Conversational Analytics Readiness: A Checklist for Organizations

Conversational analytics enables users to ask questions in natural language and receive accurate, contextual answers. But successful implementation requires more than just deploying technology - it requires organizational foundations that make accurate responses possible.

This readiness checklist helps you assess your current state and identify gaps to address before or during implementation.

Data Infrastructure Readiness

Data Accessibility

Can your data be queried by analytics systems?

Required:

• Data warehouse or lakehouse with analytics data
• Query access from analytics tools
• Reasonable query performance for common questions
• Data refresh frequency that meets business needs

Helpful:

• Single source of truth for core domains
• Well-organized schemas and naming conventions
• Documentation of data sources and pipelines

Assessment questions:

• Where does your analytics data live?
• Can external tools query it?
• How fresh is the data?

Data Quality

Is your data accurate enough for analytics?

Required:

• Core data is reasonably complete and accurate
• Known data quality issues are documented
• Critical data has validation in place
• Users understand data limitations

Helpful:

• Automated data quality monitoring
• Data lineage documentation
• Regular data quality reviews

Assessment questions:

• Do you trust your data for decisions?
• Are data quality issues known and managed?
• How often do data errors cause problems?

Data Security

Are appropriate access controls in place?

Required:

• Authentication for data access
• Authorization controls for sensitive data
• Audit logging capabilities
• Compliance requirements understood

Helpful:

• Row-level security implemented
• Data classification in place
• Regular access reviews

Assessment questions:

• Who can access what data?
• How is sensitive data protected?
• What compliance requirements apply?

Metric Governance Readiness

Metric Definitions

Are your key metrics clearly defined?

Required:

• Core metrics identified
• Definitions documented somewhere
• Calculation methods specified
• Business owners assigned

Helpful:

• Centralized metric catalog
• Version history for definitions
• Formal certification process

Assessment questions:

• Can you define your top 10 metrics precisely?
• Do different teams use the same definitions?
• Where are metric definitions documented?

Metric Consistency

Do metrics mean the same thing across the organization?

Required:

• Core metrics have single definitions
• Definition conflicts are identified
• Process to resolve conflicts exists
• Key stakeholders agree on definitions

Helpful:

• Semantic layer enforcing consistency
• Governance council for disputes
• Regular definition reviews

Assessment questions:

• Does "revenue" mean the same thing to everyone?
• When definitions conflict, how is it resolved?
• How often do metric disputes occur?

Metric Coverage

Are the metrics people need available?

Required:

• High-value metrics are defined
• Common questions can be answered
• Gaps are identified and prioritized
• Process to add new metrics exists

Helpful:

• Comprehensive metric library
• Self-service metric creation
• Regular coverage assessments

Assessment questions:

• What percentage of analytics questions can current metrics answer?
• How long does it take to add a new metric?
• What metrics are most requested but unavailable?

Organizational Readiness

Leadership Support

Is there executive sponsorship for conversational analytics?

Required:

• Executive sponsor identified
• Business case understood
• Resources allocated
• Success metrics defined

Helpful:

• Cross-functional steering committee
• Clear roadmap and milestones
• Regular executive reviews

Assessment questions:

• Who is the executive sponsor?
• What resources are committed?
• How will success be measured?

User Readiness

Are users prepared to adopt conversational analytics?

Required:

• Target user groups identified
• Current analytics pain points understood
• Users willing to try new approach
• Basic analytics literacy exists

Helpful:

• Change champions identified
• Training plan developed
• Feedback mechanisms planned

Assessment questions:

• Who will use conversational analytics first?
• What problems are you solving for them?
• How will you train and support them?

Process Integration

Can conversational analytics fit into existing workflows?

Required:

• Target use cases identified
• Current workflows understood
• Integration points mapped
• Transition plan outlined

Helpful:

• Pilot program planned
• Success stories to share
• Gradual rollout strategy

Assessment questions:

• Where will users interact with conversational analytics?
• How does it fit with existing BI tools?
• What processes might change?

Technical Readiness

Integration Capability

Can conversational analytics connect to your systems?

Required:

• API or direct database connectivity available
• Authentication mechanisms compatible
• Network access possible
• Performance requirements understood

Helpful:

• Existing semantic layer to leverage
• Modern data stack components
• API-first architecture

Assessment questions:

• How will conversational analytics access your data?
• What authentication methods are required?
• Are there network or firewall considerations?

IT Support

Is IT prepared to support the implementation?

Required:

• IT stakeholder engaged
• Security review planned
• Support responsibilities clear
• Maintenance expectations set

Helpful:

• Dedicated technical resource
• Integration with existing monitoring
• Clear escalation paths

Assessment questions:

• Who in IT will support this?
• What reviews and approvals are needed?
• How will issues be handled?

Readiness Scoring

Rate each area on a scale of 1-5:

Area	Score (1-5)
Data Accessibility
Data Quality
Data Security
Metric Definitions
Metric Consistency
Metric Coverage
Leadership Support
User Readiness
Process Integration
Integration Capability
IT Support

Scoring guide:

• 1: Not started
• 2: Early progress
• 3: Foundational capability
• 4: Solid capability
• 5: Advanced capability

Interpreting results:

• 40+ total: Strong readiness - proceed with confidence
• 30-39: Moderate readiness - start with focused scope
• 20-29: Early readiness - address key gaps first
• Below 20: Foundational work needed before implementation

Addressing Gaps

High Priority Gaps

Address these before implementation:

• Data accessibility barriers
• Critical data quality issues
• Undefined core metrics
• Missing executive sponsorship

Medium Priority Gaps

Address during implementation:

• Metric consistency issues
• User training needs
• Process integration details
• Documentation gaps

Lower Priority Gaps

Address over time:

• Advanced governance capabilities
• Comprehensive metric coverage
• Full organizational rollout
• Advanced integration features

Starting Despite Gaps

Perfection is not required to begin. Codd AI and similar platforms are designed to work with organizations at various readiness levels.

Start small: Begin with a focused domain where readiness is highest Be transparent: Communicate known limitations to users Iterate quickly: Use early feedback to prioritize improvements Build foundations: Use the implementation to drive readiness improvements

The readiness assessment is not a gate - it is a planning tool. Understanding your gaps helps you implement successfully and improve systematically over time.