Workbook

Make the Mission Yours

Role: Data Analyst

Use these activities to apply each principle to your current product, service, or project. These activities are a sample to get you started, not an exhaustive list. Adapt and expand them based on your team's context and needs. Capture your answers, share them with your team, and revisit them as you learn.

⚠️

Important: When Using AI Tools

When using AI-assisted activities, always double-check for accuracy and meaning each and every time. AI tools can help accelerate your work, but human judgment, validation, and critical thinking remain essential.

Review AI-generated content with your team, validate it against real user feedback and domain knowledge, and ensure it truly serves your mission and user outcomes before proceeding.

1) Shared Mission and Vision

Align every analysis to mission questions.

💡

Learn More

For more information and deeper understanding of this principle, refer to the 1) Shared Mission and Vision section in the framework.

Workbook Activities (do now)

☐Add a “mission question” line to each analysis request/output.
☐Prioritize metrics that reflect user outcomes; demote vanity metrics.
☐Validate with PM/Eng that your current sprint questions align to outcomes.
☐Rewrite one request that is vague into a clear mission/outcome question and confirm it.
☐Pin a mission metric at the top of your next report and explain its relevance.

AI Assisted Activities

☐Use AI to help draft analysis frameworks that map to mission outcomes, but have your team review and refine them to ensure they reflect real user needs and business goals.
☐Ask AI to generate potential analysis questions based on mission outcomes, then validate each question against direct user feedback and domain knowledge before conducting analysis.
☐Use AI to help structure your mission-aligned analyses, but ensure human team members validate that each analysis truly serves the mission before proceeding.
☐Have AI analyze past analyses to identify mission alignment patterns, then use those insights in team discussions to improve how data work connects to user outcomes.

Evidence of Progress

☐Analyses start with a mission question.
☐Metrics reviewed in rituals tie to user/business outcomes.

2) Break Down Silos

Define data needs jointly with builders.

💡

Learn More

For more information and deeper understanding of this principle, refer to the 2) Break Down Silos section in the framework.

Workbook Activities (do now)

☐Run a tracking plan session with PM/Eng before build; publish events/specs.
☐Sync with QA to validate data capture in staging before launch.
☐Hold a 15-minute “data readiness” check before release.
☐Pair with a developer to confirm event payloads and edge cases for this release.
☐Replace a metric-definition thread with a live alignment and update the dictionary.

AI Assisted Activities

☐When AI generates tracking plans or data specifications, have cross-functional team members (PM, engineering, QA) review them together to ensure they serve users and align with mission.
☐Use AI to help draft data dictionaries or tracking designs, but ensure all roles contribute their perspectives during the actual tracking design sessions.
☐Have AI analyze data handoff patterns and metric gaps, then use those insights in cross-functional discussions to improve collaboration.
☐Use AI to help structure data collaboration sessions, but ensure human team members make decisions together about what to measure and how it serves users.

Evidence of Progress

☐Fewer missing events post-release.
☐QA sign-off includes data validation.

3) User Engagement

Ground numbers in observed user behavior.

💡

Learn More

For more information and deeper understanding of this principle, refer to the 3) User Engagement section in the framework.

Workbook Activities (do now)

☐Shadow a support/sales call; extract behaviors to watch in data.
☐Pair a usability test with a metric you own; reconcile observation vs. data.
☐Translate one top user pain into a measurable funnel/metric.
☐Add one user quote to your next chart to anchor the behavior you’re measuring.
☐Validate a surprising trend by contacting support/PM for qualitative context.

AI Assisted Activities

☐Use AI to analyze user feedback, support tickets, or usage data to identify patterns for analysis, but always validate AI insights through direct user engagement or observation.
☐Have AI generate questions for user interviews based on your data assumptions, then use those questions in real conversations with users to build genuine empathy.
☐Use AI to help summarize user research findings for analysis, but ensure you review the summaries and add your own observations from direct user interactions.
☐Have AI analyze user behavior patterns from your data, then discuss those patterns with actual users to understand the "why" behind the behavior before finalizing analysis.

Evidence of Progress

☐Dashboards include context from user observations.
☐A new metric/funnel comes directly from qualitative insight.

4) Outcomes Over Outputs

Deliver readouts that drive decisions.

💡

Learn More

For more information and deeper understanding of this principle, refer to the 4) Outcomes Over Outputs section in the framework.

Workbook Activities (do now)

☐For each release, publish a one-page outcome readout with a decision/recommendation.
☐Flag when outcomes did not move; propose a hypothesis and next test.
☐Maintain a decision log tied to your analyses.
☐Create two versions of the readout (exec and squad) and share both.
☐If the outcome missed, propose one follow-up experiment with timing.

AI Assisted Activities

☐When AI generates analysis reports or outcome readouts, define outcome metrics upfront and measure whether AI-generated insights achieve intended user outcomes, not just data completeness.
☐Use AI to help analyze outcome data and identify patterns, but have human team members interpret what those patterns mean for users and the mission.
☐Have AI help draft outcome definitions and success criteria for your analyses, but ensure the team validates them against real user needs and business goals before proceeding.
☐Use AI to track and report on outcome metrics, but schedule human team reviews to discuss what the metrics mean and how to adjust analysis based on observed impact.

Evidence of Progress

☐Decisions reference your readouts and hypotheses.
☐Outcome gaps lead to follow-up experiments, not speculation.

5) Domain Knowledge

Know the data ecosystem and its constraints.

💡

Learn More

For more information and deeper understanding of this principle, refer to the 5) Domain Knowledge section in the framework.

Workbook Activities (do now)

☐Document lineage and quality for one key metric; share caveats.
☐Map front/back stage data sources for a core journey and note trust levels.
☐Review regulatory/PII constraints with security and reflect in dashboards.
☐Identify the riskiest upstream source and add a monitor or caveat to your report.
☐Meet briefly with a domain owner to confirm assumptions on a metric for this sprint.

AI Assisted Activities

☐Use AI to help summarize domain documentation, data lineage, or privacy constraints for analysis, but validate AI-generated domain knowledge through direct engagement with domain experts.
☐Have AI generate questions about domain constraints or data ecosystem relationships, then use those questions in conversations with domain experts to build deep understanding.
☐Use AI to help draft data lineage maps or metric definitions, but ensure team members review them with domain experts to verify accuracy and completeness.
☐Have AI analyze past analyses or domain-related data issues, then discuss those insights with the team and domain experts to identify patterns and prevent similar problems.

Evidence of Progress

☐Metric definitions include lineage, quality, and privacy notes.
☐Stakeholders understand data trust boundaries.

6) The Art of Storytelling

Package insights as compelling, usable stories.

💡

Learn More

For more information and deeper understanding of this principle, refer to the 6) The Art of Storytelling section in the framework.

Workbook Activities (do now)

☐Deliver one key finding as a user story with a chart and a quote.
☐Create two versions of a dashboard/slide: exec (outcomes/actions) and squad (details/next tests).
☐Frame a trend as a before/after narrative tied to a release or change.
☐Add a user verbatim to one slide to anchor the metric to real people.
☐Record a 60-second walkthrough of a chart explaining what changed and what to do next.

Evidence of Progress

☐Stakeholders accurately retell your finding.
☐Teams take concrete actions from your story-backed insight.