{
  "data_strand": {
    "workshop_meta": {
      "framework_version": "data-strand-v1.0",
      "source_templates": [
        "Data Purpose",
        "Data Domains & Entities",
        "Pipelines & Flows",
        "Storage & Architecture",
        "Access & Permissions",
        "Governance & Compliance",
        "Analytics & Insights",
        "AI & Automation",
        "Quality & Reliability",
        "Lifecycle & Retention",
        "Risks & Guardrails"
      ],
      "facilitation_notes": [
        "Run with data engineering, backend, product, marketing, and AI teams.",
        "Start by mapping real events, logs, objects, and usage telemetry.",
        "Treat this JSON as the Data OS — the backbone that every system and team relies on."
      ]
    },

    "purpose_and_role": {
      "question": "Why does this company collect and use data?",
      "answer": "Data ensures the product remains reliable, personalized and secure, enabling fast search, AI-powered assistance, performance optimization, customer insights and compliance. Data connects every strand — product behavior, UX flows, UI events, marketing attribution, and AI summarization — into one coherent operating system.",
      "objectives": [
        "Power real-time collaboration, search and AI summarization.",
        "Maintain workspace integrity, access control and security.",
        "Support product-led growth, customer insights and adoption metrics.",
        "Fuel automation through telemetry and workflow triggers."
      ]
    },

    "data_domains": {
      "question": "What are the core domains of data in the system?",
      "domains": [
        {
          "name": "Users & Identities",
          "entities": [
            "User profiles",
            "Credentials & auth tokens",
            "Permissions & roles",
            "Preferences & notification settings"
          ],
          "notes": "Tightly connected with authentication, SSO, org admin and compliance controls."
        },
        {
          "name": "Workspaces / Organizations",
          "entities": [
            "Workspace metadata",
            "Billing & plan",
            "Workspace settings",
            "Security & compliance policies"
          ],
          "notes": "Drives governance, access and cross-org collaboration."
        },
        {
          "name": "Channels & Conversations",
          "entities": [
            "Channel metadata",
            "Membership lists",
            "Messages",
            "Threads",
            "Reactions (emoji data events)",
            "Pinned items"
          ],
          "notes": "Primary collaboration dataset that powers search, grooming, AI summarization and compliance exports."
        },
        {
          "name": "Artifacts",
          "entities": [
            "Files",
            "Canvases",
            "Lists",
            "Task items",
            "Attached metadata (permissions, versions, references)"
          ],
          "notes": "Interlinked with messages; stored in object storage and indexed for search."
        },
        {
          "name": "Activity & Telemetry",
          "entities": [
            "UI interaction events",
            "UX flow events",
            "Feature adoption events",
            "Performance logs",
            "Search queries"
          ],
          "notes": "Feeds product analytics, PLG motions, UX quality metrics and AI ranking."
        },
        {
          "name": "External Integrations",
          "entities": [
            "App tokens",
            "API calls",
            "Workflow steps",
            "External channel partners",
            "Integration logs"
          ],
          "notes": "Supports platform health, audit logs, and extensibility ecosystem."
        }
      ]
    },

    "data_flows_and_pipelines": {
      "question": "How does data move through the system from creation to consumption?",
      "pipelines": [
        {
          "name": "Real-time Event Pipeline",
          "stages": [
            "Client events generated (UI)",
            "Ingestion gateway",
            "Streaming queue (Kafka/PubSub)",
            "Event processors",
            "Storage in time-series DB or warehouse"
          ],
          "use_cases": [
            "Live updates",
            "Presence indicators",
            "Message posting & thread updates",
            "Alerting & notifications",
            "Analytics & dashboards"
          ]
        },
        {
          "name": "Search Indexing Pipeline",
          "stages": [
            "Message stored",
            "Tokenization & normalization",
            "Embedding generation (for AI search)",
            "Indexing in search clusters",
            "Refresh & ranking adjustments"
          ],
          "use_cases": [
            "Full-text search",
            "Semantic search",
            "AI conversation summaries",
            "Knowledge retrieval"
          ]
        },
        {
          "name": "AI Summarization Pipeline",
          "stages": [
            "Conversation or artifact retrieved",
            "Preprocessing & cleaning",
            "LLM summary generation",
            "Metadata tagging",
            "Caching & revalidation"
          ],
          "use_cases": [
            "Channel summaries",
            "Thread catch-up",
            "Daily digests",
            "Decision extraction"
          ]
        },
        {
          "name": "ETL / Warehouse Sync",
          "stages": [
            "Batch or micro-batch extract",
            "Transform into analytics schemas",
            "Load into warehouse",
            "Expose through BI tools"
          ],
          "use_cases": [
            "Retention analysis",
            "Funnel metrics",
            "Enterprise reporting",
            "Billing & usage scoring"
          ]
        }
      ]
    },

