Next-Gen Data Catalog

Problem Statement:

Enterprise users often struggle with inefficient data discovery and understanding, leading to delays in decision-making and inaccurate insights. This problem stems from several key factors:

• Difficulty in locating relevant data assets quickly.
• Inconsistent metadata leading to confusion and errors.
• Lack of understanding of complex data structures and KPIs.
• Inefficient collaboration across teams due to silos of information.

These challenges hinder the ability of data professionals and business users to make timely and accurate decisions, impacting overall organizational efficiency and productivity.

Objectives and Goals

• The primary objective of PowerMe is to streamline data workflows and enhance data literacy across organizations. We aimed to achieve this by:
  • Automating data cataloging to ensure all relevant data assets are readily accessible.
  • Providing an intuitive search and discovery interface for easy navigation and retrieval.
  • Consolidating technical and business metadata into a single, accessible view.
  • Leveraging generative AI to enrich metadata, improving data literacy and understanding.
• The success of PowerMe was measured based on the following KPIs:
  • Reduction in time taken to locate relevant data assets.
  • Increase in user satisfaction with the data discovery process.
  • Improvement in user understanding of data assets through metadata enrichment.

Design Thinking

1. Empathize: We conducted interviews with enterprise users, data analysts, and BI professionals to understand their pain points:

• Difficulty locating relevant assets (reports, dashboards, datasets).
• Uncertainty about data quality and how data had been transformed.
• High-time investment in validating the accuracy and relevance of assets.

2. Define: Enterprise users need a way to quickly discover and trust high-quality BI & analytics assets to streamline their decision-making processes.

3. Ideate: Based on insights from user research, we brainstormed several potential solutions, including:

• A unified search and discovery system for BI assets.
• Visual data lineage mapping to track data transformations.
• Data quality indicators at the point of asset consumption.

4. Prototype: We developed a prototype for PowerMe with the following key features:

• Searchable Catalog: A centralized interface for users to find all relevant BI & analytics assets.
• Data Lineage Visualization: A visual representation of data flow and transformations, ensuring transparency.
• Quality Assessment Indicators: Built-in data quality metrics displayed alongside assets, giving users immediate confidence in their reliability.

5. Test: We conducted usability tests with diverse enterprise users.

Mockups

Key findings:

• Users found the visual lineage feature reduced time spent validating data.
• Data quality indicators built trust and sped up decision-making.
• The intuitive catalogue interface enabled the faster discovery of assets.

Result:

• Users were able to locate relevant data assets 30% faster compared to their previous workflows.
• 85% of users reported a better understanding of data assets due to AI-generated descriptions and similarity insights.
• Teams experienced a 20% increase in collaborative efforts due to the unified view of metadata and enriched explanations.