Predict Problems Before They Happen: Predictive Analytics Playbook

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1. <section> <h2>Welcome — Predict problems before they happen</h2> <p>Predictive analytics can turn recurring surprises into predictable, manageable work. This playbook helps teams frame realistic use cases, prepare trustworthy data, choose simple models that people can act on, validate results responsibly, deploy with human oversight, and monitor performance so predictions remain useful over time.</p> <h3>What you can expect</h3> <p>Use this resource to move from curiosity to a safe, low-friction pilot that creates measurable value. You should be able to:</p> <ul> <li>Capture a clear, action-oriented use case (who acts, what decision changes, and why it matters).</li> <li>Check whether your data supports the use case and avoid common pitfalls like data leakage.</li> <li>Start with simple, interpretable models and compare them to a baseline rule or heuristic.</li> <li>Validate results with business-aligned metrics, backtests, and holdouts.</li> <li>Deploy a pilot with human-in-the-loop controls, alerts, and a plan for monitoring and retraining.</li> </ul> <h3>How to get value quickly</h3> <ol> <li>Begin with the Use Case Framing tool to capture the decision and measurable outcomes.</li> <li>Run the Data Readiness Checklist to assess whether data and labels are reliable.</li> <li>Choose a simple model and validate it using the Validation & Testing guidance.</li> <li>Use the Deployment Checklist to stage a pilot with human oversight and clear alerts.</li> <li>Set up the Monitoring Tracker to detect drift, performance drops, or false-alarm fatigue.</li> </ol> <h3>Who this helps</h3> <p>This playbook is useful for analysts, team leads, product owners, managers, operations teams, and small- to mid-sized organizations that want to add predictive capabilities without creating opaque systems. Examples include maintenance teams predicting equipment faults, customer success teams anticipating churn, and clinics triaging patients for follow-up. The focus is practical: reduce surprise, make better, faster decisions, and keep humans in charge.</p> <h3>How this resource is organized</h3> <p>Each item is standalone so you can run exercises, use forms, or read deep dives in the order that fits your team. The Use Case Framing tool and the Data Readiness Checklist are high-value starting points for most teams.</p> </section>
2. { "FormType": "InteractiveForm", "Title": "Predictive Use Case Framing", "IntroductionHtml": "<p>Capture a concise, action-oriented description of a predictive problem your team can realistically pilot. Clear answers here make design, validation, and deployment far easier.</p>", "SubmitLabel": "Save Use Case", "SuccessMessage": "Use case saved. Next: run the Data Readiness Checklist and assign an owner to the pilot.", "DataType": "UseCaseDefinition", "SchemaVersion": 1, "Fields": [ { "Key": "use_case_name", "Label": "Short name for this use case", "HelpText": "Give a concise name (e.g., 'Overdue-payment churn alert').", "FieldType": "text", "IsRequired": true, "Placeholder": "Overdue-payment churn alert", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "decision_owner", "Label": "Who makes the decision based on the prediction?", "HelpText": "Name a role or person who will act (not the model owner).", "FieldType": "text", "IsRequired": true, "Placeholder": "Customer success manager / Line supervisor", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "primary_action", "Label": "What specific action will the decision owner take when the model flags a case?", "HelpText": "Be concrete (e.g., 'schedule preventive maintenance within 48 hours', 'start a retention call').", "FieldType": "textarea", "IsRequired": true, "Placeholder": "Schedule a service visit", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "decision_timeframe", "Label": "How quickly does the action need to happen after the prediction?", "HelpText": "Choose the cadence that matters for the decision.", "FieldType": "select", "IsRequired": true, "Placeholder": "", "Minimum": null, "Maximum": null, "Options": [ { "Value": "real_time", "Label": "Real-time (seconds/minutes)" }, { "Value": "hourly", "Label": "Hourly" }, { "Value": "daily", "Label": "Daily" }, { "Value": "weekly", "Label": "Weekly" }, { "Value": "monthly", "Label": "Monthly or slower" } ] }, { "Key": "expected_benefit", "Label": "Primary expected benefit / outcome", "HelpText": "Describe the measurable outcome you expect (reduced downtime, fewer missed renewals, lower cost per case).", "FieldType": "textarea", "IsRequired": true, "Placeholder": "Reduce unplanned downtime by 20%", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "current_baseline_or_rule", "Label": "Current decision rule or baseline you will compare against", "HelpText": "Describe how the decision is made today (manual checks, thresholds, heuristics).", "FieldType": "textarea", "IsRequired": true, "Placeholder": "If > 3 missed payments, escalate to collections", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "data_availability", "Label": "Data you currently have available for this use case", "HelpText": "Select all that apply. This helps estimate readiness.", "FieldType": "checkbox", "IsRequired": true, "Placeholder": "", "Minimum": null, "Maximum": null, "Options": [ { "Value": "historical_outcomes", "Label": "Historical labeled outcomes (what you want to predict)" }, { "Value": "timestamps", "Label": "Reliable event timestamps" }, { "Value": "unique_ids", "Label": "Stable unique identifiers for entities (customers, machines)" }, { "Value": "features", "Label": "Operational or behavioral features (logs, usage, sensor readings)" }, { "Value": "external_data", "Label": "Relevant external data (weather, market indicators)" }, { "Value": "no_data", "Label": "No historical labels yet (intent to run forward)" } ] }, { "Key": "privacy_or_regulatory_flags", "Label": "Any privacy, regulatory, or ethical constraints we should know?", "HelpText": "E.g., PHI, GDPR-sensitive data, or requirements for explanations.", "FieldType": "textarea", "IsRequired": false, "Placeholder": "Contains customer health data, requires consent", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "success_metrics", "Label": "How will you measure success? (list 1–3 metrics)", "HelpText": "Prefer measurable KPIs linked to action (e.g., % reduction in incidents, precision at top-k).", "FieldType": "textarea", "IsRequired": true, "Placeholder": "Precision@10 = 0.8; reduction in weekly incidents", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "estimated_frequency", "Label": "Estimated number of predictions per timeframe", "HelpText": "Give a rough volume (per day/week/month) to size operations and alerting.", "FieldType": "text", "IsRequired": false, "Placeholder": "~200/day", "Minimum": null, "Maximum": null, "Options": [] }, { "Key": "stakeholders", "Label": "Key stakeholders to involve", "HelpText": "List names, roles, or teams to include in reviews and pilots.", "FieldType": "textarea", "IsRequired": false, "Placeholder": "Ops lead; data engineer; legal", "Minimum": null, "Maximum": null, "Options": [] } ] }