Manya Singh
Seattle, WA
Re-designing Data and AI Marketplace
Designing an AI-Ready Enterprise Data Platform
Overview
ORGANIZATION
Accenture Song
ROLE
User Experience Designer
Duration
5 months
Team
2 UX Designers + 1 Senior UX Researcher + 1 Design Manager + 1 Design Director
my Tasks
Wireframing, Information Architecture, Design System, Dashboards, Heuristic Evaluations, Accessbility Audit, Usability Testing, Research
Key Impact
50%
Reduction in time spent accessing data of a business intelligence platform I redesigned
25%
Higher user satisfaction (CSAT survey)
Background
Legacy Platform in Need of Reinvention
The Data Marketplace was a critical enterprise platform used by 300K+ employees across domains like Finance, HR, Sales, and Tech. As data scaled and AI adoption accelerated, the platform couldn’t keep up. It led to slower decisions, user frustration, and a growing trust gap.
5k Daily Users
300K Users
15 Countries
Outcome
Data Marketplace: An AI-Ready Enterprise Data Platform
The entire platform was reimagined, from core components to new personalized experiences. We designed a smarter, role-aware system that helped users cut through clutter, access relevant insights, and make faster, more confident decisions across the business.
Drag the center icon to see the before and after versions!
What I designed
250+ Frames Redesigned
100+ DS components
Chat Interface
15+ Forms and Wizards
Data Viz Dashboards
Jump to Redesign
Context
A centralized data marketplace used across global teams to find, publish, and govern enterprise data
What is a data marketplace?
Data Marketplace is an internal Business Intelligence (BI) platform that empowers the C-suite and global teams to make fast, data-driven decisions. By centralizing enterprise data, it transforms complex, scattered information into accessible, high-quality insights, making big data usable, trustworthy, and actionable at scale.
Drag the center icon to see the before and after versions!
Key Actions
Supports business teams like Finance, HR, Sales, Marketing, and IT
Helps leaders at global and regional levels make data-driven decisions
Used by roles such as financial managers, analysts, and operations leads
Makes big data accessible for tracking costs, profits, and performance across teams
Persona
Designing for High-Stakes Users Across Business Functions
Our users held dual roles, both as stakeholders setting strategic direction and as active users relying on the platform for daily decision-making. From C-suite executives to data analysts, they spanned product leaders and decision-makers across critical domains. This meant the platform had to be clear, reliable, and deliver real business value.
Data Producer: Owns and manages data products, ensures timely updates, and maintains data documentation.
Data Consumer: Uses data to generate insights or make decisions.
Data Governor: Ensures data quality, compliance, and integrity across the organization.
Problem
A rigid, outdated platform that couldn’t scale with growing data needs or AI capabilities
Foundational Gaps
The platform was inefficient, impersonal, and not designed to support its growing user base. All roles shared the same rigid experience, essential actions were buried, and trust was eroding due to inconsistent and unclear flows.
Need for an AI-Ready Platform
As generative AI accelerated expectations around speed, intelligence, and adaptability, the platform had to evolve too. With data moving faster than ever, decision-making needed to be supported, not slowed down.
System Summary
Rigid
Impersonal
Slow Flow
Irrelevant content
Efficiency
Trust
Intelligent Systems
Assistive systems
Personalized experiences
Scalable
Research
Strategic Research to Uncover Gaps and Drive Alignment
The scale and complexity of the platform brought challenges that went beyond usability. Many of our users were also product leaders themselves, making it even more important that every design choice was informed, defensible, and grounded in evidence.
Heuristic Evaluation
To assess the existing experience, an evaluation was conducted by me across 25+ wireframes. This helped identify early usability issues, visual inconsistencies, and areas of friction in key workflows.
Usability Testing & Stakeholder Interviews
We interviewed 20+ stakeholders and conducted usability tests with over 30+ users. This revealed major challenges such as information overload, inefficient workflows, and unclear navigation.
Heuristics and Accessibility Issues that surfaced during initial research.
Insights that drove the re-design
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Users struggled with information overload and unclear data hierarchy.
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Navigation was inefficient, with too many steps to reach key data.
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Key Actions of multiple personas were not supported
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Inconsistent UI elements caused confusion across screens.
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Slow data access and decision-making
Goals
UX goals aligned with business objectives to drive adoption, efficiency, and long-term scalability
Design scalable systems to support future AI capabilities and growing datasets.
Improve access flows and reduce user friction without disrupting ongoing operations.
Ensure implementation feasibility by aligning closely with development constraints and timelines.
Strategy
How to transform the legacy data platform into an intelligent, AI-ready experience?
We shaped a three-phase strategy that allowed us to respond quickly to pain points while laying the foundation for long-term evolution:
Phase 1: MVP & Iterative Discovery
Targeted improvements and new features delivered while learning from rapid testing and real-time feedback
Phase 2: Rebuilding the Foundation
Addressing structural issues through redesigned flows, components, and systems
Phase 3: Integrating AI
Creating a platform ready to support evolving GenAI use cases
Iterative Discovery
Rapid ideation and testing in a safe MVP environment to explore new features and validate direction
As the sole UX designer for the MVP, I led all design sprints in partnership with PMs and developers, delivering iterations of core screens, prototyping rapidly, and grounding decisions in real-time user behavior.
