Making trusted data easier to discover, understand, and use across a complex platform ecosystem.

ABuilding Signals Marketplace, Grab’s internal Data Mesh, to make trusted data easier to identify, use, and own across the organization.

Data Signals

Defined the design strategy and principles for how trust, certification, and ownership surfaced across tools.

Foundation

Aligned product, engineering, my design around consistent principles.

Coordination

Coached designers to own domains and contribute to shared governance, ensuring the system evolved beyond any one team.

Enablement

Role

As Grab expanded across Southeast Asia, our data ecosystem grew exponentially. Access to data was improving, but the ability to trust and use it confidently was not. To move toward an AI-driven future, we needed data to be reliable, contextual, and actionable.

The challenge

Each business line produced its own pipelines and definitions, creating overlap and inconsistency. Teams duplicated work to maintain control.

Scale and Fragmentation

A central engineering model had become a bottleneck. Producers and consumers operated in silos, with limited accountability for data quality.

Centralization Bottleneck

Teams varied in data maturity. Many lacked visibility into reliability or lineage, forcing each to rebuild trust from scratch.

Uneven Literacy

Three challenges shaped our approach to rebuilding trust at scale.

Three core challenges

We introduced Data Mesh, treating data as a product owned by domain teams. Our goal: make trust visible and measurable through systems that let teams define, verify, and communicate data quality directly.

Making trust visible

Certification made trust tangible. It was both a technical mechanism and a design component, applied consistently across data surfaces. For producers, it formalized guarantees. For consumers, it provided visible trust signals, reducing manual validation.

Certification: the cornerstone

We treated Data Mesh as a design system built from modular components, sustained through contribution, and designed for action. Each designer owned a data domain and was accountable for applying certification and contract components within their context.

Design system approach

Demonstrating trust and adoption across teams.

75% of queries targeted certified assets

Reflecting more active cleanup of redundant and duplicated assets.

400% year-over-year rise in deprecation

The impact was visible in adoption and more active cleanup of the data ecosystem.

Impact

Adoption required clear technical standards and behavior change. Certification took more effort, but it established defined ownership, stronger reliability guarantees, and greater confidence for data consumers. As AI expands, trustworthy data becomes critical.

My role shifted from doing the work to enabling others. I helped designers move beyond interfaces to shaping systems, giving them confidence to work with technical complexity and data governance.

The project reinforced a core design lesson: scaling trust requires making ownership, standards, and accountability visible across the system.


Key learnings & reflection

Giving teams greater visibility and control over the data powering their products and campaigns.

The solution that helps data governors manage the process of sourcing, analyzing, and controlling the available data in Leanplum to leverage in their business and marketing strategies.

Events & Partner Integrations

Making Data Accessible and Manageable for Marketing Teams. While Leanplum's messaging and engagement tools were powerful, data capabilities had a critical gap. Data was technically accessible, but discovering, managing, or activating it often required developer support, creating bottlenecks for marketing teams.

Overview

The only data-facing tool in the platform was a static Events list with no metadata, context, or controls.

Static Events List

User attributes lacked a dedicated management interface, and importing/exporting data was primarily done through APIs.

No Attribute Management

CSV uploads required developer intervention. Partner integrations existed, but weren't visible or configurable from the UI.

Developer Dependencies

Non-technical users, especially marketers and CRM leads, were regularly blocked from executing basic workflows.

User Workflow Blocks

Our support team spent a disproportionate amount of time responding to tickets related to data imports, partner setups, and event discovery.

Support Volume

The only data-facing tool in the platform was a static Events list with no metadata, context, or controls. During early audits and user interviews, it became clear that users lacked a clear source of truth for their data.

Problem

We audited the existing experience and reviewed common support tickets. The Events list presented raw data, but lacked the metadata and control users needed to make sense of it.

Product Analysis

Marketers often shared that they weren't confident in what data existed or how reliable it was. Many avoided attempting data uploads or partner integrations altogether to avoid back-and-forth with engineering.

User Interviews

Support Ticket Review

Setting up CSV imports or integrations required combing through documentation or working with developers. In practice, many marketers simply avoided these workflows altogether.

User Workflow Analysis

We began by auditing the existing experience and understanding user needs through comprehensive analysis.

Research

Data Management - Central Hub for All Data Operations. We introduced a new top-level navigation item, Data Management. This became the central hub for all data integrated into and out of Leanplum. I worked closely with the PM and engineering leads to define the information architecture and interaction model for this new surface.

Solution

View and edit all tracked user events, with added metadata and validation controls.

Events

Manage user properties through a similar interface to Events.

Attributes

A directory of third-party integrations with setup flows embedded directly into the UI.

Partners

A drag-and-drop interface for non-technical users, with inline validation and upload history.

CSV Uploads

We built four modular, interconnected components. Each module reused consistent patterns from the rest of the product.

Four Core Modules

Initial Design Approach. We initially explored using a two-level detail view pattern similar to what we had implemented in the Audiences page. However, due to technical constraints, saving edits on individual records would block the entire interface while the backend job completed. This would have introduced a frustrating delay for users.

Events & Attributes Design

User Testing Reveals Critical Issues. We tested early flows with a small beta cohort and used their feedback to refine how we surfaced data status. One of the most significant insights came when users struggled with the Events module's save behavior. Many were confused when editing individual events caused delays or interface blocking. This validated the need for our batch-edit single-page model and clarified our prioritization going forward. The feedback reinforced that, while the adjustment required a learning curve, it ultimately aligned better with how teams work across multiple events in one session.

