User Feedback
The User Feedback area enables Data Administrators to review how business users are interacting with CryspIQ® and identify opportunities to improve data discovery, search results and Natural Language Query performance.
As users ask questions, search for information and interact with enterprise data, CryspIQ® captures valuable feedback that helps improve the overall user experience.
This feedback provides insight into:
- Frequently asked questions
- Search patterns
- Query success rates
- Poor search results
- Missing terminology
- Business jargon
- New organisational concepts
- User satisfaction
The User Feedback area is a key component in continuously improving the CryspIQ® semantic layer.
Overview
No two organisations use the same language.
Business users often ask questions using:
- Industry terminology
- Internal project names
- Acronyms
- Legacy system names
- Department-specific language
Over time, User Feedback helps Data Administrators understand:
- What users are trying to find
- What users cannot find
- Which terms require better definitions
- Which concepts should be added to Semantic Context
This creates a continuous improvement cycle.
Business User Question
↓
Natural Language Query
↓
Results Returned
↓
User Feedback
↓
Review by Data Administrator
↓
Update Semantic Context
↓
Improved Results
Why User Feedback Matters
Natural Language Query improves over time when organisations actively review user behaviour.
Benefits include:
- Improved query accuracy
- Better search results
- Faster user adoption
- Reduced support requests
- Improved AI responses
- Better semantic understanding
- Increased user satisfaction
User Feedback Dashboard
Navigate to:
Consume → User Feedback
The dashboard provides visibility into how users are interacting with CryspIQ®.
Typical metrics include:
- Total Questions Asked
- Successful Queries
- Queries Returning No Results
- Most Common Search Terms
- Most Accessed Datasets
- User Satisfaction Ratings
- Feedback Trends
Reviewing User Questions
The feedback screen provides visibility into the questions being asked across the organisation.
Examples:
Show me customer revenue.
Which sites have the highest operating costs?
How many active employees do we have?
Show me equipment downtime.
Reviewing these questions helps identify:
- Common business information needs
- Popular datasets
- Emerging reporting requirements
Reviewing Failed Queries
One of the most valuable features is identifying queries that did not return useful results.
Examples:
Show me contractor utilisation.
Display service backlog.
Show me fleet reliability.
Potential reasons include:
- Missing semantic definitions
- Missing business terminology
- Data access restrictions
- No matching dataset
- Ambiguous language
These situations provide opportunities to improve the platform.
Reviewing Search Behaviour
Users often search using terminology that differs from formal business definitions.
Example:
| User Search Term | Enterprise Definition |
|---|---|
| Client | Customer |
| Worker | Employee |
| Revenue | Financial Transactions |
| Asset Register | Asset Master |
By reviewing search activity, Data Administrators can identify missing synonyms and add them to the Semantic Context library.
Reviewing User Ratings
Users may provide feedback on query results.
Examples:
Positive Feedback
Result was helpful.
Found exactly what I needed.
Accurate information.
Negative Feedback
Results were incomplete.
Could not find the dataset.
Question misunderstood.
This feedback helps improve future user experiences.
Improving Semantic Context
One of the primary purposes of User Feedback is to support Semantic Context improvements.
Example:
Users repeatedly search for:
Contractor Hours
However the Enterprise Data Model stores:
Labour Utilisation
The Data Administrator can:
- Review the feedback.
- Identify the mismatch.
- Add a semantic definition.
- Add synonyms.
- Improve future search results.
Common Improvement Opportunities
Missing Synonyms
Example:
Customer
Client
Account
Industry Terminology
Example:
Asset Register
Plant Register
Equipment Register
Internal Project Names
Example:
Project Falcon
Project Horizon
Acronyms
Example:
CRM
ERP
HRIS
P&L
GL
Adding these terms improves query interpretation.
Reviewing Dataset Usage
The feedback dashboard can also help identify:
Frequently Used Data
Examples:
Revenue
Customer
Employee
Asset
Sales
Underutilised Data
Examples:
Specialised operational datasets
Technical engineering datasets
Legacy information sources
This helps Data Administrators understand adoption across the organisation.
Identifying Governance Gaps
User Feedback often highlights governance issues.
Examples include:
Missing Data Steward Information
Users cannot determine who owns a dataset.
Poor Dataset Descriptions
Users do not understand what the data represents.
Missing Security Access
Users repeatedly attempt to access datasets they cannot view.
Low Quality Data
Users lose confidence in datasets that contain unresolved quality issues.
Best Practices
Review Feedback Regularly
Recommended review cycle:
| Frequency | Activity |
|---|---|
| Weekly | Review common questions |
| Monthly | Review semantic improvements |
| Quarterly | Review adoption trends |
Look for Patterns
Focus on recurring issues rather than isolated comments.
Examples:
- Repeated search failures
- Common terminology gaps
- Frequently requested datasets
Improve Semantic Context
Continuously update:
- Definitions
- Synonyms
- Acronyms
- Industry terminology
Work With Data Stewards
Feedback often identifies opportunities to improve:
- Dataset descriptions
- Data quality
- Business definitions
Common Questions
Who can access User Feedback?
Typically Data Administrators and authorised governance users.
Does feedback change data?
No.
Feedback helps improve search, discovery and user experience but does not alter enterprise data.
Can feedback improve AI performance?
Yes.
User behaviour provides valuable insight that can improve:
- Natural Language Query
- Semantic Context
- AI Assistants
- Search Relevance
How often should feedback be reviewed?
Monthly reviews are recommended, with more frequent reviews during implementation or major platform rollouts.
Benefits of User Feedback
The User Feedback area helps organisations:
- Improve user experience
- Improve search relevance
- Improve Natural Language Query accuracy
- Enhance Semantic Context
- Increase user adoption
- Improve AI outcomes
- Support governance initiatives
- Continuously refine enterprise knowledge
Most importantly, User Feedback enables CryspIQ® to evolve alongside the organisation and better reflect how people actually use and understand their data.
Related Guides
Next Steps
- Review user questions and search behaviour.
- Identify recurring terminology.
- Analyse failed or low-confidence queries.
- Update Semantic Context definitions.
- Improve business descriptions and metadata.
- Monitor improvements over time.
The User Feedback area provides a continuous improvement mechanism that helps CryspIQ® become increasingly aligned with your organisation's language, knowledge and information needs.