Over the course of 12 months, I led the design and launch of the first 'Data Dictionary' feature within Product Analytics (Adobe CJA) which features a comprehensive catalog of metrics and definitions to help customers make critical business decisions in their workspace.

Overview

TITLE: Data Dictionary
GOAL: Allow users and administrators to keep track of and better understand the components within their Analytics environment
DURATION: 12 Months
MY ROLE: End-to-end process, User Insights, UX/UI Design, Wireframing, Interactive Prototype, Interactive Design, User Testing

The Persona: New Users/Administrators
Analytics administrators are responsible for curating and maintaining information about each component in the Data Dictionary to make it available to users, especially new users.

The Business: Adobe Customer Journey Analytics (Adobe CJA)
- Uncover and track insights
- Guided analysis workflows
- Reporting and analysis; includes any persona, behavioral data sets, and functions as a reporting tool
- Measure, analyze, summarize how users interact with the platform/product
- Create personalized/customized reports to help predict and elevate customer experiences

The Data Dictionary feature is a comprehensive database of metrics, definitions, and information to assist both internal and external users in making assured decisions in the self-sufficient,
Product Analytics workspace.

Concept

Its purpose is to facilitate auditing and curation of information, resulting in improved data quality and user experience.

Components can be searched, sorted, filtered, modified, and removed; as well as dragged and dropped into the workspace.

In our research, our objective was to make the new modal easily findable and to grab the user's attention to participate in daily or regular interaction. Based on our insights, we found that implementing a gamification model proved to be the most effective approach.

The development of the Data Dictionary was a dynamic, non-linear process spanning 12 months. Throughout this period, we engaged in continuous iterations of our designs, maintaining open and consistent communication for feedback with our product managers, engineers and developers, internal and external users, and relevant stakeholders.

While the need for this feature had been in our backlog and apparent since the inception of Adobe Customer Journey Analytics, we wanted to ensure that the data is easily accessible, accurate, and that the project aligned with our objectives. I believe we achieved just that.

Eng Handover Notes

The following changes are to be implemented in the Data Dictionary feature within Analysis Workspace. These improvements aim to enhance data quality, user experience, and overall data management for administrators and users.

  • Dictionary Health Tab

    • The “Dictionary Health” tab is to be added and should only be accessible to administrators. This is the administrative landing page.

    • Implement the “Missing Descriptions” filter to list components that lack descriptions.

    • Integrate the “Duplicates” feature to group components that lack descriptions.

    • Provide the ability for administrators to modify descriptions without disrupting functionality.

  • Quick Filters Tab

    • Introduce a “Quick Filters” tab for users to efficiently navigate and refine component searches accessible to both non-admin and admin. This is the non-administrative landing page.

    • Include filters for “Dimensions”, “Metrics”, “Segments”, and “Date ranges” as specified.

  • Health Tab Details

    • Create the following sections:

      • Display a numerical summary of components that are missing descriptions.

      • Display a numerical summary of components that have duplicate names or definitions.

      • Display a numerical summary of components that have not collected data in the last 90 days. 

      • Display a numerical summary of components that have not been approved.

Functionality

  • The "Dictionary Health" tab should be accessible and visible only to administrators with the relevant permissions.

  • Filters in the "Quick Filters" tab should accurately sort and refine the list of components for users.

  • The "missing descriptions" filter should correctly identify components without descriptions.

  • The "duplicates" feature should group components with identical names or definitions while allowing modification by administrators.

  • The "no recent data" filter should list components with data not collected in the last 90 days.

  • The filters for "Dimensions," "Metrics," "Segments," and "Date Ranges" should accurately sort components based on the respective criteria.

Design and User Interface

  • Maintain the existing design language and layout of the Data Dictionary.

  • Ensure the new tabs and filters are visually consistent with the overall Analytics environment.

  • Use intuitive icons and tooltips to guide users in understanding the purpose of each filter.

Technical Details

  • The underlying logic for the "Missing Descriptions" filter and "Duplicates" feature should accurately analyze component metadata.

  • The "No Recent Data" filter should consider the timestamp of data collection for each component.

  • The "Dimensions," "Metrics," "Segments," and "Date Ranges" filters should match the relevant metadata attributes of components.

Testing and QA:

  • Conduct rigorous testing to ensure all filters function as intended and provide accurate results.

  • Test on various user roles, such as administrators and regular users, to validate access controls.

  • Check for responsiveness and compatibility across different browsers and devices.