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Data Quality
Data Quality Xpert
AI-enabled data quality operations that align business priorities with technical fixes.
An internal platform built to help data teams detect, prioritise, and resolve quality issues faster. It connects business impact (KPIs, criticality) with technical workflows (rules, pipelines, remediation) so teams act with confidence.
Role
UX Researcher
Product - Service Designer
Team
Design Director
Data and AI Director
Data and AI Lead
Business Analyst
Developers
Overview
Figma
Figjam
Google Suits
Timeline
2023 - 10 weeks
Full Time
Overview
Data Quality Xpert is an AI-enabled enterprise platform designed to help organisations move from reactive data issue handling to proactive, business-aligned data quality management. By unifying monitoring, prioritisation, remediation, and impact visibility, the platform enables data teams and business leaders to act faster, with clarity and confidence.
Problem
Enterprise data teams operate in fragmented environments with multiple tools, pipelines, and reporting systems.
Problem areas:
Reactive workflows: Issues are addressed after failures instead of being prevented
Low business visibility: Leaders lack clarity on which data issues truly impact decisions
Delayed action: Critical fixes take longer due to misaligned priorities
Solution
Data Quality Xpert reframes data quality as an end-to-end service rather than a set of isolated tools.
As a result:
Unified workflow: Connects detection, prioritisation, remediation, and monitoring in one place
AI-guided decisions: Uses AI insights and business context to guide next best actions
Focused execution: Helps teams prioritise what matters most instead of firefighting
Better collaboration: Aligns data teams and business stakeholders around shared outcomes
Research
The research phase combined market analysis with qualitative user research to understand how data quality is currently managed across enterprise environments, and where existing tools and workflows fall short in supporting both technical and business users.
Given the scope and timeline of the project, the focus was on identifying patterns, gaps, and opportunity areas rather than producing exhaustive documentation.
Market Research
Conducted a comparative review of established enterprise data quality platforms The analysis was informed by hands-on exploration of available products, as well as walkthroughs from documentation and YouTube tutorials to understand real usage patterns and system capabilities.
User Research
User Research included interviews with data stewards, engineers, analysts, and business stakeholders who regularly use data quality platforms to understand their workflows, challenges, and expectations.
Define
User Persona
Insights from the interviews were synthesised into key user personas representing distinct roles across the data quality workflow.
Data Steward
Ensures data accuracy, consistency, and trust across business reporting.
Data Engineer
Builds and maintains data pipelines to enable reliable, scalable data flows.
Data Scientist
Analyses complex datasets to generate insights and support data-driven decisions.
C-Level / Lead
Use data quality signals to guide strategy, governance, and operational priorities.
Insights
Desire for Real-Time Data Insights
“It takes too long to know if a fix actually works, I shouldn’t have to wait for hours to see results.”
Fragmented Workflows and Tool Sprawl
“I feel like I’m always patching issues instead of solving them proactively.”
Low Visibility and Misaligned Expectations
“Even when the data is fixed, I’m never really sure it’s the right data to base a decision on.”
Rising Pressure for Accuracy and Compliance
“Every time there’s a new report or regulation, it feels like we’re starting from scratch.”
Journey Mapping
To design Data Quality Xpert, end-to-end data quality journey was mapped across all key personas. The journey map helped in understanding how responsibilities, decisions, and information flow across roles, and where support, visibility, or guidance was missing at critical moments.
Design
With the workflows and personas defined, the platform experience was designed. To ensure speed, consistency, and scalability across multiple dashboards and complex data workflows, creation of a UI component library from scratch was done, aligned to role-based needs and enterprise usability.
Design System
Component library in Figma was built to maintain consistency across the platform and accelerate delivery within an Agile timeline.
Key Features
Persona-Centric Dashboards
Role-based dashboards support both strategic oversight for business leaders and detailed operational tracking for analysts and project managers.
Customisation and Personalisation
Users can customise rules, KPIs, objectives, and tags to fit their workflows, while built-in collaboration tools support shared ownership, task assignment, and data governance.
Data Visualisation and Insight Delivery
Complex data is translated into intuitive visualisations that help users quickly identify trends, anomalies, and critical issues.
AI Powered Guidance and Suggestions
AI supports decision-making by recommending validation rules, optimising workflows, and prioritising actions based on business impact.
