Your data lives in 15 tools. Your decisions are based on the one spreadsheet someone updated last Tuesday.
We build data infrastructure that actually works. Pipelines that don't break. Warehouses that pull your scattered data into one place. Dashboards people trust. Reporting that doesn't require a week of manual pulls. For companies that are tired of saying 'data-driven' while making decisions on gut feel and stale spreadsheets.
You're not lacking data. You're lacking infrastructure.
Your data is everywhere and nowhere
Sales data lives in Salesforce. Marketing is split across Google Analytics, Meta Ads, and HubSpot. Finance is on Tally or Zoho Books. Operations runs on custom spreadsheets. When the CEO asks 'how are we doing this quarter?' three departments produce three different numbers and spend two days reconciling. Every cross-functional question requires a manual pull from multiple systems. The answer is always 'approximately right' at best.
Reports take a week. Decisions can't wait that long.
Your monthly business review requires someone to spend 3-5 days pulling data from different tools, cleaning it in Excel, reconciling discrepancies, and building a presentation. By the time the report is ready, the data is stale. Ad-hoc questions from leadership ('what's our CAC by channel this quarter?' or 'which product lines are margin-positive after returns?') trigger a scramble. Your analysts are not analyzing. They're data janitors. 80% extraction and cleaning, 20% actual insight.
Nobody trusts the numbers
Marketing reports different revenue figures than finance. Two people pull the same report and get different answers because they're using different date ranges, filters, or data sources. Trust is gone. Every meeting starts with 'where did these numbers come from?' Decisions get made on intuition because the data is unreliable. The data team gets blamed for what is fundamentally an infrastructure problem.
'Data-driven' is a slide in your deck, not a practice in your company
You've invested in analytics tools (Metabase, Looker, Power BI, Tableau). Dashboards exist. Nobody looks at them. The data is stale, the metrics don't match reality, and the dashboards were built by someone who left 18 months ago. No data quality framework. No documentation. No ownership model. New hires have no idea which dashboard to trust. The BI tool has become an expensive way to produce charts that nobody acts on.
Four phases. From audit to a data stack you can actually rely on.
We don't install a BI tool and call it analytics. We audit your entire data environment, design an architecture that fits your scale and complexity, build the pipelines and warehouse, and deliver dashboards your team will actually use. Because the data behind them is trustworthy.
Data Audit & Assessment
Week 1-2
We map every data source in your organization: CRM, ERP, marketing platforms, product databases, support tools, custom spreadsheets, and the tribal knowledge sitting in someone's head. For each source, we assess data quality, freshness, completeness, and accessibility. We document every existing report, dashboard, and data pull. Who uses it. How often. Whether they trust it. We interview stakeholders across departments to understand what questions they're trying to answer and where the current data infrastructure fails them. The output is a specific, prioritized map of what's broken and what to fix first.
Deliverable: Data source inventory, quality assessment, stakeholder needs matrix, and prioritized infrastructure roadmap
Architecture Design
Weeks 2-4
Based on the audit, we design a data architecture that fits your scale, budget, and team capabilities. Warehouse structure (dimensional modeling, schema design). Pipeline architecture (batch vs. streaming, orchestration, error handling). Transformation layer (business logic, metric definitions, data quality checks). Consumption layer (BI tool selection, dashboard architecture, access controls). Every metric is defined once, documented, and approved by the business owner. No more three departments reporting different revenue numbers. We design for your current volume but build for 10x growth.
Deliverable: Data architecture document, warehouse schema design, pipeline specifications, metric definitions dictionary, and BI tool recommendation
Pipeline Build & Warehouse Deploy
Weeks 4-8
We build the infrastructure. Data pipelines pull from your source systems on the cadence that matters: real-time for operational data, hourly for marketing metrics, daily for financial data. The warehouse is deployed with proper dimensional modeling, slowly changing dimensions for historical tracking, and a transformation layer that implements the business logic we defined in Phase 2. Data quality checks run on every pipeline load: freshness monitoring, row count validation, schema change detection, business rule verification. If something breaks, you know within minutes, not when someone complains about a dashboard.
Deliverable: Deployed data warehouse, production data pipelines for all priority sources, data quality monitoring framework, and pipeline documentation
Dashboard & Reporting Layer
Weeks 8-10
With trustworthy data in the warehouse, we build the dashboards and reports your team needs. Executive dashboards that show what the CEO and board care about. Department dashboards for marketing, sales, finance, and operations with drill-down capabilities. Automated reports that replace the manual weekly and monthly pulls. Self-serve analytics for your more technical team members. Every dashboard is built with the stakeholder who will use it, tested with real data, and documented. We run training sessions so your team knows how to read the dashboards and ask new questions of the data.
