Project Overview

The Consistency Checker is a critical tool within the for:sight platform that uses machine learning to detect potential data anomalies. By referencing historical quarterly and yearly data uploads, it intelligently flags specific cells across large datasets for review. This eliminates the need for users to manually sift through thousands of rows, allowing them to quickly identify and address potential issues.For each flagged item, the tool provides an analysis explaining why the system suspects a discrepancy. Users can then choose to:

Research Objectives

To ensure the redesigned Consistency Checker aligned with user needs, the research phase focused on understanding:

Key Insights

• Overwhelming Interface
  ‍Users found the original UI dense and difficult to scan, especially with nested content and large headers.
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• Lack of Clarity Around Flags
  Many users struggled to understand why certain cells were flagged or what the expected correction was.
• Frequent Scrolling
  Due to large row heights and deep layouts, users had to scroll excessively, impacting workflow speed.
• High Mental Load
  Users had to manually compare flagged data with expected results and prior knowledge with limited visual assistance.
• Desire for Batch Actions
  Users often wanted to approve or edit similar rows in bulk rather than one at a time.
• Need for In-Row Analysis
  Users expressed a strong need to view analysis inline without losing the table’s context or increasing visual clutter

How Research Informed Design Decisions

• Confusing Flagged Logic
  Added inline expandable panels with machine-generated analysis.
• Table Rows Too Deep
  Reduced row height and header size; added density toggle.
• No Bulk Actions
  Introduced multi-row select and batch edit/approve.
• Poor Scanability
  Improved use of colour, icons, and visual cues to quickly distinguish flagged vs. approved data.
• Users Unsure On What To Do Next
  Implemented clearer step-by-step flow and call-to-action buttons.

Design Goals

The original interface was functional but overly complex. The objective was to redesign the experience to enhance usability without sacrificing core capabilities.

Key Goals

• Simplify the interface
  Reduce visual clutter in the header and streamline table complexity.
  Provide a cleaner, more approachable UI for both technical and non-technical users.
• Improve data visibility
  Decrease table depth to display more rows on screen without scrolling.
  Optimise layout for large datasets.
• Enhance flagged data interaction
  Clearly highlight flagged cells with intuitive visual cues.
  Ensure users can immediately identify which rows require attention.
• Contextual analysis and actions
  Introduce collapsible analysis panels within each row to display expected results and system reasoning.
  Maintain a compact row height while ensuring clarity of the analysis feature.
• Introduce new functionality
  Analyse flagged data
  Edit directly within the table
  Approve corrected or verified data
  Add notes for context or documentation
  Adjust row display density
  Select/edit multiple rows for batch updates

Solution