Evaluation Process

This guide outlines a suggested 4-week evaluation process designed to help you evaluate Honeydew’s capabilities and demonstrate its value within your organization. The evaluation process is structured to progressively build your understanding and showcase Honeydew’s capabilities through hands-on experience.

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Overview

This suggested 4-week evaluation process is designed as a template, to help you:

• Explore whether Honeydew fits your specific use cases
• Define measurable value through concrete business scenarios
• Build up internal expertise and confidence in the platform
• Lay the groundwork for broader organizational adoption

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Prerequisites: Business Use Case Definition

Before beginning the evaluation process, it is recommended to identify a clear business use case for testing Honeydew. This ensures your assessment is focused and meaningful. There are three common approaches to selecting a use case:

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Re-implement Existing Dashboard

Recommended for: Organizations with existing analytics infrastructure Benefits:

• Direct comparison of numbers and behavior
• Validation that Honeydew produces consistent results
• Reduced risk through familiar scenarios

Process:

1. Select an existing dashboard with well-understood metrics
2. Document the current data model and calculations
3. Identify the underlying data sources and transformations
4. Define success criteria (performance, accuracy, ease of use)

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Implement New Use Case

Recommended for: Organizations exploring new analytics capabilities Benefits:

• Demonstrates ease of solving new business problems
• Shows Honeydew’s flexibility and speed of implementation
• Provides fresh perspective on analytics workflows

Process:

1. Identify a business question that lacks current analytics support
2. Define the required metrics and dimensions
3. Map available data sources
4. Establish success criteria for the new capability

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Migrate from Existing Semantic Model

Recommended for: Organizations with existing semantic layer solutions Benefits:

• Accelerated implementation using automated migration tools
• Preserves existing business logic and definitions
• Reduces risk through guided conversion process
• Demonstrates Honeydew’s ability to replace existing solutions

Process:

1. Document your current semantic model structure and definitions
2. Identify key entities, metrics, and business rules to migrate
3. Work with Honeydew’s onboarding automation tools for conversion
4. Validate migrated model accuracy and completeness
5. Test performance improvements and new capabilities

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Week 1: Foundation

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Days 1-3: Initial Setup

Complete the core Honeydew setup following the Initial Setup guide:

1. Honeydew Application Setup
   • Org provisioning and admin user creation
   • Network access configuration (if required)
   • User authentication setup (and SSO setup - if required)
2. Git Integration
   • Set up version control for semantic definitions
   • Choose between Honeydew-managed or self-hosted repository
3. Snowflake Integration
   • Configure Snowflake access and permissions
   • Test basic connectivity
   • Install Snowflake Native Application
4. Team Onboarding
   • Consider inviting key team members
   • It is recommended to schedule a training workshop session with Honeydew, to accelerate onboarding
   • It can be helpful setting up a dedicated support channels (Slack/Microsoft Teams) with Honeydew team
   • Consider scheduling regular check-ins or office hours with Honeydew team

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Success Criteria for Week 1

• Honeydew application accessible to all team members
• Snowflake integration functional
• Git repository configured and accessible
• Team members can log in and navigate the interface
• Support channels established

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Weeks 1-2: Core Modeling

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Days 4-7: Basic Semantic Model

Build your first semantic model based on the use case defined in prerequisites:

1. Data Source Configuration
   • Connect to your Snowflake data sources
   • Validate data access and permissions
   • Review data quality and structure
2. Entity Modeling
   • Define core entities (customers, products, orders, etc.)
   • Establish relationships between entities
   • Configure entity attributes and hierarchies
3. Metric Development
   • Create business KPIs as metrics
   • Implement calculated attributes
   • Test metric calculations and validation using Honeydew’s Playground
4. Dataset Creation
   • Create dynamic datasets for analysis
   • Test query performance and results

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Success Criteria for Week 2

• Semantic model accurately represents business entities
• Core metrics calculate correctly
• Dynamic datasets deploy successfully
• Query performance meets expectations
• Complex business contexts (if applicable) are properly modeled

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Weeks 2-3: Integration

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Days 8-14: BI Connection and Integration