Deliverable: Executive and department dashboards, automated reporting workflows, self-serve analytics layer, user training, and 30-day post-deployment support
Data Audit & Assessment
Week 1-2
We map every data source in your organization: CRM, ERP, marketing platforms, product databases, support tools, custom spreadsheets, and the tribal knowledge sitting in someone's head. For each source, we assess data quality, freshness, completeness, and accessibility. We document every existing report, dashboard, and data pull. Who uses it. How often. Whether they trust it. We interview stakeholders across departments to understand what questions they're trying to answer and where the current data infrastructure fails them. The output is a specific, prioritized map of what's broken and what to fix first.
Deliverable: Data source inventory, quality assessment, stakeholder needs matrix, and prioritized infrastructure roadmap
Architecture Design
Weeks 2-4
Based on the audit, we design a data architecture that fits your scale, budget, and team capabilities. Warehouse structure (dimensional modeling, schema design). Pipeline architecture (batch vs. streaming, orchestration, error handling). Transformation layer (business logic, metric definitions, data quality checks). Consumption layer (BI tool selection, dashboard architecture, access controls). Every metric is defined once, documented, and approved by the business owner. No more three departments reporting different revenue numbers. We design for your current volume but build for 10x growth.
Deliverable: Data architecture document, warehouse schema design, pipeline specifications, metric definitions dictionary, and BI tool recommendation
Pipeline Build & Warehouse Deploy
Weeks 4-8
We build the infrastructure. Data pipelines pull from your source systems on the cadence that matters: real-time for operational data, hourly for marketing metrics, daily for financial data. The warehouse is deployed with proper dimensional modeling, slowly changing dimensions for historical tracking, and a transformation layer that implements the business logic we defined in Phase 2. Data quality checks run on every pipeline load: freshness monitoring, row count validation, schema change detection, business rule verification. If something breaks, you know within minutes, not when someone complains about a dashboard.
Deliverable: Deployed data warehouse, production data pipelines for all priority sources, data quality monitoring framework, and pipeline documentation
Dashboard & Reporting Layer
Weeks 8-10
With trustworthy data in the warehouse, we build the dashboards and reports your team needs. Executive dashboards that show what the CEO and board care about. Department dashboards for marketing, sales, finance, and operations with drill-down capabilities. Automated reports that replace the manual weekly and monthly pulls. Self-serve analytics for your more technical team members. Every dashboard is built with the stakeholder who will use it, tested with real data, and documented. We run training sessions so your team knows how to read the dashboards and ask new questions of the data.
Deliverable: Executive and department dashboards, automated reporting workflows, self-serve analytics layer, user training, and 30-day post-deployment support
Infrastructure that makes dashboards trustworthy.
Audit & Design (Weeks 1-4)
- Complete data source inventory with quality scores and integration assessment for every system
- Stakeholder needs matrix mapping business questions to required data sources and metrics
- Data architecture document with warehouse schema, pipeline design, and technology recommendations
- Metric definitions dictionary: single source of truth for how every KPI is calculated
Build & Deploy (Weeks 4-8)
- Production data warehouse with dimensional modeling and historical tracking
- Data pipelines connecting all priority source systems with appropriate refresh cadences
- Transformation layer implementing business logic, metric calculations, and data quality rules
- Data quality monitoring with automated alerting for freshness, completeness, and accuracy issues
Dashboards & Handoff (Weeks 8-10)
- Executive dashboard with company-level KPIs, trends, and drill-down capabilities
- Department-specific dashboards for marketing, sales, finance, and operations
- Automated reporting workflows replacing manual weekly and monthly data pulls
- Full documentation, team training, and 30-day post-deployment support
We build data infrastructure. Here's what sits outside this scope.
Each of these can be scoped as a follow-on engagement based on the audit findings.
Machine learning or predictive analytics
Clean, unified data is a prerequisite for ML. Not the same thing. If the audit shows you're ready for predictive models (churn prediction, demand forecasting, recommendation engines), that's a separate engagement built on top of the data infrastructure we put in place.
AI DevelopmentSource system implementation or migration
If your CRM, ERP, or other source systems need replacing, that's a different project. We build pipelines from whatever systems you're running. If the audit reveals that a source system is the root problem, we'll tell you. But we don't implement CRMs or ERPs.
Operations AutomationOngoing analytics or BI team staffing
We build the infrastructure and train your team. We don't provide ongoing analysts or BI developers. If you need embedded analytics talent, we can help you hire the right profiles based on the architecture we've built.
Is this the right fit?
Right for you if
- You're a mid-market company with data in 5+ systems and no unified view. Leadership makes decisions on gut feel because pulling reliable data takes days, and nobody trusts the numbers when they finally arrive.
- You've invested in BI tools but they've become shelfware. Dashboards exist. Nobody uses them. The underlying data is stale or inconsistent. You need infrastructure, not more visualization.