The platform was in urgent need of improvement, so we launched fast-impact features while uncovering deeper problems for Phase 2.
What I Designed
Core Page Iterations: Landing, Product Detail, Search Results
0→1 Features: Chat Interface, Bookmarking
Testing a Chatbot Assistant to Address Frequent Data Marketplace Queries
I evaluated and iterated multiple approaches (FAQs, wizards, and support pages) but a chatbot offered the best balance between scalability and user needs. It helped streamline answers to the most frequently asked questions by role, improving clarity and reducing time spent navigating.
Introducing Bookmarks as a First Step Toward Personalization
Most users returned to the same datasets regularly. I introduced bookmarks on product cards and key pages to help users easily access what mattered to them. It replaced underused elements (like duplicate glossary links) and laid the groundwork for deeper personalization.
Re-design
Redesigning key flows, core screens, and system foundations for clarity, usability, and scale
Redesigned Core Screens
Landing Page, Product Details, Search Results
Introduce Role-Aware Flows
My Assets, My Profile, Notifications, Subscription, Wizards
A role-aware, personalized homepage to surface relevant insights and drive faster decisions
Why It Was Needed
The legacy homepage was static, cluttered, and ignored user intent. No differentiation for roles, no clear CTAs, and irrelevant cards meant users often hit dead ends.
What I Designed
We restructured the homepage to prioritize action. CTAs were clarified, irrelevant modules removed, and sections tailored by role. The result: less noise, more relevance, and faster access to key data.
A redesigned product card layout to surface trust signals, key metadata, and dataset context at a glance
Issues
The original card layout included elements that didn’t support decision-making, such as irrelevant icons, unused review stars, and inconsistent CTA colors. Key information like access status was missing, and the design created visual clutter, making it harder for users to quickly scan and act.
What I Designed
As part of the core user experience redesign, the card was restructured to prioritize clarity and utility. Unnecessary elements were removed, the CTA color was standardized to purple, and space was optimized by limiting title length. New features like access status, bookmarks, and notifications were introduced to support personalization and improve user efficiency.
My Assets: A dedicated space for users to manage published and favorited assets in one place
Why It Was Needed
There was no way for repeat users to keep track of what they used. Search became repetitive. Users often accessed the same 5 - 10 products regularly.
What I Designed
I introduced the My Assets page, a centralized place to manage published and favorited data products. It reduced search reliance and cut access time significantly.
Integrating AI
Designing for Smarter Discovery Before AI Infrastructure Was Ready
We couldn’t wait for AI to catch up. The existing platform was losing users, and backend upgrades would take time. So, we designed scalable AI-ready features in phases. These upgrades ensured the platform stayed relevant, even as tech and expectations continued to evolve.
Introducing natural language search to improve dataset discoverability and reduce friction
Legacy: Search required exact dataset names, making discovery rigid and prone to failure.
Redesigned: Added tag-enhanced filters and helper text to reduce failed queries and surface metadata.
Future State: Designed a GenAI assistant that supports conversational queries and task automation, like requesting access or exploring usage insights directly from the search bar.
Dynamic homepage cards tailored by role, behavior, and usage to surface what matters most
Legacy: Static “popular this week” cards showed generalized content irrelevant to individual roles.
Redesigned: Role-based collections introduced relevance by department (e.g., Finance).
Future State: AI-driven cards tailored by role, past usage, company events, and search behavior, providing dynamic, high-impact content on login.
Design System
Standardizing components and patterns to ensure consistency and accelerate future development
Why It Was Needed
Teams across the Data & AI Studio were using inconsistent design systems, leading to fragmented UI, accessibility issues, and inefficient handoffs. An audit revealed problems like misaligned padding, color mismatches, and complex variants.
What I Designed
Collaborated with global teams to create a unified, responsive design system that supported diverse product needs while ensuring accessibility and consistency. This became the foundation for the Accenture Design System Playbook.
Colors
Typography
Icons
Accordions
Buttons
Filters
Cards
Tables
Alerts
KPIs
Navigation
Charts
More
Takeaway
Less is More - in enterprise UX, the real challenge isn't designing new components, but ruthlessly simplifying existing ones. While stakeholders will always demand new features, our real value comes from designing less: removing the unnecessary, consolidating the redundant, and fighting for only what truly matters to users.
Cross-functional collaboration was key - Aligning designers, developers, and stakeholders ensured solutions were both usable and scalable.
Stakeholders speak different languages - Learned to translate UX findings into business metrics that resonated with different leaderships.
Hear From My Team
More
Due to NDA restrictions, the visuals from my work at Accenture are limited and masked. Feel free to reach out for more details.