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This case study contains information from work completed under non-disclosure agreements. Sensitive details have been modified or omitted to respect confidentiality obligations. The content represents my personal analysis and work contributions, and does not necessarily reflect the views or positions of Whatagraph.

As the platform scaled across integrations and enterprise clients, structural data fragmentation began limiting product evolution.

INTRODUCTION

This initiative redefined how multichannel data was gathered, unified, and operationalized — creating the foundation for scalable analytics, automation, and advanced intelligence capabilities.

As the platform scaled across 48+ marketing channels, data preparation became a structural bottleneck for performance monitoring, automation, and advanced analytics.

CONTEXT

Inconsistent schemas, slow API dependencies, and limited data flexibility constrained data visualization, while the legacy data mixing functionality required repetitive manual setup and was difficult for non-technical users to operate.

To unlock scalable goal tracking, intelligent alerts, AI capabilities, and advanced blended reporting, the data layer required systemic redesign.

IMPACT

Increase adoption among all clients and reduce data setup time.

MY ROLE

I shaped the product direction and led the end-to-end design of the initiative — from early validation and scoping to final system definition for development.

As the sole designer, I worked closely with the CTO, CPO, and backend engineers to align architectural decisions with business strategy and deliver a scalable foundation for future product growth.

CHALLANGE

As the platform scaled, inconsistent data structures and complex manual data mixing limited teams’ ability to confidently organize and visualize their data — requiring a more unified and scalable foundation.

OBJECTIVES

Build a scalable foundation for advanced analytics and future growth

Create consistency across fragmented multichannel data

Reduce friction and performance bottlenecks in data operations

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Scalable Data Foundation

To achieve this, we restructured how data is unified, combined, and operated across the platform.

Unified Structure

Established a consistent data model across integrations, removing fragmentation and creating clarity at scale.

Simplified Combination

Redesigned how data is combined across channels, turning a complex, repetitive process into an intuitive and scalable experience.

Smart Operational Layer

Introduced intelligent data storage to improve performance, reduce latency, and support advanced analytical capabilities.

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A wealth of data and insights translated into a better experience

Taking the findings from the numerous interviews, user tests, and analytics — we amended the data field selection to be simpler and mapped for users as much as possible, providing a more streamlined experience for first time users.

Explain the general idea how everything can be done easily when you start from a template (happy path)

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Animated or single view of templates

Benefits, templates based use-cases

We need to ensure that the data can be automatically unified by the platform. This will also unblock any type of modelling capabilities for the future.

Explain what the solution is, how easy jobs to be done can be done based two views, basic and advanced.

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Animated or single view of advanced view

Benefits

INTRODUCING AGGREGATION BUILDER

Aggregation Builder gives teams a unified way to combine, organize, and operate with data across multiple marketing channels — removing the friction caused by inconsistent metrics and dimensions.

With an intuitive interface and powerful logic under the hood, users can create unified views in seconds, while advanced filtering and aggregation options support more complex, technical scenarios.

The result is a scalable data foundation that empowers teams to move from fragmented inputs to confident decision-making.

Bringing structure and flexibility to cross-channel data.

OVERALL IMPACT

This initiative reduced manual setup time by 40-60%, improved data consistency across accounts, accelerated enterprise onboarding, and established a scalable architecture for future data capabilities.

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