The Need for a Data Dictionary

Oct 13, 2025 by recoveryshake.com 61 views

Introduction

In the world of data exploration, having a clear understanding of the entities and attributes involved is crucial for making informed decisions. However, with the increasing complexity of data, it can be challenging to keep track of the various fields and their definitions. To address this issue, we propose the implementation of a data dictionary UI that allows users to display definitions for all entities and attributes. In this article, we will delve into the need for a data dictionary, outline the approach to its implementation, and provide a technical design and implementation plan.

The Need for a Data Dictionary

The current tables and filters in our system have an optional definition describing the fields. However, this is limited to individual fields and does not provide a comprehensive view of the entities and attributes involved. A data dictionary UI would enable users to:

List all entities and attributes
Scroll to a specific entity
Filter attributes
Search attributes

This would provide a centralized location for users to access and understand the definitions of all entities and attributes, making it easier to navigate and analyze the data.

Approach

To implement the data dictionary UI, we will follow an iterative approach with the following use cases:

List all entities and attributes: This will involve displaying a list of all entities and attributes in the system, along with their corresponding definitions.
Scroll to entity: Users will be able to scroll to a specific entity and view its associated attributes and definitions.
Filter Attributes: Users will be able to filter attributes based on specific criteria, such as data type or category.
Search Attributes: Users will be able to search for specific attributes and view their definitions.

The first implementation will be for the AnVIL Explorer, using the data-dictionary.json file as the source of the descriptions.

Technical Design

To implement the data dictionary UI, we will use the following technical design:

React Table: We will use the React Table library to display the list of entities and attributes.
Data Dictionary JSON: We will take the data-dictionary.json file as the data source at build time (static build the page).
Re-use existing components: We will re-use any existing components where possible to minimize development time and effort.
Determine additional components: We will determine the additional components required to implement the data dictionary UI.
Search and filter parameters: When filters and searches are implemented, the search and filter parameters will be added to the address bar so that links can be shared.

Implementation

To implement the data dictionary UI, we will follow the following steps:

Experiment with auto-generation: We will use this as an experiment to see how much of the implementation can be auto-generated.
Use AI chat: We will use an AI chat to:
- Help determine the component breakdown
- Write the prompt for each component with inputs, any state, and the link to the Figma section
- Link to the Figma with the Figma MCP client
Start with a small component: We will start with a small component and see if we can get it "right".
Consider separate tickets: We will consider doing the components as separate tickets to facilitate collaboration and tracking.

Benefits of a Data Dictionary UI

A data dictionary UI will provide several benefits, including:

Improved data understanding: A data dictionary UI will provide a centralized location for users to access and understand the definitions of all entities and attributes.
Increased productivity: With a data dictionary UI, users will be able to quickly and easily find the information they need, reducing the time and effort required to navigate and analyze the data.
Better decision-making: A data dictionary UI will enable users to make informed decisions by providing a clear understanding of the entities and attributes involved.

Conclusion

In conclusion, a data dictionary UI is a crucial component of any data exploration system. It provides a centralized location for users to access and understand the definitions of all entities and attributes, making it easier to navigate and analyze the data. By following the approach outlined in this article, we can implement a data dictionary UI that meets the needs of our users and provides several benefits, including improved data understanding, increased productivity, and better decision-making.

Future Work

Future work on the data dictionary UI will involve:

Implementing filters and searches: We will implement filters and searches to enable users to quickly and easily find the information they need.
Adding additional components: We will add additional components to the data dictionary UI to provide a more comprehensive view of the entities and attributes involved.
Continuously improving the UI: We will continuously improve the UI to ensure that it is user-friendly and meets the needs of our users.

References

Figma Design
Data Dictionary JSON
Data Dictionary Q&A: Frequently Asked Questions =====================================================

Introduction

In our previous article, we discussed the importance of a data dictionary UI in data exploration systems. In this article, we will answer some of the most frequently asked questions about data dictionaries and their implementation.

Q: What is a data dictionary?

A: A data dictionary is a centralized location that provides definitions and descriptions of all entities and attributes in a data system. It enables users to quickly and easily understand the meaning and context of the data.

Q: Why do I need a data dictionary?

A: A data dictionary is essential for data exploration and analysis. It provides a clear understanding of the entities and attributes involved, making it easier to navigate and analyze the data. This leads to improved data understanding, increased productivity, and better decision-making.

Q: How do I implement a data dictionary?

A: Implementing a data dictionary involves several steps, including:

Defining the entities and attributes: Identify the entities and attributes in your data system and define their meanings and contexts.
Creating a data dictionary JSON file: Create a JSON file that contains the definitions and descriptions of the entities and attributes.
Implementing a data dictionary UI: Use a UI library such as React Table to display the data dictionary in a user-friendly format.
Adding filters and searches: Implement filters and searches to enable users to quickly and easily find the information they need.

Q: What are the benefits of a data dictionary?

A: The benefits of a data dictionary include:

Improved data understanding: A data dictionary provides a clear understanding of the entities and attributes involved.
Increased productivity: With a data dictionary, users can quickly and easily find the information they need, reducing the time and effort required to navigate and analyze the data.
Better decision-making: A data dictionary enables users to make informed decisions by providing a clear understanding of the entities and attributes involved.

Q: How do I maintain a data dictionary?

A: Maintaining a data dictionary involves several steps, including:

Regularly updating the data dictionary JSON file: Update the JSON file to reflect changes in the data system.
Reviewing and refining the data dictionary UI: Review and refine the UI to ensure that it is user-friendly and meets the needs of users.
Continuously improving the data dictionary: Continuously improve the data dictionary to ensure that it remains relevant and effective.

Q: Can I use a data dictionary with other data exploration tools?

A: Yes, you can use a data dictionary with other data exploration tools. A data dictionary is a flexible and adaptable tool that can be integrated with a variety of data exploration tools and systems.

Q: How do I get started with implementing a data dictionary?

A: To get started with implementing a data dictionary, follow these steps:

Define the entities and attributes: Identify the entities and attributes in your data system and define their meanings and contexts.
Create a data dictionary JSON file: Create a JSON file that contains the definitions and descriptions of the entities and attributes.
Choose a UI library: Choose a UI library such as React Table to display the data dictionary in a user-friendly format.
Start implementing the data dictionary UI: Start implementing the data dictionary UI, adding filters and searches as needed.

Conclusion

In conclusion, a data dictionary is a crucial component of any data exploration system. It provides a centralized location for users to access and understand the definitions of all entities and attributes, making it easier to navigate and analyze the data. By following the steps outlined in this article, you can implement a data dictionary that meets the needs of your users and provides several benefits, including improved data understanding, increased productivity, and better decision-making.