Connect Honeydew to your existing BI tools and workflows:

1. BI Tool Integration
   • Configure connection to your primary BI tool
   • Test data source connectivity
   • Validate metric and dimension availability
   • Create sample reports/dashboards
   • Test any additional BI tools as needed
2. API Testing
   • Test GraphQL API endpoints
   • Validate API responses and performance
   • Document integration patterns for your team
3. Snowflake Native App
   • Install and configure Snowflake Native App
   • Test native app endpoints
   • Test dbt integration (if applicable)
4. SQL Interface
   • Explore direct SQL interface capabilities

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Success Criteria for Week 3

• BI tool successfully connected to Honeydew
• Sample reports/dashboards created and functional
• API endpoints tested and documented
• Snowflake Native App operational
• Team comfortable with integration patterns

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Weeks 3-4: Advanced Capabilities

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Days 15-20: Optional Advanced Capabilities

Explore Honeydew’s advanced capabilities based on your organization’s needs:

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AI Integration (Optional)

• Slack/Microsoft Teams Integration: Set up the AI analyst chatbot for Slack or Microsoft Teams
• AI metadata setup: Create a domain for AI interactions. Add AI descriptions and metadata for entities and attributes when needed.
• AI Workflow: Test AI-driven data exploration and maintenance workflow

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Performance Optimization (Optional)

• Acceleration Capabilities: Implement query acceleration by defining pre-aggregates, and validate performance improvements.
• Performance Monitoring: Track and optimize query performance

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Success Criteria for Week 4

• Advanced capabilities evaluated and tested
• AI integration functional (if implemented)
• Performance optimizations implemented (if applicable)
• Pilot outcomes documented and evaluated

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Evaluation Summary and Next Steps

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Evaluation Assessment and Planning

To ensure the evaluation provides clear outcomes, we recommend documenting results across four dimensions:

1. Technical Fit
   • Benchmark Honeydew’s performance against your current approach (speed, accuracy, reliability)
   • Capture any technical challenges and how they were resolved, limitations and workarounds
2. Business Impact Assessment
   • Quantify improvements in time-to-insight (e.g., reporting cycles, query response times)
   • Track adoption indicators such as how quickly new users build confidence in the tool
   • Estimate ROI by comparing effort/cost saved with current workflows
3. User & Team Experience
   • Collect feedback from all evaluation participants
   • Note training requirements and any friction points - both will inform scale-up planning
   • Identify best practices or workflows that emerged during the evaluation period
4. Forward Planning
   • Outline a strategy for scaling Honeydew across teams or departments
   • Map out priority use cases for broader rollout
   • Define the support, training, and success metrics needed for a smooth adoption

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Success Metrics

Key metrics you may find useful during the evaluation period include:

• Technical Metrics
  • Query performance (response time, resource usage)
  • Data accuracy and consistency
  • System reliability and uptime
• Business Metrics
  • Time to create new analytics (days/weeks)
  • User adoption and engagement
  • Reduction in data inconsistencies
• User Experience Metrics
  • Learning curve and training time
  • User satisfaction scores
  • Support ticket volume and resolution time

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Support and Resources

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Dedicated Support

• Real-time Support: Dedicated Slack/Teams channel for immediate assistance
• Regular Check-ins: Weekly progress reviews with Honeydew team
• Expert Consultation: Access to Honeydew solution architects

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Training Resources

• Training Workshop: Free 1-hour session scheduled for Week 2
• Documentation: Comprehensive guides and examples
• Best Practices: Proven methodologies and patterns
• Hands-on Assistance: Help with specific modeling or integration challenges

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Community and Learning

• Example Models: Reference implementations and templates
• Use Case Library: Industry-specific examples and patterns
• Knowledge Base: Searchable documentation and FAQs

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Getting Started

Ready to begin your Honeydew evaluation? Follow these steps:

1. Contact Support: Reach out to support@honeydew.ai to initiate the evaluation process
2. Schedule Kickoff: Set up an initial meeting to align on objectives and timeline
3. Complete Prerequisites: Define your business use case and gather requirements
4. Begin Week 1: Start with the foundation setup outlined above