This playbook is a practical guide to using ADM effectively across roles—data engineers, analytics engineers, data stewards, and business stakeholders.
It focuses on:
- Asking better questions (prompting)
- Choosing modes intentionally (Conversation vs Workflows vs Notebooks)
- Building repeatable operations (triage workflows)
- Collaborating safely (multi-user + permissions)
- Grounding answers using Knowledge Base and MCP
How to Access These Features
- Conversation: left nav → Conversation / New Conversation
- Workflows: left nav → Understanding Workflows / Workflows
- Agents: left nav → Understanding Agents
- Business Notebooks: left nav → Business Notebooks
- Knowledge Base: left nav → Knowledge Base (upload docs)
- Collaboration: open a conversation → Share / Add participants
- MCP: left nav → Understanding MCP Server (and configured integrations)
Playbook by role
Data Engineer (Triage + Remediation)
Best outcomes when you:
- Ask for root cause hypotheses + evidence
- Compare “before vs after”
- Request “what changed” upstream
- Standardize triage into workflows
Go-to prompts
- “Compare today vs yesterday for this asset: what changed in quality, freshness, schema?”
- “List the top 3 likely causes and the fastest checks to confirm.”
Analytics Engineer (Stability + Correctness)
Best outcomes when you:
- Ask for rule-level breakdowns
- Validate assumptions about transformations
- Request impact analysis (downstream assets/dashboards)
Go-to prompts
- “Which downstream dashboards depend on this asset and are impacted?”
- “Which rule failures are most likely due to transformation logic?”
Data Steward (Governance + Standards)
Best outcomes when you:
- Reference documented policies and definitions (Knowledge Base)
- Ask for “standard vs exception”
- Request stakeholder-ready summaries
Go-to prompts
- “According to our standards, what should quality thresholds be for this dataset?”
- “Summarize non-compliant assets and recommend which policies to add.”
Business User (Impact + Decisions)
Best outcomes when you:
- ask for business impact first
- request non-technical language
- ask for “what should I do now”
Go-to prompts
- “Is this metric safe to use today? If not, what’s the recommended workaround?”
- “Summarize impact on revenue/finance reporting in 8 bullets.”
Operational Best Practices (What High-performing Teams Do)
1) Start with a “triage checklist” Approach
Ask ADM to answer, in order:
- What happened (symptoms)?
- When did it start?
- What changed?
- What is impacted?
- What do we do next?
This avoids skipping to conclusions.
2) Turn Repeatable Work into Workflows
Once you run the same analysis more than twice, convert it into a workflow:
- Consistent steps
- Consistent output format
- Reusable across teams
3) Use the Knowledge Base for “single source of truth”
Upload:
- Governance docs
- SLAs
- Definitions of key metrics
- Runbooks
- Incident playbooks
Then ask ADM: “Use Knowledge Base sources and cite them.”
4) Use Collaboration to Reduce Decision Latency
Bring in:
- Data platform (pipelines)
- Domain owners (definitions)
- Consumers (impact confirmation)
Use @mentions + short asks:
- “Can you confirm whether this is expected seasonality?”
- “Is the SLA for this report 9 AM or 11 AM?”
5) Always Ask for “next checks”
For investigations, require ADM to include:
- 3–5 verification checks
- the fastest path to confirm root cause
- recommended owner team
Real-world Scenarios (Templates You Can Copy)
Scenario A — Freshness Breach on a Tier-1 Table
Prompt:
“Investigate why daily_revenue_summary is delayed today. Compare to prior runs, identify the likely cause, list impacted downstream assets, and provide a recommended mitigation plan.”
Expected output:
- Summary (5 bullets)
- Likely root cause + evidence
- Impacted dashboards/reports
- Next steps + owners
Scenario B — Schema Drift Broke a Pipeline
Prompt:
“Schema drift detected on orders. Identify added/removed/modified columns, assess likely pipeline breakpoints, and propose a remediation plan.”
Expected output:
- Changes table (column, change type, risk)
- Suspected breaking dependencies
- Recommended fix steps
Scenario C — Data Quality Score Dropped Suddenly
Prompt:
“Quality score for customer_master dropped from 97% to 85% in 24 hours. Show failing rules, affected row counts, and what upstream change could explain it. Provide verification steps.”
Expected output:
- Failing rules table
- Pattern analysis
- Verification checks
- Prevention recommendation
Scenario D — Business Asks “Can I trust the dashboard?”
Prompt: “For the executive revenue dashboard, confirm whether data is reliable today. If not, explain impact and recommended decision guidance in plain language.”
Expected output:
- Trust status (green/yellow/red)
- What is impacted
- Recommended actions / workarounds
- Links to evidence (where supported)
Common Pitfalls and Fixes
Pitfall: Unclear Ownership
Fix: Ask ADM to suggest owner teams based on domain/asset tags (or your org’s structure).
Pitfall: Too Much Detail for Stakeholders
Fix: Ask for “exec summary + appendix,” where appendix contains technical details.
Pitfall: Too Many One-off Conversations
Fix: Create a workflow + notebook template for recurring reporting.
Checklist: “Good ADM request”
- Asset(s) named (tables, domains, dashboards)
- Time window defined
- Goal stated (investigate / summarize / decide)
- Output format specified (bullets/table)
- Ask for evidence/citations where appropriate
- Ask for next steps + verification checks