    "storage_and_architecture": {
      "datastores": [
        {
          "type": "Relational DB",
          "use": "Users, orgs, channels, permissions, metadata",
          "notes": "Strong consistency required for identity and access."
        },
        {
          "type": "Object Storage",
          "use": "Files, media, canvas versions",
          "notes": "Versioning, scanning, encryption at rest."
        },
        {
          "type": "Search Clusters",
          "use": "Messages, threads, artifacts",
          "notes": "Combines keyword indexing and vector embeddings."
        },
        {
          "type": "Time-series DB",
          "use": "Metrics, telemetry, performance logs",
          "notes": "Used by SRE, reliability and product analytics teams."
        },
        {
          "type": "Data Warehouse",
          "use": "Analytics, BI, dashboards, segmentation",
          "notes": "Source of truth for user and workspace metrics."
        },
        {
          "type": "Cache / KV Store",
          "use": "Presence, recent items, hot keys, ephemeral data",
          "notes": "Supports real-time responsiveness."
        }
      ]
    },

    "access_and_permissions": {
      "question": "Who has access to what data, and how is it enforced?",
      "principles": [
        "Least privilege by default.",
        "Role-based permissions for org admins, owners and users.",
        "Data-tier separation between internal staff, customers and external partners.",
        "All access points audited."
      ],
      "permission_layers": [
        "Workspace-level permissions",
        "Channel membership",
        "Thread visibility",
        "Artifact-level permissions",
        "Admin override rules with audit documentation"
      ]
    },

    "data_governance_and_compliance": {
      "question": "How do we ensure data is secure, compliant and high-integrity?",
      "policies": [
        "Encryption in transit and at rest.",
        "Data residency options for enterprise customers.",
        "Retention settings configurable per workspace.",
        "Export tools for compliance and eDiscovery.",
        "Audit logs for all critical actions."
      ],
      "compliance_frameworks": [
        "SOC 2",
        "ISO 27001",
        "GDPR",
        "HIPAA (if applicable)",
        "FedRAMP / GovCloud (for government workspaces)"
      ]
    },

    "analytics_and_insights": {
      "question": "What metrics and insights are generated from data?",
      "product_metrics": [
        "Daily Active Users",
        "Weekly Active Channels",
        "Messages sent per user",
        "Search usage",
        "Workflow Builder usage",
        "AI summary usage"
      ],
      "experience_metrics": [
        "Task completion time",
        "Flow drop-off",
        "Latency and error rates",
        "UX friction points from telemetry"
      ],
      "business_metrics": [
        "Retention and expansion",
        "Activation milestones",
        "Seat growth",
        "External collaboration adoption"
      ],
      "marketing_metrics": [
        "Attribution data",
        "Lifecycle segmentation",
        "Campaign performance",
        "Lead → conversion pipeline"
      ]
    },

    "ai_and_automation": {
      "question": "How does data feed AI and automation systems?",
      "ai_uses": [
        "Summaries of channels, threads and canvases",
        "Semantic search embeddings",
        "Decision extraction",
        "User preference prediction",
        "Workflow suggestions"
      ],
      "automation_uses": [
        "Triggers based on message patterns",
        "Workflow Builder events",
        "Bot interactions",
        "Cross-platform signals"
      ],
      "responsible_ai_policies": [
        "AI never accesses content the user can't access.",
        "Summaries are cached and revalidated to reduce overprocessing.",
        "Models tested for hallucination reduction.",
        "User consent and visibility into AI operations."
      ]
    },

    "quality_and_reliability": {
      "dimensions": [
        "Latency (message post, render, search)",
        "Event delivery reliability",
        "Data correctness",
        "Search accuracy",
        "AI summary precision",
        "Zero data loss under scale"
      ],
      "monitoring": [
        "Real-time dashboards for ingestion and pipeline health",
        "Anomaly detection on message volume",
        "Alerting rules for indexing delays"
      ]
    },

    "data_lifecycle_and_retention": {
      "phases": [
        {
          "phase": "Creation",
          "includes": "Messages, events, files, artifacts, telemetry"
        },
        {
          "phase": "Active use",
          "includes": "Displayed in UI, threads, search, canvases"
        },
        {
          "phase": "Archival",
          "includes": "Older content stored in less costly storage tiers"
        },
        {
          "phase": "Deletion",
          "includes": "Retention-based or admin-initiated removals"
        }
      ],
      "principles": [
        "Users and admins control visibility and retention.",
        "Search respects retention windows.",
        "Deletion propagates to all indexes and caches."
      ]
    },

    "risks_and_guardrails": {
      "risks": [
        "Data overload causing slow search and degraded performance.",
        "Inaccurate or outdated search indexes creating trust issues.",
        "AI summarizing sensitive content incorrectly.",
        "Broken workflows due to missing telemetry."
      ],
      "guardrails": [
        "Strict pipeline ownership per data domain.",
        "Automated reindexing for stale content.",
        "AI summaries labeled and easily toggled off.",
        "Rate limiting on ingestion systems under overload."
      ]
    },

    "data_archetype": {
      "question": "If the data system were a role in the organization, who would it be?",
      "primary_archetype": "Archivist",
      "secondary_archetype": "Strategist",
      "rationale": "The data system remembers everything, organizes it intelligently, and provides the insight and foresight needed to make strategic decisions at scale."
    }
  }
}