データウェアハウスアーキテクト
コンピュータ・数学AI露出度
- データ出典: BLS公表時点: 2026-08
非常に高い· 相対
低い4段階の相対区分非常に高い職業群単位の値
尺度・母数・出典
4段階の相対区分（低い / 中程度 / 高い / 非常に高い）
BLS雇用見通し表の詳細職業831件が母数。値は NEM(全国雇用マトリクス)コード単位で付与されるため、同じ NEM コードの職業は同じバンドになります
- データ出典: Anthropic公表時点: 2026-03
0.579
0.000ここに掲載された値の範囲0.745尺度・母数・出典
- データ出典: ILO公表時点: 2025
0.55
0.09ここに掲載された値の範囲0.70職業群単位の値
尺度・母数・出典
生成AI露出度指数、公開されたまま0–1
ISCO-08の職業小分類単位 — 同じコードの職業はすべて同じ値
当サイトの算出であり、ILOが公表した数値ではありません。当サイトがILOデータセットに結び付けた職業1,012件のうち、この値以上のものは12%です。
この出典がどのような性格の値か
BLSの区分は絶対水準ではなく相対順位であり、一次測定でもありません。複数の既存研究が付けた職業別パーセンタイル順位を4段階にまとめた値です。雇用や賃金の予測でもなく、導入確率でもなく、自動化と増強を区別しません。
Task-level exposure
非表示の作業 29 件を表示| Task | Claude.aiRaw / share % | APIRaw / share % |
|---|---|---|
Write new programs or modify existing programs to meet customer requirements, using current programming languages and technologies.15-1243 | 1.470080.8 | 1.350081.8 |
Perform system analysis, data analysis or programming, using a variety of computer languages and procedures.15-1243 | 0.09004.9 | 0.08004.8 |
Create supporting documentation, such as metadata and diagrams of entity relationships, business processes, and process flow.15-1243 | 0.07003.8 | 0.01000.6 |
Train users and answer questions.15-1243 | 0.06003.3 | 0.00000.0 |
Provide technical support to junior staff or clients.15-1243 | 0.04002.2 | 0.00000.0 |
Develop and document database architectures.15-1243 | 0.02001.1 | 0.01000.6 |
Write and code logical and physical database descriptions, and specify identifiers of database to management system or direct others in coding descriptions.15-1243 | 0.02001.1 | 0.01000.6 |
Test software systems or applications for software enhancements or new products.15-1243 | 0.01000.5 | 0.10006.1 |
Design databases to support business applications, ensuring system scalability, security, performance, and reliability.15-1243 | 0.01000.5 | 0.01000.6 |
Provide or coordinate troubleshooting support for data warehouses.15-1243 | 0.01000.5 | 0.01000.6 |
Values in this tab are predicted labels, not observations. Eloundou et al. (2023) published two rating regimes — human raters and GPT-4 — and the β shown here is derived from the GPT-4 rater basis alone; the same task can take a different value under the other regime. The unit and the meaning differ from the observed shares (%) in the other tabs, so do not place them on the same axis.
| Task | βE1 + 0.5 × E2 |
|---|---|
Test software systems or applications for software enhancements or new products.O*NET Task ID 16116 | 1.0 |
Review designs, codes, test plans, or documentation to ensure quality.O*NET Task ID 16117 | 1.0 |
Prepare functional or technical documentation for data warehouses.O*NET Task ID 16119 | 1.0 |
Write new programs or modify existing programs to meet customer requirements, using current programming languages and technologies.O*NET Task ID 16120 | 1.0 |
Select methods, techniques, or criteria for data warehousing evaluative procedures.O*NET Task ID 16122 | 1.0 |
Perform system analysis, data analysis or programming, using a variety of computer languages and procedures.O*NET Task ID 16123 | 1.0 |
Map data between source systems, data warehouses, and data marts.O*NET Task ID 16124 | 1.0 |
Implement business rules via stored procedures, middleware, or other technologies.O*NET Task ID 16125 | 1.0 |
Develop and implement data extraction procedures from other systems, such as administration, billing, or claims.O*NET Task ID 16126 | 1.0 |
Develop or maintain standards, such as organization, structure, or nomenclature, for the design of data warehouse elements, such as data architectures, models, tools, and databases.O*NET Task ID 16127 | 1.0 |
β = E1 + 0.5 × E2 · E1 = tasks where direct LLM access alone cuts time by at least 50%, E2 = tasks where software built on top of an LLM cuts time by at least 50%. Values take only 0 / 0.5 / 1.0.
Data sources & licenses — O*NET®, Anthropic Economic Index, Eloundou et al. (2023): see full notices on the Credits page