Comparison Visualisation Tool - Enabling Data-Driven Decisions at Scale

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Comparison Visualisation Tool - Enabling Data-Driven Decisions at Scale

role

Product Designer
(Individual Contributor)

deliverables

User Research & UX Strategy, UI/UX Design, Prototyping & Interactions

timeline

Apr 2025 - May 2025

the
challenge

In our IoT platform, a long-standing limitation was the inability to view or compare asset-level data across locations. The dashboard only provided high-level summaries or derived metrics, which made operational analysis inefficient and error-prone. Users relied on manually exported reports to compare asset data - a tedious and time-consuming process prone to errors. There was no in-app mechanism to compare across parameters, stores, or time ranges. Users lacked visibility into asset-level insights, making it difficult to identify inefficiencies, spot anomalies, or benchmark performance.

users

Store Managers - overseeing a single store with ~20 IoT assets, needing to track performance and alerts daily.

City/Zone Managers - responsible for asset health across multiple stores in a city or a zone within.

Operations Heads & CXOs - looking at high-level comparisons across geographies and business units to identify trends, bottlenecks, and opportunities.

role

Led the end-to-end product design from user research, UX flows, and interaction patterns to the final high-fidelity UI and dev handoff. The tool was built as an interactive prototype in Figma using variables and advanced prototyping features, closely simulating real user behaviour for internal testing and alignment.

constraints and challenges

The interface needed to handle high data density without overwhelming users.
Layout had to be responsive and usable across desktops and tab devices.
Balancing customisation and simplicity was key, users could compare multiple assets, toggle metrics, switch chart types, and apply filters without friction.
Seamless performance was essential, despite the complexity of data combinations and interactions.

process & exploration

Spoke with key user groups, store managers, city managers, and ops heads to understand their current workflow. Most users relied on exported Excel sheets to manually compare data, often cross-referencing alerts, consumptions, and downtime across multiple assets or locations. This process was time-consuming and error-prone, especially when comparing more than 5-6 assets at once and comparing across cities or pan India was a very big pain.

Exploration began with selecting assets from a tabular list. Designed a dual-pane layout as our majority users were used to excels and tables and in terms of making this tool generic to use with any other module, table of assets/groups were done as the left pane and right was given for visualisation options.

prototyping and testing

A fully interactive prototype in Figma, leveraging variables and nested components to simulate data point changes, layout updates, filters, different visualisations, trigger errors for invalid combinations and micro interactions for empty states.

This enabled realistic demos to both product and engineering teams, helping validate the experience early and reduce design-to-dev churn. Also internal testing taking early feedbacks from closed testing.

This project not only solved a long-standing user pain point, but also positioned the platform as a more strategic operational tool, moving from data display to data intelligence.

While the tool is still in internal testing, initial feedback has been extremely positive.

- Teams reported a 5x increase in asset-level data exploration compared to the legacy approach.

- Store managers could identify anomalies, patterns within minutes, something that earlier required manual effort and Excel.

- Product and customer success teams plan to roll this out across regions, with expectations of reducing manual report dependency by over 60%.