Data & Technical specialist
Default Kona assistant
Data Analyst
Answers business questions with transparent analysis and decision-ready visuals.
When to use it
A focused role with a visible finish line.
Best for: technical and analytics teams that need reproducible analysis, implementation, or quality work.
Expected outcome: an inspectable technical deliverable with assumptions, tests, and reproducible steps.
- A focused data, analysis, metrics task where the expected decision or deliverable is clear.
- Recurring work that benefits from the same answer, assumptions, risks structure each time.
- A team that wants Data Analyst available in direct chat, inline @mentions, and bounded Workspace tasks.
Configured instruction
“Start from the decision and define the metric precisely. Check data quality, select an appropriate method, show calculations and limitations, and translate findings into actions and further questions.”
This instruction is part of the shipped default profile—not a generic prompt assembled for this page.
Workflow
How Data Analyst approaches the work
- 01
Frame the outcome
State the decision, audience, deadline, constraints, and what a useful an inspectable technical deliverable with assumptions, tests, and reproducible steps looks like.
- 02
Ground the work
Provide relevant schemas, code, logs, event definitions, environments, constraints, and expected behavior. Label supplied facts, working assumptions, and unresolved unknowns.
- 03
Build the contracted output
Data Analyst follows its markdown output contract and covers answer, assumptions, risks, next evidence.
- 04
Verify before use
Run the profile's quality checks, expose evidence gaps, and route high-impact low-risk conclusions to human review.
Capabilities
What the profile can use
- Knowledge grounding
- Structured deliverables
- Verification pass
- Analysis and code
- Editable workspace artifacts
Enabled tool families: code, canvas. Runtime availability still depends on account configuration, permissions, and the task.
Output contract
What a complete response must contain
The contract improves consistency; it does not make an answer automatically correct. Kona still marks assumptions, evidence gaps, and review requirements.
Prompt examples
Start with context and a decision
Prompt 1
Act as my Data Analyst. Start from the decision and define the metric precisely. Check data quality, select an appropriate method, show calculations and limitations, and translate findings into actions and further questions. Start by listing the missing inputs that would materially change the result.
Prompt 2
Use the Data Analyst workflow for this data task: [describe the situation]. Audience: [who will use it]. Constraints: [time, budget, policy, or data]. Return answer, assumptions, risks, next evidence.
Prompt 3
Review this draft as the Data Analyst: [paste draft]. Check it against the stated evidence, identify unsupported claims or missing assumptions, and return a prioritized correction list.
Related specialists
Put Data Analyst to work.
Use the default profile or fork it into a version your team controls.