UX Design Case Study: 1Clearsense Data Quality & Potential Duplicates Report

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Project Overview

Goal: To design a user-friendly and informative report that effectively communicates both data quality metrics and potential duplicate data within the 1Clearsense platform.

Target Audience: Data analysts, data scientists, and data quality professionals.

Challenges

• Complex data: Potential duplicates can involve various data attributes and matching criteria, while data quality metrics can be abstract and challenging to interpret.
• Large datasets: Reports may need to handle significant volumes of data.
• Technical terminology: The concepts of data duplicates and data quality metrics can be unfamiliar to some users.

Design Approach

1. User Research:
• Interviews: Conduct interviews with target users to understand their needs, pain points, and expectations.
• Surveys: Gather feedback on existing data quality reports and identify areas for improvement.
2. Information Architecture:
• Organize data: Structure the report to present data quality metrics and potential duplicates in a logical and intuitive manner.
• Prioritize key metrics: Highlight the most important metrics for the target audience.
3. Visual Design:
• Clear and concise: Use simple and effective visualizations to communicate complex data.
• Consistent branding: Adhere to the 1Clearsense brand guidelines to maintain a cohesive experience.
4. Interaction Design:
• Filtering and sorting: Allow users to filter and sort data based on various criteria.
• Drill-down functionality: Enable users to explore specific data quality issues or potential duplicates in more detail.
• Export options: Provide options to export data in different formats (e.g., CSV, Excel).

Key Features

• Interactive data table: A sortable and filterable table displaying potential duplicates with relevant attributes.
• Data visualization: Charts and graphs to visualize patterns and trends in duplicate data and data quality metrics.
• Similarity score: A metric indicating the level of similarity between potential duplicates.
• Data quality scorecard: A comprehensive overview of overall data quality across different dimensions.
• Contextual information: Additional details about each potential duplicate, such as source systems or data quality rules.
• Actionable insights: Recommendations for resolving duplicates or improving data quality processes.

Design Outcomes

• Improved user experience: The report provides a clear and intuitive way to identify and analyze both data quality issues and potential duplicates.
• Increased efficiency: Users can quickly identify and address data quality problems and potential duplicates.
• Enhanced data accuracy: The report helps to ensure that data is clean, consistent, and free from duplicates.

Example Visuals

By combining data quality metrics and potential duplicates into a single report, the 1Clearsense Data Quality & Potential Duplicates Report can provide a comprehensive overview of data health and empower users to make informed decisions about data management and improvement.