THE PROBLEM IS NOT LACK OF DATA
Discovery & Information Architecture
The engagement started with understanding how the business operates rather than how the systems were built.
At first glance, the company had plenty of data.
In reality, it had multiple versions of the truth. Shopify knew about orders. Klaviyo knew about marketing campaigns. Pipedrive and Freshsales tracked opportunities and managed customer relationships. Internal systems contained critical operational data. Each platform answered different questions, but none could explain the whole business.
Instead of immediately building a data platform, we started by understanding the business itself.
Through workshops with stakeholders, we mapped business processes, identified the information leaders needed to make decisions, and created a shared vocabulary for the organization.
Only after everyone agreed on what should be measured and how it should be interpreted did we design the analytical foundation.
This transformed disconnected systems into a single, consistent view of the business creating a trusted source of information for reporting, decision-making, and future AI initiatives.
How We Designed a Modern Data Platform
We helped a fast-growing e-commerce marketplace transform data from Shopify, Klaviyo, Pipedrive, Freshsales, and custom applications into a unified analytical platform. Starting with business discovery and information architecture, we designed a scalable foundation that supports trusted reporting, executive decision-making, and future AI initiatives.
1
Business Discovery
We worked with stakeholders to understand business processes, reporting needs, KPIs, and decision-making challenges before defining the target architecture.
2
Information Architecture
We defined canonical business entities, standardized KPI definitions, and designed an analytical data model independent of Shopify, CRM, or any individual system.
3
Source System Integration
We analyzed Shopify, Klaviyo, Pipedrive, Freshsales, and custom applications, mapping every required dataset into a unified business model.
4
Data Platform Architecture
We selected the cloud architecture, designed ingestion pipelines, orchestration, transformation workflows, and the analytical warehouse.
5
Facts, Dimensions and Data Marts
We designed dimensional models, fact tables, and business-oriented data marts supporting reporting, executive dashboards, and future AI use cases.
6
Implementation Leadership
We led implementation, reviewed technical decisions, coordinated engineering teams, and ensured the solution aligned with the original business objectives.
Before
The organization relied on multiple operational systems, each optimized for a specific business function. While every platform served its purpose, there was no unified view of the business.
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Business data scattered across Shopify, Klaviyo, Pipedrive, Freshsales, and custom systems
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Different departments reporting different numbers
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Manual effort required to combine data
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Inconsistent KPI definitions
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Limited visibility across sales, marketing, finance, and operations
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No trusted foundation for advanced analytics or AI
After
A unified analytical platform connected data from every business system into a single, trusted information layer. Teams gained consistent KPIs, reliable reporting, and the confidence to make faster, data-driven decisions.
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Single source of truth across all connected systems
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Standardized business definitions and KPIs
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Business-oriented data marts for reporting and analytics
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Automated data pipelines replacing manual data preparation
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Consistent reporting across departments
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AI-ready analytical foundation designed for future growth
