You're sitting on enormous amounts of data that nobody can act on because it's in the wrong format, the wrong place, or nobody trusts it. We fix that — pipelines, warehouses, and intelligence layers that executives actually use to run the business.
Most organizations drown
in data they can't use.
Enterprise data infrastructure has grown by accretion — tools added as needed, integrations duct-taped together, no coherent architecture. The result is data chaos dressed up as a data strategy.
The average knowledge worker spends 2.5 hours per day searching for the right data. The average analytics request takes 11 days to fulfill. The average dashboard is updated once a day — at best. This is not a business intelligence problem. It's an infrastructure problem.
Six data problems we
solve permanently.
Nobody Trusts the Numbers
When every department has a different answer to "how many customers did we acquire last month?", analytics becomes politics. Decisions stall. Meetings become debates about whose spreadsheet is correct.
Data Scattered Across 175+ Tools
CRM data in Salesforce. Marketing data in HubSpot. Ops data in spreadsheets. Finance data in Sage. Each system an island. Every cross-functional report requires manual data extraction and painful reconciliation.
Analysts Buried in Data Prep
Your most expensive analytical talent spends 70-80% of their time cleaning, formatting, and joining data before they can even start the analysis. The strategic insight work happens in the last 20%.
Real-Time Data is Yesterday's News
Your morning dashboards show data from 11pm last night. By the time an executive sees the numbers, the operational window to act has closed. Legacy batch ETL was designed for a world that no longer exists.
Pipeline Fragility at Scale
One API change at a source system breaks six downstream pipelines. One schema update silently corrupts a week of historical data. Nobody knows until an executive spots the anomaly in a QBR.
Data Team is a Bottleneck
Every analytical request goes through a ticket queue. Business teams wait weeks for reports they could build themselves with the right infrastructure. The data team is overwhelmed. Business decisions are delayed.
From raw events to real decisions.
The complete modern data stack — ingestion to insight. Every component built to be reliable, maintainable, and trusted by the people who use it daily.
Data Pipeline Engineering
ETL and ELT pipelines designed for schema evolution, late arrivals, and source system failures. Built with dbt, Airbyte, Fivetran, or custom Python — whichever gives you the most control without the most maintenance.
BI Dashboards & Self-Serve Analytics
Dashboards executives actually open — designed for the decisions they need to make, not the data that happens to be available. We implement semantic layers that let business teams explore data without writing SQL.
Modern Data Warehouse Architecture
Cloud data warehouse design with proper dimensional modeling, semantic layers, data catalog, and access governance. Built once. Trusted forever. Snowflake, BigQuery, or Redshift — architected to your scale.
Advanced Analytics & ML Forecasting
Statistical and machine learning models that move your team from reporting on the past to predicting the future. Demand forecasting, customer lifetime value modeling, churn prediction, attribution analysis.
Real-Time Streaming Analytics
Event-driven analytics with sub-second latency. Kafka ingestion, stream processing with Apache Flink or Spark Streaming, real-time aggregations, and live dashboards that reflect what just happened — not last night.
Data Quality & Governance Framework
dbt tests, Great Expectations, data contracts, lineage tracking, and automated anomaly detection. When a number in a dashboard is wrong, you'll know before any executive does — and you'll know exactly where it broke.
The modern data stack
we build on.
Open standards. Best-in-class tools. No vendor lock-in where avoidable. Each layer chosen for reliability, cost efficiency, and ecosystem depth.
Where are you on the curve?
Most organizations we engage with are between Collecting and Centralizing. We move them to Governing — and beyond — systematically. Here's what each stage looks like.
Collecting
Data exists in silos but isn't centralized or trusted.
- Spreadsheets everywhere
- Conflicting numbers in meetings
- No single source of truth
- Manual data extraction for every report
Centralizing
A data warehouse exists but quality and access are inconsistent.
- Some dashboards are trusted, others not
- Data team is the bottleneck for all requests
- Schema changes break downstream pipelines
- No automated quality checks
Governing
Data quality is reliable. Business teams are self-serve.
- Automated data quality checks in CI/CD
- Business teams run their own reports
- Data catalog documents all assets
- SLAs on data freshness are monitored
Predicting
Machine learning and forecasting drive proactive decisions.
- Demand forecasting live in production
- Anomaly detection alerts before issues surface
- ML models integrated into operational systems
- Data drives real-time business actions
Real data.
Real decisions made.
A 200-store retail chain with 40,000 SKUs was managing inventory on gut instinct and weekly manual analysis. Stockouts and overstock were costing $4.2M annually. The data existed — it just wasn't usable.
We built a unified data warehouse consolidating POS, web, and supplier data. Deployed a demand forecasting ML model using three years of historical sales with seasonality, promotions, and external signals. Integrated the forecasts directly into the ERP for automated replenishment.
Forecast accuracy improved from 71% to 94% on a 90-day horizon. Stockout rate reduced by 62%. Overstock carrying costs down 28%. Analytics team reduced from 8 to 4 by automating weekly reporting.
A lending company's risk team was manually pulling data from three systems into Excel for monthly portfolio review. The process took 12 analyst-hours. By the time the report was ready, the data was 3 weeks stale.
Automated data pipeline connecting loan origination system, core banking, and credit bureau data into a Snowflake data warehouse. Built a real-time risk dashboard with cohort analysis, vintage curves, and early warning indicators updated hourly.
Monthly portfolio review now takes 45 minutes instead of 12 hours. Early warning indicators caught a delinquency spike 6 weeks before it appeared in standard reporting — enabling proactive underwriting tightening.
A multi-facility hospital network had 14 different systems generating operational data — bed utilization, patient throughput, staffing, and revenue cycle — but no unified view. Department heads made decisions with 30-day-old reports.
Designed and built a healthcare data warehouse integrating EMR (Epic), scheduling, billing, and HR data. Created an operational analytics suite with real-time bed management, staff-to-census ratios, and revenue cycle KPIs. Deployed HIPAA-compliant access controls throughout.
Hospital administrators now see real-time operational metrics. Bed utilization improved by 11% through better scheduling. Revenue cycle reporting time reduced from 4 days to same-day. Zero PHI exposure incidents in 18 months of production.
Data intelligence built for
your specific vertical.
Portfolio Risk Analytics
Real-time cohort analysis, vintage curves, and early warning systems for lending portfolios.
Regulatory Reporting Automation
Basel III, RBI, and SEBI reporting pipelines that produce compliant reports with full audit trails.
Fraud Pattern Analytics
Transaction monitoring with ML-powered anomaly detection and investigation workflows.
Customer Lifetime Value Modeling
Propensity models, churn prediction, and next-best-action analytics across customer segments.
From data chaos to
trusted intelligence.
Data Audit & Foundation (Weeks 1–4)
- Comprehensive data source inventory and quality assessment
- Data warehouse architecture design and stack selection
- Core ingestion pipelines for highest-priority sources
- Initial data model and transformation layer
Analytics Layer (Weeks 4–10)
- Business-critical dashboards for key stakeholders
- Self-serve analytics layer with semantic model
- Automated data quality checks and alerting
- Data catalog and documentation
Intelligence Layer (Weeks 8–16)
- ML forecasting and predictive analytics models
- Real-time streaming for time-sensitive use cases
- Advanced segmentation and propensity models
- Data mesh architecture for organizational scale
What data teams ask us.
How many hours per week goes to data prep?
If the answer is more than two per analyst, you have a pipeline problem. We can usually fix it within four weeks. Bring us one specific use case and we'll demonstrate it.