Reporting Redesign
FintechData Viz2023

Reporting Redesign

RoleLead Product Designer
PlatformDesktop
TeamPO · 2 UX designers (incl. me) · Product designer · 11 devs · 5 QA
What I didResearch · Solutions · Testing · Design system · Interface

01 Intro

A leading Nordic fintech company providing software for financial and personnel management to thousands of daily users. Years of incremental product growth had left the reporting feature as an afterthought — capable on paper, but frustrating enough in practice that users were abandoning it for Excel and external tools.

02 Problem

Retain users by making reporting a reason to stay, not leave

People resorted to other platforms to build reports. Thousands relied on the system daily to manage their finances and personnel data — but frustration with the reporting tool had led many to export raw data and rebuild what they needed in Excel and other external tools instead.

01Filter states

No visual distinction between active and inactive filters — users couldn't tell what was applied without re-checking every time

02Touch targets

Interaction targets were undersized, causing frequent mis-clicks and slowing down actions users performed dozens of times a day

03Data hierarchy

All figures appeared equally important — no visual weight or color coding, making it impossible to spot trends or anomalies at a glance

04Export limits

No way to export selectively — users dumped entire reports and rebuilt the views they actually needed in Excel

03 Approach

Research

A live board through stakeholder meetings captured the backlog of complaints, while user interviews mapped how people actually report — and exactly where they gave up.

Usability audit

A heuristic audit of the reporting tool rated every issue by severity — turning a vague "it's frustrating" into a concrete, ranked list of what to fix first.

The problems, documented

Each issue was written up with a real example from the product, so the whole team could see exactly what broke and why it mattered.

User testing

The redesign was prototyped and put in front of real users across several moderated rounds, iterating until the feedback turned consistently positive.

Synthesis

Findings were affinity-mapped by theme — filters, tables, export, general usability — into a single prioritized list that drove the design.

A new design system

On top of the research, I built a new design system on Material UI — the foundation that kept the redesign consistent and let it scale across the rest of the product.

04 Solution

The redesign
Step 01 / 05

Clearer filters, readable data

Filters now sit horizontally with their details tucked into dropdowns — each one distinct, so there's less scanning and less overload. Interaction targets are larger, data changes are color-coded to surface trends at a glance, and titles plus tooltips mean power users never reach for a manual.

Clearer filters, readable data
01 / 05

05 Impact

Reporting usage+9%Within 2 months post-launch
Avg task completion timeNot tracked · planned to measure
Feature return rateNot tracked · planned to measure

06 Before & After

The clearest way to see the change is side by side — the original Sales report against the redesign.

Before

Original Sales report — dense filters, undersized targets, and a flat data hierarchy.

After

The redesigned report — clear horizontal filters, color-coded data, and interactive, exportable tables.

07 Reflections

What worked

Front-loading research before any design work — usability audit first, then interviews — meant every decision traced directly to a specific user complaint. Stakeholder sign-off was unusually smooth as a result.

What I'd change

I'd define quantifiable success metrics at kickoff. Task-completion benchmarks, before/after heatmaps (e.g. Hotjar), and adoption tracking would have measured the impact far better than the single 9% usage number we could point to.

What I learned

Plan the design work earlier. A preliminary workload estimate and a designer roadmap up front would have saved the time we lost debating approach at the outset — and faster iteration cycles keep momentum.

Next Project

Carbon Data Reliability