Stop Building Reports. Start Making Decisions.
Data Analytics Platforms That Actually Get Used.
Your analysts spend 80% of their time finding and cleaning data, not analyzing it. Your executives don't trust the numbers. Different teams use different spreadsheets. We build production data platforms that unify your data, automate your pipelines, and enable self-service analytics—so your team can finally focus on insights, not infrastructure.
You Have More Data Than Ever. And Less Insight.
The promise of data-driven decision making has become a data-driven frustration. Every system generates data. Every tool has a dashboard. But when leadership asks a simple question—"How are we actually performing?"—nobody has a consistent answer.
The Data Prep Death Spiral
Your analysts are supposed to uncover insights. Instead, they spend 80% of their time wrangling data from different sources, fixing inconsistencies, and reconciling numbers that don't match. By the time the report is ready, the decision has already been made.
The Single Source of Lies
You were promised a "single source of truth." What you got was multiple databases, conflicting definitions, and endless debates about whose numbers are right. When executives can't trust the data, they fall back on gut instinct.
The BI Tool Graveyard
You've invested in Power BI, Tableau, or Looker. But adoption is low because getting data into these tools requires IT tickets, custom development, and weeks of waiting. Users default to Excel exports and email attachments.
The Governance Gap
Who has access to customer data? Where does that revenue number come from? Can you prove compliance in an audit? Without governance, you're one bad actor away from a data breach and one regulator away from a fine.
The AI Readiness Crisis
Everyone wants to use AI and machine learning. But AI is only as good as the data it's trained on. Fragmented, ungoverned, inconsistent data means AI projects fail before they start.
What is a Data Analytics Platform?
A data analytics platform is the integrated infrastructure that transforms raw data from across your organization into trusted, accessible insights. It's not a single tool—it's an ecosystem of connected components that work together to collect, store, transform, govern, and visualize your data.
Think of it as your organization's central nervous system for information: data flows in from all your systems, gets cleaned and organized automatically, and becomes available for anyone who needs it—in dashboards, reports, or AI applications. Unlike legacy approaches where every report requires custom development, a modern data platform enables self-service: business users can explore data, build their own visualizations, and answer their own questions without waiting for IT.
Data Integration & Pipelines
Automated extraction from 100+ sources—databases, APIs, SaaS applications, spreadsheets. Data flows continuously without manual intervention.
Cloud Data Warehouse
Central repository for all analytical data. Modern cloud warehouses provide unlimited scale, high performance, and pay-for-what-you-use economics.
Data Transformation
Raw data transformed into analysis-ready datasets. Business logic encoded once, applied consistently. No more manual spreadsheet formulas.
Data Governance
Catalog of all data assets with clear ownership. Data lineage showing where every number comes from. Access controls ensuring right people see right data.
Business Intelligence
Self-service dashboards and reports. Interactive exploration without SQL knowledge required. Embedded analytics in your applications.
Semantic Layer
Business-friendly definitions on top of technical data. "Revenue" means the same thing to everyone. Metrics calculated consistently everywhere.
How We Build Data Analytics Platforms
We don't sell software and walk away. We implement production data platforms that integrate with your existing systems, reflect your actual business logic, and enable your team to make decisions faster—starting within weeks, not months.
Discovery & Architecture
We analyze your current data landscape, business requirements, and technical constraints. You get a complete picture of your data maturity and a concrete roadmap to production analytics.
Deliverables:
- Data source inventory and assessment
- Current state analysis
- Architecture design with technology recommendations
- Implementation roadmap
Data Foundation
We set up your cloud data warehouse, configure data ingestion pipelines, and establish the foundation for all future analytics. Data starts flowing from your critical sources.
Deliverables:
- Cloud data warehouse deployment
- Data ingestion pipelines for priority sources
- Raw data layer with historical load
- Data quality checks and monitoring
Transformation & Modeling
We build the transformation layer that turns raw data into analytics-ready datasets. Business logic gets encoded consistently. Your data model reflects how your business actually thinks.
Deliverables:
- Data transformation pipelines (dbt)
- Dimensional data model
- Automated testing for data quality
- Semantic layer with business definitions
Analytics & Launch
We build your initial dashboards, configure self-service access, and launch your data platform to users. Training ensures adoption. Documentation enables independence.
Deliverables:
- Executive dashboards
- Self-service analytics environment
- Team training (technical and business)
- Operations runbook and documentation
Discovery & Architecture
We analyze your current data landscape, business requirements, and technical constraints. You get a complete picture of your data maturity and a concrete roadmap to production analytics.
