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

• 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

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
• Contact: contact@ada.im

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