You have more data than ever and less clarity than ever.
Data pipelines, warehousing, BI dashboards, and reporting automation for mid-market companies where nobody trusts the numbers. Replace spreadsheet chaos with one reliable source.
The Problem
You have plenty of data. You don't have infrastructure.
Data is everywhere and nowhere: Sales in Salesforce. Marketing split across Google Analytics, Meta Ads, and HubSpot. Finance on Tally or Zoho Books. Operations on spreadsheets. When the CEO asks 'how are we doing?' three departments produce three different numbers and spend two days reconciling. Every cross-team question means a manual pull from multiple systems.
Reports take a week. Decisions can't wait that long.: Monthly business reviews take 3-5 days of pulling data, cleaning in Excel, reconciling, and building presentations. By the time the report is ready, it's stale. Ad-hoc questions from leadership trigger a scramble. Your analysts spend 80% of their time on extraction and cleaning, 20% on 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 used different date ranges, filters, or data sources. Every meeting starts with 'where did these numbers come from?' Decisions get made on gut feel because the data is unreliable. The data team gets blamed for what is an infrastructure problem.
'Data-driven' is a slide in your deck, not how your company works: You've invested in analytics tools. Dashboards exist. Nobody looks at them. Data is stale, metrics don't match reality, and the dashboards were built by someone who left 18 months ago. There's no quality framework, no documentation, no ownership model. The BI tool is an expensive way to produce charts nobody acts on.
Our Approach
Four phases. From audit to a data stack you can rely on. We don't install a BI tool and call it analytics. We audit your full data environment, design architecture that fits your scale, build the pipelines and warehouse, and deliver dashboards your team will use. Because the data behind them is trustworthy.
Phase 1 — Data audit & assessment (Week 1-2): We map every data source: CRM, ERP, marketing platforms, product databases, support tools, spreadsheets. For each one, we assess quality, freshness, completeness, and accessibility. We document every existing report and dashboard, who uses it, how often, and whether they trust it. Stakeholder interviews tell us what questions need answering and where the infrastructure fails. Output: a prioritized map of what's broken and what to fix first. Deliverable: Data source inventory, quality assessment, stakeholder needs matrix, and prioritized infrastructure roadmap
Phase 2 — Architecture design (Weeks 2-4): We design a data architecture that fits your scale, budget, and team. Warehouse structure (dimensional modeling, schema design). Pipeline architecture (batch vs. streaming, orchestration, error handling). Transformation layer (business logic, metric definitions, quality checks). Consumption layer (BI tool, dashboards, access controls). Every metric gets defined once, documented, and approved by the business owner. Designed for current volume, built for 10x growth. Deliverable: Data architecture document, warehouse schema design, pipeline specifications, metric definitions dictionary, and BI tool recommendation
Phase 3 — Pipeline build & warehouse deploy (Weeks 4-8): Pipelines pull from source systems at the cadence that matters: real-time for operational data, hourly for marketing, daily for financial. Warehouse goes live with dimensional modeling, slowly changing dimensions for history, and the transformation layer from Phase 2. Data quality checks run on every load: freshness monitoring, row count validation, schema change detection. If something breaks, you know within minutes. Deliverable: Deployed data warehouse, production data pipelines for all priority sources, data quality monitoring framework, and pipeline documentation
Phase 4 — Dashboard & reporting layer (Weeks 8-10): With trustworthy data in the warehouse, we build the dashboards your team needs. Executive dashboards for CEO and board. Department dashboards for marketing, sales, finance, and operations with drill-down. Automated reports that replace manual weekly and monthly pulls. Self-serve analytics for technical team members. Every dashboard is built with the person who'll use it, tested with your data, and documented. Deliverable: Executive and department dashboards, automated reporting workflows, self-serve analytics layer, user training, and 30-day post-deployment support
Deliverables
Audit & Design (Weeks 1-4)
- Complete data source inventory with quality scores and integration assessment for every system
- Stakeholder needs matrix that maps business questions to required data sources and metrics
- Data architecture document with warehouse schema, pipeline design, and technology recommendations
- Metric definitions dictionary: one reliable reference for how every KPI is calculated
Build & Deploy (Weeks 4-8)
- Production data warehouse with dimensional modeling and historical tracking
- Data pipelines that connect all priority source systems at the right refresh cadences
- Transformation layer that runs business logic, metric calculations, and data quality rules
- Data quality monitoring with automated alerts for freshness, completeness, and accuracy issues
Dashboards & Handoff (Weeks 8-10)
- Executive dashboard with company-level KPIs, trends, and drill-down
- Department dashboards for marketing, sales, finance, and operations
- Automated reporting workflows that replace manual weekly and monthly data pulls
- Full documentation, team training, and 30-day post-deployment support
Who This Is For
Right for you if: You're a mid-market company with data in 5+ systems and no unified view. Leadership decides on anecdotal evidence 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 won't scale.. The cost of bad data (bad decisions, time wasted arguing about numbers) is now 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 will cover you without the overhead of a data warehouse. We'll tell you this during a consultation.. Your real 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. The 'what should we track' question comes first..