- Data source inventory and assessment
- Architecture design with technology recommendations
- Implementation roadmap
Data Foundation
We set up your cloud data warehouse, configure data ingestion pipelines, and establish the foundation for all future analytics.
- Cloud data warehouse deployment
- Data ingestion pipelines for priority sources
- Data quality checks and monitoring
Transformation & Modeling
We build the transformation layer that turns raw data into analytics-ready datasets. Business logic gets encoded consistently.
- Data transformation pipelines (dbt)
- Dimensional data model
- Semantic layer with business definitions
Analytics & Launch
We build your initial dashboards, configure self-service access, and launch your data platform to users.
- Executive dashboards
- Self-service analytics environment
- Team training and operations runbook
Complete Data Platform Infrastructure
Everything you need to go from fragmented data to production analytics.
Production Data Warehouse
Your central repository for all analytical data. Cloud-native deployment (Snowflake, Databricks, BigQuery, or Redshift). Scalable, performant, and cost-optimized for your workload.
Automated Data Pipelines
Reliable data ingestion from all your sources—databases, SaaS applications, APIs, and files. Incremental updates, error handling, and monitoring included.
Transformation Framework
Modern transformation layer using dbt (industry standard). Business logic in version-controlled code. Automated testing catches issues before they reach dashboards.
Data Governance Foundation
Data catalog with documentation. Lineage tracking showing data origin. Access controls and audit logging. Quality monitoring with alerting.
Business Intelligence Environment
Self-service dashboards and reporting. Interactive exploration for business users. Embedded analytics options. Scheduled reports and alerts.
Semantic Layer
Business-friendly definitions and metrics. Consistent calculations across all reports. Single definition of "revenue," "customer," and other critical terms.
Documentation & Training
Complete technical documentation. Training for technical team and business users. Runbook for operations and troubleshooting.
How Teams Use Data Analytics Platforms
See how different functions across your organization leverage unified data infrastructure.
Executive & Strategic
Sales & Revenue
Marketing & Growth
Product & Engineering
Finance & Operations
Customer Success
Who Benefits from Data Analytics Platforms
We work with organizations at every stage of their data journey—from startups building their first analytics to enterprises modernizing legacy infrastructure.
Startups & Scale-ups
Growing fast, drowning in data
Investors want metrics. Product needs analytics. But you don't have a data team, and you can't distract engineers from product development.
Our Approach
Right-sized data foundation that scales with you. Start with essential metrics and expand as you grow. Cloud-native economics.
Product-Led Growth Companies
Data should drive every feature decision
Your product generates enormous behavioral data. User analytics should drive decisions. But connecting product data to business outcomes requires infrastructure.
Our Approach
Product analytics foundations that connect user behavior to revenue. Event tracking strategy, behavioral models, and self-service tools for PMs.
Enterprise Data Teams
Buried in requests, not insights
You have data people, but they're drowning in ad-hoc requests. Every report is custom work. Self-service never materialized. Modernization seems overwhelming.
Our Approach
Modernize without disruption. Migrate from legacy to cloud. Build self-service that actually works. Enable your team for high-value work.
Business Analysts
Spending 80% on prep, 20% on analysis
You're supposed to analyze data and find insights. Instead, you spend most of your time in Excel, reconciling numbers that don't match.
Our Approach
Analyst-friendly infrastructure. Data that's clean when you access it. Tools that let you explore without SQL. Your time back for actual analysis.
Data-Aspiring Organizations
Know data matters, struggling to get there
You know data should be a competitive advantage. You have tools but low adoption. Reports exist but decisions still happen by instinct.
Our Approach
Start with business outcomes, not technology. Build infrastructure for specific high-value decisions. Demonstrate value quickly, then expand.
Modern Data Stack Implementation
We implement using modern, cloud-native tools that have become industry standards. No proprietary technology. No vendor lock-in. Your team can operate, extend, and evolve the platform after we leave.
Data Ingestion & Integration
Managed ingestion for standard sources, custom development for unique integrations. Incremental updates, not full refreshes.
Cloud Data Warehouse
We recommend based on your cloud presence, team skills, workload, and budget. No single right answer—fit matters.
Data Transformation
Industry standard. Version-controlled transformation code. Modular, tested, documented. Business logic encoded once.
Data Orchestration
Automated scheduling, dependency management, and monitoring. Pipelines run reliably without manual intervention.
Business Intelligence
Based on your existing investments, user needs, and embedding requirements. We implement what works for your team.
