Check the component’s fit
Choose the date preparation and aggregation operators. Review the input fields and definitions each expects before connecting the regional dataset.
Scientist · Reusable operators
Reuse the building block. Recheck the assumption.
Assemble familiar preparation and analysis components into the next investigation. Scientist lets researchers reuse operators while keeping their configuration connected to the question being studied.
Reusable operators
Reuse preparation operators for a regional study
Build a regional renewal workflow using the existing date preparation and cohort aggregation operators.
Preparation component
Normalize subscription dates
Analysis component
Aggregate renewal activity by cohort
Study configuration
Regional account filter · Relevant observation period
Reuse the analytical building blocks while reviewing their assumptions.
Follow the investigation
A concrete path through the research.
A regional renewal study can start from established date preparation and cohort aggregation. Reuse is useful only if those components still express the right population, date boundaries and unit of analysis.
Configure the current study
Apply the regional selection and observation period. Keep these study-specific settings distinct from the reusable preparation logic.
Inspect the combined result
Review boundary dates and a small set of relevant records. Check that the connected components preserve the cohort definition intended for this investigation.
What to examine
Put the analytical choices in view.
The useful output is not just a completed run. It is an analysis whose inputs, assumptions and limits another researcher can question.
Expected input
Field meanings and formats—not just matching column names.
Study configuration
The region, observation period and cohort definition for this run.
Reuse decision
Whether to keep the component, adjust its settings or revise the underlying logic.
Scientist in the DO Stack
Take the research to the right next owner.
Developer can help turn specialist code into maintainable implementation work. Agree the input/output contract and review responsibilities before treating an analytical component as a production software dependency.
What should we validate for implementation?
Validate the operator library, packaging, dependency handling and supported reuse mechanisms. Do not assume a public marketplace, automatic compatibility checks or dependency pinning across environments.
What should the team bring to a first discussion?
Bring one research question, its source requirements, a relevant workflow and the review criteria. Confirm available capabilities, access and operating requirements before using sensitive material or applying a finding operationally.
Start with a question worth investigating.
Bring the question, the available data and the people who understand it. We’ll work through the analytical path, the review points and the implementation requirements.