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Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.

Use this Skill: https://skilld.dev/gh/nimrodfisher/data-analytics-skills/analysis-planning

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referencesrisks_dependencies.md

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Risks and Dependencies in Analysis Planning

How to identify, document, and mitigate things that could delay or invalidate an analysis before they cause problems.


Dependency Types

Data Dependencies

Dependency Example How to resolve
Data access not yet granted Need access to a vendor's export Raise access request immediately; don't start the analysis until confirmed
Data doesn't exist Metric was never tracked Scope out or use a proxy; document the limitation
Data is unreliable / known quality issues A known ETL bug affects the column Document the issue; decide whether to proceed with caveated results or wait for the fix
Data is too fresh (not enough history) New feature only 2 weeks old Narrow the question or set a longer timeline

People Dependencies

Dependency Example How to resolve
SME input required Need product manager to confirm event definition Schedule immediately; flag as a blocker if unresolved
Stakeholder needs to confirm scope Requirements not yet signed off Do not start until sign-off; treat as day 0
Another analyst is needed Two people needed for parallel work Confirm availability before committing the timeline

Tool / System Dependencies

Dependency Example How to resolve
Warehouse quota / cost approval Large query needs DBA sign-off Get approval before running
Dashboard tool access Need a BI tool licence Request early; use an alternative format as fallback
Third-party data delivery Vendor sends a file on the 5th of the month Build the timeline around the delivery date

Risk Register

For each identified risk, record:

Field What to capture
Risk Brief description of what could go wrong
Probability High / Medium / Low
Impact on timeline Hours / days it would add
Mitigation Action taken before the risk materialises
Contingency What to do if the risk materialises anyway

Common Analysis Risks

Risk Probability Typical impact Mitigation
Data quality issue discovered mid-analysis Medium +0.5–1 day Run EDA on data before committing to approach
Scope expands after requirements sign-off High +0.5–2 days Document scope boundaries explicitly; get written sign-off
Stakeholder unavailable for feedback Medium +1–3 days Book feedback slot in advance; set a response deadline
Key assumption turns out to be wrong Medium Restart sub-questions Make assumptions explicit early; validate before building on them
Approach doesn't work on the actual data Low–Medium +0.5–1 day Run a proof-of-concept before full implementation
External data delivery delayed Low–Medium +1–5 days Identify alternative or contingency approach

Escalation Trigger

Escalate to the requestor when:

  • A dependency will cause the delivery date to slip by more than 20%
  • A key assumption is found to be incorrect and changes the conclusion
  • The data needed for a must-have requirement doesn't exist
  • A risk materialises and changes the feasibility of the plan

Escalate early. A stakeholder told on day 1 that the delivery will be late has options. A stakeholder told on the eve of the delivery has none.

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub16d

    This skill provides a structured framework and templates for data analysis planning. It contains no executable code, network requests, or sensitive data access, and is safe to use.

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    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

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Last checked against GitHub 5 days ago.

Activeupdated 5 months ago

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