Use Cases
E-commerce / D2C: A D2C brand doing $18M annually across their own website, Amazon, Flipkart, and Myntra had data everywhere and insight nowhere. Marketing spend 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 lived in three different systems. — 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 pipelines 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. SKU-level margin data 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 shifted from arguing about numbers to discussing strategy.
SaaS / B2B: 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 it reconciling conflicting numbers. The product team couldn't answer 'which features drive retention?' because usage data wasn't connected to subscription data. Sales was blind on expansion revenue because nobody could see usage patterns alongside contract data. — Built a data warehouse that unified product telemetry, Salesforce CRM data, Chargebee billing data, and Freshdesk support data into a single schema. Created a canonical customer entity that linked product usage, subscription status, support interactions, and revenue 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 with early churn signals that CS hadn't flagged. Product identified 3 features with outsized retention impact that became next quarter's roadmap focus. Expansion revenue pipeline grew as sales could see usage patterns for upsell timing.
Manufacturing: 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 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. — 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, stockout predictions).. Outcome: Cross-plant production visibility went from a 2-day manual exercise to a real-time dashboard. The normalized OEE comparison showed Plant 2 was consistently 12% below the other plants, a 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 stopped production.
Results
Project walkthrough
D2C e-commerce, multi-channel data unification: Monthly reporting from 5 days to automated, 15% of catalog identified as margin-negative. A D2C brand selling across their own website and 3 marketplaces came to us because their leadership team couldn't get a straight answer on basic questions: blended customer acquisition cost, true margin by SKU, or return rates by channel. Data was spread across 12 systems with no integration. Monthly reporting consumed one person for a week. Marketing couldn't attribute spend to revenue at the SKU level. Finance and marketing reported different revenue numbers every month. We audited their data environment in Weeks 1-2, designed the warehouse architecture and metric definitions in Weeks 2-4, built pipelines and the warehouse in Weeks 4-8, and deployed dashboards with training in Weeks 8-10. The metric definitions process alone (getting marketing, finance, and operations to agree on how to calculate CAC, margin, and ROAS) was worth the engagement. The infrastructure revealed that 15% of their SKU catalog was margin-negative after accounting for returns, logistics, and marketplace commissions. They'd been spending ad budget on products that lost money. The shift from 'arguing about numbers' to 'acting on numbers' changed how the leadership team operated.
Frequently Asked Questions
What technology stack do you use?
We pick tools based on your scale, budget, and team. For warehousing: BigQuery, Snowflake, Redshift, or Postgres depending on volume and cloud preference. For pipelines: Airbyte, Fivetran, or custom Python depending on source systems. For transformation: dbt. For BI: Metabase, Looker, Power BI, or Tableau based on your team's technical comfort and existing investments. We recommend during the architecture phase and explain trade-offs in plain language.
How long before we see value?
The audit (Weeks 1-2) delivers value right away. Many companies discover data quality issues, redundant tools, or metric definition conflicts they didn't know existed. First dashboards go live in Weeks 8-10. The bigger shift is when your team stops spending days on manual reporting and starts spending hours on analysis. That happens the month after deployment.
We already have a BI tool. Why aren't dashboards enough?
Dashboards are the visualization layer. If the data behind them is stale, inconsistent, or wrong, the dashboards are just pretty lies. The majority of companies that come to us already have BI tools. The problem is rarely the tool itself. There's no reliable warehouse feeding it, no pipeline monitoring, no quality checks, no agreed-upon metric definitions. We create what makes your existing BI tool useful.
Can our team maintain this after you leave?
That's the entire point. We build with standard, well-documented tools (dbt for transformations, Airbyte or Fivetran for pipelines, your BI tool of choice). Everything is documented, version-controlled, and tested. The 30-day post-deployment support covers knowledge transfer and any issues your team hits. We design the architecture for your team's skill level. If you don't have a senior data engineer, we won't build something that requires one.
What if our data quality is terrible?
It usually is. That's one of the problems we solve. The audit identifies quality issues at the source. The pipeline architecture includes quality checks on every load. The transformation layer handles cleanup, deduplication, and normalization. We can't fix garbage data at the source (if your sales team doesn't update the CRM, that's a process problem). But we can detect it, flag it, and stop it from corrupting downstream reporting.
Do we need to migrate to the cloud?
Not necessarily. If your source systems are on-premises, we can build pipelines that extract from on-prem and load into a cloud warehouse. Most modern data stacks are cloud-based because the economics and scalability make sense. But if you have constraints (regulatory, policy, or preference) that require on-prem, we can design for that. The architecture phase covers this explicitly.
Can we start with just one department?
Yes, and it's often the smart move. A lot of firms start with the department where the pain is worst, usually marketing or finance. The audit covers the full picture, but the build can be phased. Start with one department's data, prove the value, then expand. The architecture is designed to take on additional sources and use cases without a rebuild.