Data Governance & Quality
Start with essential governance. Automated quality testing. Monitoring and alerting for production issues.
Technology-Agnostic by Design
We don't have preferred vendors or referral agreements. Our recommendations are based purely on fit for your organization—your existing infrastructure, team capabilities, workload characteristics, and budget constraints. The tools listed above represent the current state of the modern data stack, but we evaluate based on your specific context.
Data Governance: The Foundation Your AI Strategy Requires
Governance isn't bureaucracy—it's trust. When business users trust their data, they use it. When executives trust the numbers, they make decisions. When regulators trust your controls, you avoid fines. Governance creates the foundation for everything data enables.
Data Catalog & Discovery
Every data asset documented and searchable. Business users find data without asking IT. Technical metadata and business context combined. Data discovery takes minutes, not weeks.
Data Lineage
Trace any metric back to its source. Understand what transformations occurred. Know which reports are affected when source data changes. Answer "where does this number come from?" instantly.
Data Quality Management
Quality rules that run automatically. Alerts when data violates expectations. Trending to identify degradation. Problems fixed at the source before they impact decisions.
Access Control & Security
Right people see right data. Role-based access at table and row level. Sensitive data masked or restricted. Audit trail of who accessed what. Compliance-ready from day one.
Business Glossary
One definition for each business term. "Customer" means the same thing everywhere. "Revenue" calculated consistently. No more arguments about whose number is right.
Why 65% of Data Leaders Prioritize Governance
AI initiatives fail without governed data. Self-service analytics collapse without trust. Data teams drown in "is this number right?" questions. Governance isn't overhead—it's the multiplier that makes every other data investment pay off.
Self-Service Analytics That Actually Works
Self-service sounds great until you try it. Users get overwhelmed by complexity. They don't trust the data. They can't find what they need. Real self-service requires more than giving users access—it requires building an environment where they can succeed.
Curated Data, Not Everything
Users don't need access to raw tables. They need curated datasets designed for analysis. Business-friendly names, clear relationships, precalculated metrics. Complexity hidden, simplicity exposed.
Semantic Layer
Business logic shouldn't live in every dashboard. Metrics defined once, used everywhere. Users explore without understanding SQL or data modeling. The platform does the hard work.
Training & Adoption
Technology without adoption is waste. We train users on the tools AND on analytical thinking. Office hours, documentation, and champions create a culture of data use.
Governance That Enables
Governance doesn't block access—it enables trust. Users know the data is accurate because governance ensures it. Exploration is encouraged within guardrails.
Investment & Engagement Options
Choose the engagement level that matches your needs and readiness.
Assessment
Data Readiness Assessment
2-3 weeks
Comprehensive analysis of your current data landscape with specific recommendations for platform implementation. Ideal before committing to full implementation.
Implementation
Foundation Platform
8-10 weeks
Complete data platform implementation for organizations with moderate complexity (5-15 data sources, single primary use case focus).
Enterprise
Enterprise Platform
12-20 weeks
Comprehensive platform for complex environments with multiple business units, extensive source systems, and advanced governance requirements.
The ROI of Getting Data Right
Organizations report 127% ROI from BI implementation over 3 years. Improved data quality alone saves 12% of revenue lost to poor data. The investment in a proper data foundation pays dividends across every business decision.
Frequently Asked Questions
Common questions about data analytics platform implementations.
Turn Your Data Into Decisions
Your data should be a competitive advantage, not a source of frustration. When business users can answer their own questions, when executives trust the numbers, when analysts focus on insights instead of data prep—that's when data becomes valuable.
Start with an assessment. We'll analyze your current data landscape, identify high-value opportunities, and show you exactly what a production data platform can deliver for your organization. Clear roadmap. Specific recommendations. No commitment to implementation.
At a Glance
Industry Deployment Patterns
How different industries leverage Data & Analytics platforms for operational intelligence.