Beta Validation

From Individual Edits to Batch Workflows. Based on beta feedback, we optimized batch workflows to meet user expectations at scale and shifted additional design focus toward clarity, responsiveness, and feedback mechanisms for these batch actions.

Refined Solution

Final Single-Page Layout. To resolve the interface blocking issue, I designed a single-page layout that allowed users to make batch edits across events and then save them together. This pattern preserved flexibility while accommodating technical constraints. We added persistent save indicators and inline validation to prevent surprises.

Events Management

Dedicated Interface for User Properties. User attributes gained a management interface similar to Events. Marketing teams could now manage customer properties directly without relying on documentation or developer support.

User Attributes Management

Making Integrations Discoverable and Actionable. Integrations were previously handled through engineering teams and documentation. We made them discoverable and actionable in-product by introducing a Partners page where users could view and configure integrations directly from the UI. This also helped surface and promote the full set of available integrations in Leanplum, many of which users were previously unaware of.

Streamlined Drag-and-Drop Experience. We designed a streamlined drag-and-drop experience for uploading user data via CSV. Integrated validations provide immediate feedback on file structure and formatting issues before upload. To support collaboration and traceability, we also included a visible upload history with metadata such as filename, upload time, and uploader.

Non-technical users gained the ability to access and manage data without relying on engineering or support. This unlocked faster iteration cycles and gave marketing teams greater autonomy in campaign setup.

User Empowerment

Tickets related to data access, configuration, and imports dropped significantly, allowing support teams to focus on higher-impact work.

Support Load Reduction

By surfacing and enabling partner integrations directly within the platform, we extended Leanplum's capabilities and positioned Data Management as the foundation for future ecosystem expansion and vendor flexibility.

Expanded Product Offerings

Data Management became the foundation for broader data governance and user behavior segmentation. By centralizing data visibility and control, we enabled more sophisticated campaign strategies.

From Blocker to Strategic Enabler. Data Management transformed how Leanplum treated data-no longer just a backend artifact, but a strategic, user-facing capability that became a core enabler of campaign strategy and lifecycle marketing.

Reflection

A lightweight design system with essential UI components and responsive layouts.

Enabling product teams to plan, launch, monitor, and learn from experiments with greater confidence.

The experimentation platform supports how teams discover, run, evaluate, and roll out experiments at scale. As usage grew, the platform needed to evolve into a system that accelerates learning, improves decision-making, and reduces the cost of iteration.

A redesigned dashboard optimized for speed, clarity, and scale.

Interactive Experiment Dashboard

Experimentation Platform

I led Product Design for the experimentation platform, owning strategy, discovery, and execution end to end.

Understood how experimentation worked end to end across discovery, evaluation, and setup by analyzing usage data and collaborating closely with engineers, data scientists, and product managers.

Product Discovery

Translated insights into a clear product strategy and roadmap, prioritizing improvements across the Dashboard, Experiment Details, and Creation Flow.

Product Strategy

Led design from prototyping through implementation, partnering closely with engineering to ship changes that materially improved experimentation velocity and reliability.

Design & Delivery

Experimentation is a core driver of product innovation, but at scale, the tools supporting it began to slow teams down.

Friction accumulated across discovery, evaluation, and setup. The problem was not isolated usability issues. The experimentation workflow itself did not scale.


The Challenge

The platform struggled to keep up as experimentation volume increased, slowing down routine workflows.

Performance and Efficiency

Critical signals were difficult to scan and reason about, increasing cognitive overhead during discovery and evaluation.

Clarity and Information Access

Key workflows introduced unnecessary cognitive load and surfaced errors too late, reducing trust in experiment quality.

Workflow Complexity and Reliability

Three systemic challenges shaped how we approached rebuilding the experimentation platform.

Three Core Challenges

The entry point to experimentation, redesigned for speed and control. A lightweight, scan-friendly dashboard that supports fast discovery, clear ownership, and efficient monitoring at scale.

Optimized for fast discovery and everyday control as experimentation scales.

Experimentation Dashboard

The decision surface for experimentation. A consolidated results view that brings context, metrics, and outcomes into one place, enabling faster evaluation and more confident rollout decisions.

Clear hierarchy and standardized patterns for reliable decision-making.

Experiment Details Page

Experiment Details

The foundation of experiment quality. A guided creation flow with real-time validation and reusable configurations that improves setup reliability and reduces errors.

Structured setup with validation and cloning to support safe, repeatable experimentation.

Treatment Configuration Table

Experiment Creation Flow

Teams could move from idea to outcome more quickly.

Faster Learning Cycles

Teams ran more valid experiments in parallel with the same resources.

Higher Experimentation Throughput

Clearer results enabled faster rollouts and earlier termination of underperforming experiments.

Greater Decision Confidence

By reducing friction across discovery, evaluation, and setup, the platform shifted from slowing teams down to enabling faster, more reliable experimentation.

Experimentation tooling must be treated as a system. Optimizing individual features without addressing end-to-end flow creates local improvements but global friction.

Internal platforms shape how teams work, decide, and allocate effort. Thoughtful design increases velocity and confidence, while poor design quietly taxes the organization.

Product success depends on adoption. Clear communication, onboarding, and change management were essential to ensuring these improvements translated into sustained behavior change.


Key Learnings & Reflection

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