Amazon QuickSight is a scalable, serverless, machine learning (ML)-powered business intelligence (BI) solution that makes it simple to connect to your data, create interactive dashboards, get access to ML-enabled insights, enable natural language querying of your data, and share visuals and dashboards with tens of thousands of internal and external users, either within QuickSight itself or embedded into any application.
Recently, we launched some new features for tables and pivot tables in QuickSight centered around interactivity and performance. These new features enabled users to alter field visibility, load tables faster, and build consistency across different interactions. In the continuous streak of providing rich user experiences and readability, QuickSight is now introducing data bars for table visual.
In this post, we demonstrate how to use data bars to improve table readability and identify outliers.
Introduction to data bars
Tables are a popular way of organizing and presenting data, but it could be difficult for reading and understanding data, especially in large datasets. One way to make table presentation effective is to provide a visual representation with data bars.
Data bars are essentially bar charts displayed for a given column, where the length of the bar represents each cell value relative to the range of values within the same column. Data bars are very efficient in enabling user focus on outliers and emerging data patterns or trends, especially when dealing with large volumes of data. Data bars improve the readability and navigation of complex tables by integrating tabular data with visualizations. Their visual nature enables quick comprehension and understanding, making them a popular choice for displaying and analyzing data. With QuickSight, you can now use data bars on numeric fields and adjust your color scheme for both positive and negative values individually.
Our use case focuses on AnyHealth Inc., a large hospital corporation in the US. They manage different hospitals across different regions of the country. As part of their analytics requirements, they want to be able to quickly find outliers and determine health economics outcomes. They use QuickSight for their visualizations. With the recent addition of data bars to the available table visuals, AnyHealth can get these insights with ease. Not only that, they can also get the information by reading through the cells. With data bars, they are instantly able to identify the outliers visually, identify values that significantly deviate from rest of the data, and monitor emerging trends. With data bars, understanding and reading the tables has been a breeze.
In the following sections, we examine two use cases using data bars in QuickSight.
Identify outliers with data bars visually
To add a table visual to the analysis with data bars, we create a table visual with at least one metric in the Values field well. In this example, we create a table to load profits across various hospitals and categories. The following screenshot shows our initial data.
Complete the following steps to configure a visualization:
- On the table visual, choose the pencil icon to open the Format visual navigation pane.
- In the navigation pane, expand the Visuals drop-down menu and choose ADD DATA BARS.
- For Value field, choose Profit. By default, data bars are configured for two colors: green for positive values and red for negative values.
Note: Data bars are applicable only on the Values field of the visual.
- To further configure these colors, choose the paint bucket icon and choose your preferred color.
- Close the Data bars menu.
The data bars visualization now appears in the table and an instant outlier can be identified at South Hospital in
Ante/Post Partum category.
Display various metrics on the same scale
AnyHealth often has several metrics that they want to visualize and compare side by side, sliced by a single dimension on a same metric scale. For this use case, they want to visualize
price sliced by the
Hospital dimension. Having all these metrics on the same scale is challenging because the numbers vary greatly. With data bars, AnyHealth was able to achieve this in a very simple and clean way, which enabled them to show their data without additional calculations.
The following screenshot shows the example implementation.
In this post, we looked at the data bars feature in QuickSight, its various use cases, and how to configure them. With data bars, you can analyze and quickly scan a table to see the values of a cell. Furthermore, you can use data bars to identify outliers visually that deviate from the rest of the data. Data bars can be very powerful when it comes to understanding and reading data in tables. Start using data bars to enrich your dashboards’ current visualization and unlock new business use cases today!
If you have any questions or feedback, please leave a comment.
For additional discussions and help getting answers to your questions, check out the QuickSight Community.
About the authors
Bhupinder Chadha is a senior product manager for Amazon QuickSight focused on visualization and front end experiences. He is passionate about BI, data visualization and low-code/no-code experiences. Prior to QuickSight he was the lead product manager for Inforiver, responsible for building a enterprise BI product from ground up. Bhupinder started his career in presales, followed by a small gig in consulting and then PM for xViz, an add on visualization product.
Raji Sivasubramaniam is a Sr. Solutions Architect at AWS, focusing on Analytics. Raji is specialized in architecting end-to-end Enterprise Data Management, Business Intelligence and Analytics solutions for Fortune 500 and Fortune 100 companies across the globe. She has in-depth experience in integrated healthcare data and analytics with wide variety of healthcare datasets including managed market, physician targeting and patient analytics.
Srikanth Baheti is a Specialized World Wide Principal Solution Architect for Amazon QuickSight. He started his career as a consultant and worked for multiple private and government organizations. Later he worked for PerkinElmer Health and Sciences & eResearch Technology Inc, where he was responsible for designing and developing high traffic web applications, highly scalable and maintainable data pipelines for reporting platforms using AWS services and Serverless computing.