Manufacturing
Production KPI dashboards with OEE & yield tracking
Real-time equipment health monitoring with downtime root cause analysis and predictive maintenance alerts integrated with EAM systems
Transportation & Rail
Track health GIS with maintenance priority heat maps
Visual inspection data overlaid on GIS with automated work order generation for high-priority segments based on condition scores
Warehousing & Logistics
Real-time inventory flow with geofence alerts
Package movement tracking across facilities with dwell-time anomalies and capacity utilization dashboards for operational optimization
Energy & Utilities
Asset health dashboards with outage prediction
Grid asset monitoring with failure prediction models, outage impact zones, and crew dispatch optimization via GIS routing
Architecture Decision Guide
Choosing the right data platform architecture for your analytics requirements.
| Approach | When to Use | Tradeoffs | Best For |
|---|---|---|---|
| Cloud-Native (Snowflake/BigQuery) | Need scalability, managed services, ML integration, multi-region | Best performance and features, cloud costs, requires connectivity | Enterprises with cloud-first strategy, high data volumes |
| Hybrid (Cloud + On-Prem) | Regulatory constraints, sensitive data on-prem, cloud analytics | Balanced compliance and capabilities, moderate complexity | Healthcare, finance, government with data residency requirements |
| On-Prem Only | Air-gapped environments, full data sovereignty required | Complete control, higher ops burden, limited scalability | Defense, critical infrastructure, strict compliance environments |
Timeline & Team
Typical Timeline
3–6 weeks
From kickoff to acceptance testing
Delivery Team
Inputs We Need
To ensure a successful implementation, we'll need the following information from your team.
- Data sources and existing workbooks to replace
- KPI definitions and data stewardship owners
- Data quality target tolerances and thresholds
- Security roles and SSO configuration scopes
- Reporting cadence and target audiences
- GIS layer requirements and spatial data sources
KPI Catalog: Example Metrics
Every KPI includes ownership, clear definitions, calculation formulas, and refresh schedules for accountability and consistency.
| KPI Name | Owner | Definition | Formula | Refresh |
|---|---|---|---|---|
| On-Time Delivery Rate | Operations VP | Percentage of deliveries completed within promised window | (Delivered on time / Total deliveries) × 100 | Hourly |
| Inventory Turnover | Warehouse Manager | Rate at which inventory is sold and replaced over a period | Cost of Goods Sold / Average Inventory Value | Daily at 6 AM |
| Asset Utilization | Fleet Director | Percentage of time assets are actively generating revenue | (Active hours / Available hours) × 100 | Every 15 minutes |
Data Quality Validation
Automated validation rules catch issues before they impact dashboards.
Supported Data Sources
Pre-built connectors for common enterprise and GIS systems.
Procurement & RFP Readiness
Common requirements for Data & Analytics platform vendor evaluation and compliance.
Need vendor compliance docs? Visit Trust Center →
Outcomes
See the math →- •Live KPI dashboards with GIS layers and drill-down capabilities
- •Data quality pipeline with automated alerts on critical field failures
- •Scheduled narrative reports with anomaly detection and trend analysis
- •Measurable reduction in report prep time (typically 75%)
- •Self-service analytics with governed data catalog
What You Get (Acceptance Criteria)
Our standards →Industry Benchmarks & Performance
Representative performance metrics from typical Data & Analytics platform deployments.*
*Representative industry examples based on typical deployments. Actual results vary by use case, data quality, infrastructure configuration, and deployment environment. See our methodology →
Technology Stack & Integration Compatibility
Proven integration stack across data warehouses, GIS platforms, BI tools, and data quality frameworks.
Data Warehouses
GIS Platforms
BI & Visualization
Data Quality & Transformation
ETL/ELT & Orchestration
Export Formats
Timeline
3–6 weeks
Team
Data EngineerFrontend Dashboard DeveloperData AnalystProject Manager
Inputs We Need
- •Data sources and existing workbooks to replace
- •KPI definitions and data stewardship owners
- •Data quality target tolerances and thresholds
- •Security roles and SSO configuration scopes
- •Reporting cadence and target audiences
- •GIS layer requirements and spatial data sources
Tech & Deployment
Data Warehouses: Snowflake, BigQuery, Redshift, Azure Synapse, Databricks. GIS: ArcGIS Enterprise/Online, QGIS, Mapbox, Google Maps Platform, PostGIS. BI Tools: Tableau, Power BI, Looker, Metabase, custom React dashboards with Recharts/D3. Data Quality: Great Expectations, dbt tests, custom Python validators, Soda. ETL/ELT: Fivetran, Airbyte, dbt, custom Airflow/Prefect DAGs. Orchestration: Airflow, Prefect, dbt Cloud. Integration: REST/GraphQL APIs, CDC (Debezium), message queues (Kafka, RabbitMQ). Observability: Prometheus, Grafana, Monte Carlo, elementary. Export formats: CSV, Excel, PDF, Shapefiles, GeoJSON, ArcGIS Feature Service.
Proof We Show
Full evidence list →Frequently Asked Questions
Need More Capabilities?
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Book a free 30-minute scoping call with a solution architect.
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