Chat with Your Data: Why Most Analytics Vendors Backed Away

Between 2016 and 2022, nearly every major analytics vendor launched natural language query features. Users could type questions and get answers - "chat with your data" had arrived. By 2024, most of these features had been quietly deprecated or relegated to demo footnotes.

Understanding why reveals the fundamental requirements for conversational analytics that actually works in production.

The Promise and the Reality

What Vendors Promised

The marketing was compelling:

• "Ask questions in plain English"
• "No SQL required"
• "Anyone can analyze data"
• "Insights in seconds"

Demos showed impressive results: type a question, get a chart. Simple, accessible, transformative.

What Users Experienced

Production reality was different:

• Questions were often misinterpreted
• Results frequently incorrect
• Complex questions failed completely
• Users lost trust and stopped trying

The gap between demo and production destroyed adoption.

Why Early Implementations Failed

Failure 1: No Business Context

Early systems tried to translate natural language directly to SQL:

User: "What was revenue last quarter?"
     ↓
AI interprets "revenue" and "last quarter"
     ↓
Generates SQL against database
     ↓
Returns result

The problem: "revenue" could mean different things:

• Booked revenue
• Recognized revenue
• Recurring revenue
• Gross revenue
• Net revenue

Without business context, AI guessed - and often guessed wrong.

Failure 2: Schema Complexity

Real databases are complicated:

• Multiple tables that could contain "revenue"
• Complex joins to get complete pictures
• Historical tables versus current tables
• Data quality issues in some sources

AI systems needed to navigate this complexity without guidance.

Failure 3: Ambiguous Questions

Natural language is inherently ambiguous:

"How many customers do we have?"

• All customers ever?
• Active customers?
• Paying customers?
• Customers in a specific region?

Early systems made assumptions. Assumptions were often wrong.

Failure 4: Insufficient Error Handling

When systems were uncertain, they often:

• Generated plausible-looking wrong answers
• Did not indicate confidence level
• Could not explain their reasoning
• Provided no path to verification

Wrong answers presented confidently destroyed trust.

Failure 5: Narrow Capabilities

Even when correct, capabilities were limited:

• Simple aggregations worked
• Multi-step analysis failed
• Follow-up questions lost context
• Complex business logic was impossible

Users quickly hit walls.

The Trust Destruction Cycle

Early chat-with-data features followed a predictable pattern:

Launch with excitement
         ↓
Users try it
         ↓
Errors occur
         ↓
Users verify manually (extra work)
         ↓
More errors found
         ↓
Trust erodes
         ↓
Users stop trying
         ↓
Feature usage drops
         ↓
Vendor deprioritizes
         ↓
Feature deprecated

This cycle played out across the industry.

What Actually Works

The Semantic Layer Foundation

Successful conversational analytics requires semantic layers:

User: "What was revenue last quarter?"
         ↓
AI interprets question
         ↓
Matches to semantic layer metric "quarterly_revenue"
         ↓
Uses certified definition and calculation
         ↓
Queries through governed path
         ↓
Returns accurate result

The semantic layer eliminates guessing about what "revenue" means.

Context Engineering

Beyond semantic layers, AI needs rich context:

• How are metrics typically used?
• What are common follow-up questions?
• What comparisons are meaningful?
• What caveats should be mentioned?

This context engineering enables appropriate responses.

Bounded Scope

Successful systems know their limits:

• Clear about what questions can be answered
• Graceful handling of out-of-scope requests
• Transparency about confidence levels
• Paths to human assistance when needed

Saying "I don't know" is better than being wrong.

Explanation and Verification

Trust requires transparency:

• Show how answers were calculated
• Indicate which metric definitions were used
• Enable verification against known sources
• Provide confidence indicators

Users can trust what they can verify.

The Modern Approach

Architecture That Works

┌─────────────────────────────────────────────────┐
│              Natural Language Input              │
└────────────────────────┬────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────┐
│           Intent Understanding Layer             │
│  - Question interpretation                       │
│  - Terminology mapping                          │
│  - Ambiguity resolution                         │
└────────────────────────┬────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────┐
│              Semantic Layer                      │
│  - Metric definitions                           │
│  - Relationship knowledge                       │
│  - Business rules                               │
│  - Governance constraints                       │
└────────────────────────┬────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────┐
│              Query Execution                     │
│  - Validated query generation                   │
│  - Access control enforcement                   │
│  - Result validation                            │
└────────────────────────┬────────────────────────┘
                         │
                         ▼
┌─────────────────────────────────────────────────┐
│              Response Generation                 │
│  - Clear answer presentation                    │
│  - Calculation explanation                      │
│  - Confidence indication                        │
│  - Verification path                            │
└─────────────────────────────────────────────────┘

Each layer addresses failure modes of early implementations.

Key Differentiators

Modern conversational analytics platforms like Codd AI differ from early attempts:

Early Attempts	Modern Approach
Direct NL-to-SQL	Semantic layer mediated
No business context	Rich context engineering
Unlimited scope	Bounded, known capabilities
Confident wrong answers	Transparency and explanation
Generic technology	Purpose-built for analytics

The architecture fundamentally differs.

Lessons for Organizations

Lesson 1: Beware Impressive Demos

Demos are curated. Production is messy.

Ask vendors:

• What is production accuracy across all query types?
• How does the system handle ambiguity?
• What happens when it doesn't know?
• Can you show results with our data, not demo data?

Lesson 2: Semantic Layers Are Essential

Without business context, AI guesses. Guessing is not acceptable for business decisions.

Requirements:

• Certified metric definitions
• Documented relationships
• Business rule encoding
• Governance integration

Lesson 3: Expect Investment

Conversational analytics is not plug-and-play:

• Semantic layer must be built or adapted
• Business knowledge must be captured
• Users must be trained
• Accuracy must be monitored

Plan for the investment required.

Lesson 4: Start Narrow

Begin with focused scope:

• Specific domain where definitions are clear
• Well-understood metrics
• Engaged users who will provide feedback
• Room to learn and iterate

Expand as accuracy is proven.

The Path Forward

The failures of early chat-with-data features were not signs that conversational analytics is impossible. They were lessons about what the technology requires:

• Semantic foundations, not just AI capabilities
• Context engineering, not just natural language processing
• Bounded scope, not unlimited ambition
• Transparency, not confident guessing

Organizations that learn from these failures can implement conversational analytics successfully. Platforms like Codd AI have incorporated these lessons into their architecture.

The question is not whether chat-with-data can work - it can. The question is whether organizations are willing to build the foundations that make it work.