How might we help portfolio and project managers identify and resolve Data Quality Errors in Planisware without navigating across multiple disconnected dashboards?


Instead of redesigning individual reports, I reframed the problem around the user's investigation journey.

The goal wasn't simply to visualize more data - it was to reduce the effort required to make informed decisions.

Understanding the Problem (SME Workshop)

What we learned


Rather than beginning with interfaces, I started by understanding how SMEs interpreted and investigated data quality issues.

Conversations with subject matter experts revealed that the challenge wasn't interpreting charts - it was navigating between them.


Some recurring themes included:


💬 "I never know which dashboard to start with."

💬 "Finding the root cause takes longer than identifying the issue."

💬 "Everything feels equally important."

💬 "By the time I find the right metric, I've already lost context."


Rather than treating these as isolated usability issues, I translated them into design opportunities.


Monitoring and investigation

are two different tasks.

WORKSHOP INSIGHTS

Every team interpreted

dashboard health

differently.

WORKSHOP INSIGHTS

Dashboard structure reflected

system architecture rather

than user workflows.

WORKSHOP INSIGHTS

The Problem Statement

Setting the Context

Planisware is used by portfolio and project managers to manage project and portfolio data across the organization.


However, inaccurate or incomplete data can create Data Quality Errors (DQEs) that affect planning, reporting, and decision-making.


To monitor these issues, users relied on multiple dashboards that had evolved over time. While each dashboard surfaced valuable information, together they created a fragmented experience that made it difficult to understand the overall health of project data or prioritize what needed attention.

The opportunity wasn't simply to redesign dashboards—it was to create a single experience that helped users identify, understand, and resolve DQEs more efficiently.

Restructuring the Information Architecture & User Flow

Key UX Improvements

The information architecture transforms three independent dashboard sections into a connected investigation experience. Information is organized progressively—from portfolio-level health to detailed DQE analysis and resolution—ensuring every layer builds naturally on the previous one.

  • Previously, remediation guidance was repeated alongside every Data Quality Error, creating a dense, spreadsheet-like experience that made project records harder to scan. By moving remediation into a persistent side panel, users can reference corrective actions on demand while keeping the data table focused on investigation.

    Reducing Information Redundancy

  • The dashboard is designed to reveal information progressively, allowing users to move naturally from monitoring overall portfolio health to investigating project-specific Data Quality Errors. By surfacing only the information needed at each stage of the workflow, the experience reduces cognitive load, improves focus, and enables more efficient decision-making without overwhelming users.

    Progressive Disclosure

  • The redesigned dashboard establishes a clear visual hierarchy by surfacing critical KPIs and summary insights first, allowing users to quickly assess overall portfolio health before exploring detailed analytics. By prioritizing the most actionable information upfront, the experience improves scanability, reduces cognitive load, and helps users focus on Data Quality Errors that require immediate attention.

    Prioritizing What Matters First

Lets continue the Conversation?

This case study captures the highlights, but every decision was shaped by workshops, stakeholder feedback, and multiple iterations. Feel free to reach out if you'd like to dive deeper into the process.

Start

Open the DQE Dashboard to monitor Data Quality Errors across projects and portfolios.

Executive Summary

Review the overall health of project data using high-level KPIs and filters.

Overall Project Health

Overall DQE health across portfolios.

Filters & Scope

Region, Portfolio, Segment and Time.

Navigation

Executive Summary, Error Analysis and Error Resolution.

Dashboard Outcome

Provide a unified experience to monitor, investigate and resolve Data Quality Errors across Planisware

Error Analysis

Retrieve insights across regions, portfolios and DQE rules.

Error Resolution

Retrieve project-level records, backlog status and SLA information for investigation.

Pattern Analysis

Analyze error trends and identify recurring DQEs.

Root Cause Analysis

Analyze failed rules and determine the source of Data Quality Errors.

Investigation Insights

Highlight regions, rules and projects requiring attention.

Resolution Priorities

Identify projects requiring immediate corrective action.

Supply Chain

Reimagining Data Quality Analytics

Making data quality inconsistencies easier to act on.

Role : UX Designer

Timeline : 4 Weeks

Team: PM + SMEs

Tools & Skills: Figma • Power BI•

UX Research • UI Design • Prototyping

TL;DR

The challenge

Portfolio and Project Managers relied on multiple disconnected dashboards to monitor Data Quality Errors (DQEs) in Planisware. The fragmented experience made it difficult to identify which projects required attention, understand the root cause of issues, and prioritize remediation.


My role

As the UX Designer, I led the end-to-end redesign - from understanding stakeholder workflows and auditing legacy dashboards to defining the information architecture, designing the experience, and iterating with cross-functional teams.


Outcome

Consolidated multiple dashboards into a single, scalable experience that helps users monitor project data health, investigate DQEs more efficiently, and navigate from high-level portfolio insights to project-level details without losing context.


Note: Business terminology, metrics, and visuals have been anonymized to protect confidential information.

XOXO

Tan <3

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