# Application of Large Models and Metrics Platforms

**Keywords:** Large Models, Metrics Platform, Data Democratization, NL2MQL, Intelligent Analytics

## Executive summary

Large models and metrics platforms are essential for data intelligence. Shirley, VP at Ada.im, shared how combining LLMs with a metrics platform raises effective data usage from ~5% to 50%+ of employees. Ada.im’s NL2MQL delivers more accurate, stable queries than traditional NL2SQL, with success in retail, automotive, and finance.

## Exploring LLM applications in data analysis

Ada.im’s intelligent analytics assistant leverages LLMs to enable interactive queries, root-cause analysis, and automated reports, improving speed and accuracy.

### The power of natural language analytics

Staff and managers access data via conversation, producing analyst-grade reports to support strategic decisions.

> “Large language models are not just about technology—they're about democratizing access to insights that were previously locked away from most employees.” — Shirley

## LLMs + metrics platforms bring data to more people

### From 5% to 50%+ engagement

Expanding data access from analyst-only to organization-wide engagement.

- **Faster decisions** at the point of action
- **Better insights** by those closest to the problem
- **Reduced bottlenecks** for analytics teams
- **Competitive advantage** through broad data leverage

## NL2MQL improves accuracy with large data

### Problems with NL2SQL

Direct NL2SQL often yields inaccurate SQL, struggles with multi-table joins, and falters on advanced analytics.

### Two-step NL2MQL approach

1. **Natural language → metric semantics** mapped to a standardized catalog
2. **Metric semantics → optimized SQL** via a metric engine

NL2MQL delivers superior accuracy and resilience versus NL2SQL.

### Technical advantages

- **Accuracy:** 95%+ for complex queries
- **Consistency:** Standardized definitions
- **Speed:** Optimized execution
- **Advanced analytics:** Forecasting, attribution, simulation
- **Maintenance:** Resilient to schema changes

**Key insight:** Separating business semantics from technical implementation ensures queries keep working as schemas evolve.

## Case studies across industries

### Retail: Beverage chain

- **Automated analytics processes**
- **Improved quality** via consistent metrics
- **Real-time decisions** by store managers
- **Analyst focus shift** to strategic work

### Automotive

- **Production metrics** in real-time
- **Automated quality analysis**
- **Supply chain optimization**
- **Dealer performance management**

### Financial services

- **Regulatory reporting** with audit trails
- **Risk analytics** and portfolio monitoring
- **Customer analysis** and personalization
- **Fraud detection** and anomaly identification

- **Time savings:** 70–90% reduction
- **Decision quality:** improved via broader access
- **Employee satisfaction:** higher due to fewer barriers
- **Business impact:** faster market response

## The path forward

### Standards participation

Open collaboration and standards-based approaches for interoperability and accessible innovation.

### Continuous innovation

- **Enhanced NL understanding**
- **Expanded analytical capabilities**
- **Industry-specific intelligence**
- **Performance at scale**

## Key takeaways

- **Data democratization:** critical for competitiveness
- **LLM + metrics platform:** practical AI value
- **NL2MQL:** solves enterprise accuracy and reliability
- **Proven success:** retail, automotive, finance
- **Standards:** reduce risk and lock-in

## Quantifying business value

### Operational efficiency

- **Analyst productivity:** +70–90%
- **Report generation:** automated
- **Decision speed:** minutes instead of days

### Strategic advantages

- **Market responsiveness**
- **Competitive intelligence**
- **Innovation acceleration**

### Financial returns

- **Cost savings**
- **Revenue growth**
- **Risk reduction**

### Organizational transformation

- **Data-driven culture**
- **Higher satisfaction**
- **Broader analytical skill**

## Getting started: Journey to data democratization

### Phase 1: Assessment and planning (Weeks 1–4)

- **Current state:** access patterns, bottlenecks, impact
- **Vision:** utilization targets, use cases, success metrics, ROI

### Phase 2: Foundation building (Weeks 5–12)

- **Metrics infrastructure:** catalog, unified definitions, governance
- **LLM capabilities:** NL2MQL assistant, terminology, integration

### Phase 3: Pilot and refinement (Weeks 13–20)

- **Controlled pilot:** training, feedback, iteration
- **Measure & optimize:** usage, best practices

### Phase 4: Scale and sustain (Weeks 21+)

- **Rollout:** training, community of practice, monitoring
- **Continuous improvement:** new use cases, advanced analytics

## About Ada.im

- **Solutions:** Intelligent Analytics Assistant, Unified Metrics Platform, NL2MQL, industry modules
- **Website:** [ada.im](/content/site-root.html)
- **Contact:** [contact@ada.im](mailto:contact@ada.im)

**Topics:** #LargeModels #MetricsPlatform #DataDemocratization #NL2MQL #IntelligentAnalytics #NaturalLanguageAnalytics #BusinessIntelligence #DigitalTransformation #EnterpriseData