The bottom line
Roughly 85% of the work in Prepare data is information-shaped — already within reach of AI delivery. The question here is not whether it shifts, but which tasks go first and who staffs the residual.
Why: With no child occupations seeded, the score is derived from the process name and lens. Although the parent PCF category ('Manage environmental health and safety (EHS)') suggests a hybrid real-world context, the specific process ('Prepare data') and its description ('Creation and validation of data in order to initiate the process of analysis') indicate pure information transformation. Because the value-producing work is data manipulation, it is highly digital.
grounded in the economy graph · digital scalar 0.85 · digital
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How the work flows
Trigger: An analytical model, report, or business intelligence dashboard requests specific data inputs for processing.
- Identify target data sources and requirements
- Extract raw data from source systems
- Cleanse data of duplicates and formatting errors
- Transform values into standardized data models
- Validate dataset against quality thresholds
- Load prepared data into analytical environments
Outcome: A validated, standardized dataset is loaded into the target environment for analysis.