Sharaya Naik

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Data Quality

Enterprise_UX
Product_Design
SaaS
AI

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.


MY ROLE

I was the sole designer on this project, embedded in a cross-functional team of data engineers, business analysts, and technical leads. Everything you read here is my work, from scoping the research to shipping the final prototype.

Enterprise data teams were drowning, not in bad data, but in disconnected tools with no unified view. Issues lived in one place, business impact in another, remediation in a third. Nothing talked to anything.

Over 10 weeks I designed an end-to-end platform (0 - 1 product) that connected all three into one coherent workflow, delivered as an MVP.

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.


MY ROLE

I was the sole designer on this project, embedded in a cross-functional team of data engineers, business analysts, and technical leads. Everything you read here is my work, from scoping the research to shipping the final prototype.


Enterprise data teams were drowning, not in bad data, but in disconnected tools with no unified view. Issues lived in one place, business impact in another, remediation in a third. Nothing talked to anything.


Over 10 weeks I designed an end-to-end platform (0 - 1 product) that connected all three into one coherent workflow, delivered as an MVP.

RESEARCH


As the sole researcher,

Finding where the

system broke down

I scoped and ran the entire research phase independently. Where I defined what we needed to learn, selected which enterprise platforms to audit, and recruited and interviewed users across all four roles who would live in this system daily.

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.

RESEARCH


As the sole researcher,


Finding where the

system broke down


I scoped and ran the entire research phase independently. Where I defined what we needed to learn, selected which enterprise platforms to audit, and recruited and interviewed users across all four roles who would live in this system daily.


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.

RESEARCH


As the sole researcher,


Finding where the

system broke down


I scoped and ran the entire research phase independently. Where I defined what we needed to learn, selected which enterprise platforms to audit, and recruited and interviewed users across all four roles who would live in this system daily.

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 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

As the systems thinker,


Failures that happened
in the handoffs


User Persona

Insights from the interviews were synthesised into key user personas representing distinct roles across the data quality workflow.

DEFINE

As the systems thinker,


Failures that happened
in the handoffs


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

As a product designer,


Every role gets exactly
the context they need

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 & 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.

OUTCOME


What changed

Faster response.

The MVP was delivered to the development and business team as a proof of concept. The design demonstrated for the first time how business priority and technical action could live in the same system, giving stakeholders a concrete vision of what faster, aligned data operations could look like in practice.