Standardizing Multichannel Data Architecture

Transforming fragmented cross-channel data into a scalable system that teams could understand, operate, and grow with confidence.

CONFIDANCE NOTICE

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 expanded across more integrations and enterprise use cases, the way data was prepared and combined began limiting what the product could support next.

INTRODUCTION

This initiative redefined how cross-channel data was structured, combined, and operationalized — creating a stronger foundation for scalable analytics, automation, and future 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 visualization, 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

The workflow relied on repetitive manual setup, advanced configuration reduced visibility into how information lined up, and editing existing setups could be fragile. What was needed was not just a cleaner interface, but a clearer and more scalable model for how cross-channel data should work.

OBJECTIVES

Reduce friction and performance bottlenecks in data operations

Build a scalable foundation for advanced analytics and future growth

Create consistency across fragmented multichannel data

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The opportunity was not just to simplify one flow, but to introduce a clearer product model for how data should be structured, combined, and maintained across channels — one that could support both everyday use cases and more advanced scenarios over time.

Scalable Data Foundation

To move forward, we restructured how data is organized, 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 sources, turning a manual and repetitive process into a workflow that was easier to operate and repeat.

Smart Operational Layer

Introduced a stronger operational foundation to improve performance, reduce latency, and support more advanced analytical and modelling capabilities.

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A wealth of data and insights translated into a better experience

Using insights from interviews, usability testing, and product signals, I reworked the setup flow to be simpler and easier to understand — especially for first-time users.

This helped reduce unnecessary complexity earlier in the experience and created a more guided path into a system that had previously been difficult to approach.

Instead of asking users to build logic from scratch every time, the experience provided a faster way to begin from a clearer structure. This lowered cognitive load, made setup easier to repeat, and helped less technical users get to value faster.

While the default path made common setup easier, the system also needed to support more advanced configuration.

Structured the experience to offer a simpler path for everyday use, while still allowing deeper control when teams needed more flexibility, filtering, or precision. This helped the product serve a broader range of users without forcing everyone into the same level of complexity.

INTRODUCING AGGREGATION BUILDER

It replaced a fragmented setup experience with a more scalable workflow that supports both speed and flexibility — giving users a clearer path to create unified views, while enabling more advanced logic where needed.

The result was a stronger data foundation that helped teams move from disconnected inputs to more confident decision-making.

Aggregation Builder introduced a unified way to combine, organize, and operate with data across multiple marketing channels.

OVERALL IMPACT

Reduced manual setup time by 40%, supported continued growth in metric creation across accounts, and established a scalable data foundation that enabled more advanced workflows while supporting enterprise adoption.

Reduction in Data Setup Time

Streamlined data preparation and unification, significantly reducing manual effort required to structure and combine data across channels.

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60%

Growth in Advanced Metric Usage

Increased adoption of custom metrics and structured data workflows, particularly among high-volume and enterprise accounts.

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