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Best Practices Playbook

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