- Someone on your team spends 3-5 days every month pulling data from multiple systems, reconciling it in Excel, and building reports. You know this doesn't scale.
- You've hit the point where the cost of bad data (bad decisions, time wasted arguing about numbers) is visibly larger than the cost of fixing the infrastructure.
Not right if
- You're a small team with 1-2 data sources and simple reporting needs. Google Sheets and a basic BI tool can handle your requirements without the overhead of a data warehouse. We'll tell you this during a consultation.
- Your core problem is that you don't know what to measure. You need a business strategy or OKR framework before you need data infrastructure. Data engineering solves the 'how do we access and trust our data' problem, not the 'what should we be tracking' problem.
What this looks like across industries.
Problem
A D2C brand doing $18M annually across their own website, Amazon, Flipkart, and Myntra had data everywhere and insight nowhere. Marketing spend data was in Google Ads, Meta, and influencer tracking spreadsheets. Sales data was split across Shopify, marketplace APIs, and their ERP. Inventory data was in a warehouse management system that didn't talk to anything else. The marketing team couldn't answer 'what's our blended CAC by channel including returns?' without a week of manual work. The founder couldn't get a real-time margin view by SKU because cost data, return rates, and logistics costs were in three different systems.
What we did
Built a cloud data warehouse that unified data from 12 source systems: Shopify, marketplace APIs, Google Ads, Meta, ERP, WMS, logistics partners, and payment gateways. Designed a pipeline architecture that refreshed marketing data hourly, sales data every 15 minutes, and financial data daily. Built a transformation layer that calculated true unit economics: revenue minus COGS, logistics, returns, marketplace commissions, and attributed marketing spend by SKU and channel. Deployed dashboards for marketing (channel performance, CAC, ROAS), operations (inventory, fulfillment, returns), and finance (margin by SKU, cash flow, P&L).
Outcome
The 'what's our blended CAC?' question went from a week-long project to a dashboard refresh. Real-time SKU-level margin visibility revealed that 15% of their catalog was margin-negative after returns and logistics. These were products they'd been actively advertising. Monthly reporting went from 5 days of manual work to automated delivery on the 2nd of every month. The founder's weekly review meeting shifted from arguing about numbers to discussing strategy.
Problem
A B2B SaaS company at $7.5M ARR had a metrics trust problem. Product reported 'active users' one way, customer success had a different definition, and finance calculated churn differently from both teams. Board reporting took the CFO 4 days every quarter, most of which was reconciling conflicting numbers. The product team couldn't answer 'which features drive retention?' because product usage data wasn't connected to subscription data. The sales team was flying blind on expansion revenue opportunities because nobody could see usage patterns alongside contract data.
What we did
Built a data warehouse that unified product telemetry, Salesforce CRM data, billing system data (Chargebee), and support data (Freshdesk) into a single schema. Created a canonical customer entity that linked product usage, subscription status, support interactions, and revenue data per account. Defined metrics once (MRR, churn, NRR, active users, feature adoption) with documented calculation logic approved by each department head. Built executive dashboards, a customer health scoring model, and automated the quarterly board reporting package.
Outcome
Board reporting went from 4 days of manual work to automated generation on demand. The 'three different churn numbers' problem disappeared. One definition, one source, no debate. Customer health scoring surfaced 20+ accounts showing early churn signals that CS hadn't flagged. Product identified 3 features with outsized retention impact that became the focus of next quarter's roadmap. Expansion revenue pipeline increased as sales could see usage patterns for upsell timing.
Problem
A mid-market manufacturer with 3 plants and $50M annual revenue had a visibility problem. Production data was in MES systems at each plant, all different vendors and different formats. Quality data was in Excel. Procurement data was in SAP. Sales forecasts were in spreadsheets. The COO couldn't get a production efficiency view across all 3 plants without someone spending 2 days pulling and normalizing data. Demand planning was disconnected from production capacity, which was disconnected from procurement timelines. Every month, they either over-produced (inventory carrying costs) or under-produced (missed delivery dates) because nobody had an integrated view.
What we did
Built a unified data warehouse that ingested data from 3 different MES systems, SAP, quality management spreadsheets, and the sales forecasting tool. Normalized production metrics (OEE, yield, cycle time, downtime) across plants into a consistent schema despite different source systems. Created a demand-supply matching layer that connected sales forecasts to production capacity and procurement lead times. Built dashboards for plant managers (real-time production), the COO (cross-plant performance), and procurement (lead time tracking and stockout predictions).
Outcome
Cross-plant production visibility went from a 2-day manual exercise to a real-time dashboard. The normalized OEE comparison revealed that Plant 2 was consistently 12% below the other plants, a performance gap that had been hidden by different reporting formats. Demand-supply matching reduced both overproduction and stockouts in the first quarter. Procurement lead time visibility prevented 3 material shortages that would have caused production stoppages.