Helping data teams see the quality and shape of their data before they ever build on it.
Overview
Product
Data profiling inside the Fosfor Decision Cloud, the suite that turns data into decisions.
Problem
Teams could not easily judge the quality or structure of a dataset before analysing it, so flawed data slipped through.
Details
UI and UX design across research, structure, and visual design over four months in Figma.
Solution
A clean profiling interface that computes statistical metrics on any data model and surfaces issues at a glance.
Result
A faster, more trustworthy first step in the data to decision workflow.
Impact
4 months
From research to final design
2
Profiling depths, basic and advanced, on any data model
[real number]
Reduction in time spent on manual data quality checks
Context
Fosfor is LTIMindtree's data and decision platform, a suite that helps organisations move from raw data to confident decisions. Data profiling is the step that comes first, the work of checking whether a dataset is even worth trusting.
It computes statistical metrics across a data model to expose structure, quality, and anomalies before anyone builds analysis on top of it. My job was to make that step fast, legible, and built into the flow rather than a chore done by hand.
Problem
Before this, understanding a dataset's quality meant slow manual inspection, which was time consuming and easy to get wrong. Teams carried flawed data into analysis without knowing it, and wrong data quietly became wrong conclusions.
Goals
A profiling step so clear that no one builds on bad data without knowing it.
- 01
Compute statistical metrics on any data model, basic and advanced
- 02
Surface data quality issues at a glance
- 03
Cut the time spent on manual data checks
- 04
Fit profiling into the existing decision workflow rather than bolting it on
Process
Research and analysis
Studied the problem space, profiling patterns, and how competing tools handled the same job.
Information architecture
Structured the profiling views around how analysts actually move through data.
Wireframing and prototyping
Explored layouts and validated the flow before visual design.
Usability testing
Put prototypes in front of users and refined from their feedback.
Visual design and style guide
Aligned the final design to the Fosfor design system for consistency.
Key insights
01
Data lived in silos across databases, spreadsheets, and APIs, so the first job was a single unified view.
02
Quality issues, missing values, and inconsistencies were the real source of bad decisions, not the analysis itself.
03
Raw data without actionable insight just slowed people down, so every metric had to point to a next step.
Solution
A clean, clutter free profiling interface that turns any data model into a clear read on its own quality.
Design
Profiling overview
A single view that computes and presents statistical metrics across a whole data model, replacing manual inspection.
Design
Basic and advanced profiling
Two depths of profiling, so users can take a quick read or go deep without leaving the flow.
Design
Issue surfacing
Quality issues, anomalies, and missing values flagged where the eye lands first.
A selection of profiling and data quality views.
Before and after
Before
After
Manual inspection beside the profiling view, the same checks made fast and legible.
Good decisions start with data you can trust, so the profiling step had to earn that trust before anything else.
Reflection
This project sharpened how I think about enterprise tools, where the win is rarely a flashier screen and almost always less friction in a critical step.
If I were extending it, I would track whether faster profiling actually changed how often teams caught bad data before it reached analysis, since that is the outcome that matters.
The first step in the data to decision journey, finally worth trusting.