Start with the source relationship
Place the account and subscription inputs. Identify the key that connects them and whether one account can have multiple subscriptions.
Scientist · Workflow canvas
Make the route from records to findings readable.
Connect visual operators into an analytical workflow. Scientist makes preparation and analysis visible as a path that collaborators can inspect, from the starting datasets to the output used to investigate a question.
Workflow canvas
Build a renewal cohort preparation workflow
Connect the account and subscription inputs, define eligible renewals, and prepare a cohort summary.
Inputs
Accounts → Subscriptions
Preparation path
Join records → Filter eligibility → Derive cohort
Analysis output
Group by cohort → Inspect summary
Trace the research workflow from source inputs to analytical output.
Follow the investigation
A concrete path through the research.
A cohort summary hides important choices. The canvas brings the join, eligibility filter and cohort derivation into view, so reviewers can question how the population was built before debating its interpretation.
Expose the preparation sequence
Connect the join, eligibility filter and cohort derivation. Make the unit of analysis explicit before aggregation combines the records.
Walk the path with a reviewer
Trace a relevant record through the connected steps. Inspect the summary and decide whether the resulting cohorts fit the question.
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.
Input contract
Expected fields, join keys and the meaning of an account or subscription record.
Transformation logic
Where records are excluded, duplicated or assigned to a cohort.
Output interpretation
The level of aggregation and the population represented in the summary.
Scientist in the DO Stack
Take the research to the right next owner.
Developer becomes relevant when an analysis needs application-specific engineering. Use the visible workflow to communicate the required logic, then separately scope the code, interfaces and review needed for implementation.
What should we validate for implementation?
Confirm the available operators, supported data types and execution behavior for your workflow. A visual path does not guarantee automatic lineage capture across external systems or support for every analytical library.
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.