling a decision-maker something they do not want to hear.
What's new
Work that did not exist in this job ten years ago.
- Checking machine-generated analysis that is fluent and occasionally wrong.
- Governance over what data may be used, and how.
- Far more analysis requested, because asking has become nearly free.
These three lists are editorial commentary, written against task-level data but not generated from it (OCC-05). Treat them as the author's reading, not as output.
Compare with another job
Pick one. You get a card with both scores, both sets of components, and a link worth sending to someone.
- Hairdresser 84
- Plumber 82
- Primary school teacher 79
- Electrician 79
- Aged care worker 75
- General practitioner 73
- Registered nurse 72
- Chef 69
- Fashion designer 65
- Civil engineer 59
- Product designer 55
- Software developer 44
- Graphic designer 43
- Heavy truck driver 34
- Paralegal 31
- Accountant 28
- Copywriter 24
- Bank teller 13
- Customer service representative 12
Sources used on this page
- Working Paper 140, Generative AI and Jobs
International Labour Organization · 2025 index · Confirm
- O*NET
US Department of Labor · 31.0 · CC BY 4.0
- Anthropic Economic Index
Anthropic · Pin one release · Confirm
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