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Databricks Apps Adoption Playbook

Field vs Product framework for driving adoption

Use Case Tracking

Owner: Product Management / Analytics

Purpose

Track actual use cases in the wild to:

  • Validate archetypes (Cockpit, Vertical, Horizontal)
  • Identify emerging patterns not yet classified
  • Prioritize product investment based on real usage
  • Inform GTM messaging with customer evidence

Use Case Classification Framework

Primary Archetypes

Archetype Definition Key Signals
Business Cockpit Executive/business visibility app Dashboard, analytics, predictions
Deep Vertical Industry-specific solution Domain expertise, proprietary data
Horizontal Platform productivity tool Data quality, catalog, self-serve

Secondary Attributes

Attribute Options
Traffic Pattern Always-on / Burst / Periodic
User Type Executive / Analyst / Developer / Business
AI Integration None / Predictive / Generative / Agentic
Data Source Lakehouse-native / External / Hybrid
Exposure Internal / Partner / Public

Tracking Mechanism

Data Sources

Source What It Captures
Product Telemetry App creation, usage, features used
SFDC Opportunity Deal context, customer, value
Field Signal Log Qualitative insights, blockers
PS Engagements Deep implementation details

Classification Process

flowchart LR
    subgraph Capture["1. CAPTURE"]
        App["New App Created"]
        Deal["Deal Closed"]
        PS["PS Engagement"]
    end
    
    subgraph Classify["2. CLASSIFY"]
        Arch["Assign Archetype"]
        Attr["Add Attributes"]
    end
    
    subgraph Analyze["3. ANALYZE"]
        Pattern["Pattern Recognition"]
        Validate["Archetype Validation"]
    end
    
    Capture --> Classify --> Analyze

Use Case Registry

Registry Template

Field Description
Use Case ID Unique identifier
Customer Account name
Industry Vertical
Archetype Cockpit / Vertical / Horizontal
Description What the app does
AI Integration Type of AI used
Traffic Pattern Usage characteristics
Databricks Features Unity Catalog, Lakebase, etc.
Business Value Quantified impact
Status POC / Production / Churned

Sample Registry

ID Customer Industry Archetype Description AI Status
UC001 [TBD] Retail Cockpit Inventory visibility dashboard Predictive TBD
UC002 [TBD] FSI Vertical Fraud detection app ML TBD
UC003 [TBD] DN Horizontal Data quality tool None TBD

Archetype Validation

Hypothesis to Test

H4: Three App Archetypes (Cockpit, Vertical, Horizontal) drive 80% of successful adoption.

Validation Criteria

Metric Source Target
% of apps classifiable into 3 archetypes Registry 80%+
Revenue distribution by archetype SFDC Measurable split
Win rate by archetype SFDC Comparable or better
Retention by archetype Telemetry Track patterns

Quarterly Review

Quarter Total Apps Cockpit Vertical Horizontal Other % in 3 Archetypes
Q1 FY26 TBD TBD TBD TBD TBD TBD

Emerging Pattern Detection

Signals for New Archetypes

Signal Action
Cluster of “Other” use cases Analyze for common pattern
Repeated field feedback Investigate as potential archetype
Competitive loss pattern Check if new archetype needed

New Pattern Template

Field Description
Pattern Name Proposed archetype name
Definition What characterizes this pattern
Example Use Cases 3+ examples
Customer Profile Who builds these
Databricks Fit How we differentiate
Recommendation Add as archetype? Merge? Ignore?

Actions for Product Management

Action Purpose Priority
Establish use case registry Systematic tracking High
Tag top 20 customers by archetype Initial classification High
Build quarterly review cadence Ongoing validation Medium
Create telemetry for archetype signals Automated classification Medium

Success Metrics

Metric Baseline Target (6 mo)
Use cases in registry 0 50+
Classification accuracy N/A 90%+
Archetype validation (H4) Untested Validated/Invalidated
New patterns identified 0 1-2

Last Updated: January 2026

Related: Sales Plays Hypotheses H4