Amazon QuickSight

Amazon QuickSight creates Dashboards, a published version of the analysis that can be shared publicly. We can use multiple data sources for the reports. QuickSight uses SPICE (Super-fast, Parallel, In-memory Calculation Engine), a robust memory engine, to process this data. It speeds up the analytical queries executed against data and saves time.

The basic workspace in QuickSight is Data Analysis, which is the collection of visualizations. Each Visualization is a graphical representation of data. Quicksight supports multiple visualization types, including graphs, tables, charts, and diagrams. While creating a visualization, QuickSight recommends some visuals based on the data. The machine learning insights further suggest add-ons to efficiently represent data. These visualizations and insights are displayed on a sheet. QuickSight offers two types of sheets: paginated sheets and interactive reports. We can add multiple sheets or work on a single one to represent data.

Before using the data for analysis, we prepare the data. The data preparation includes changes such as filtering required data, changing field names or types, adding new fields, or creating SQL queries to refine data.

How Amazon QuickSight works

The general workflow to create an analysis is to select a data source. QuickSight asses the data and suggests some of the best visualizations. In addition to the suggested visualizations, we can add our charts, tables, and ML insights, which are resized and arranged on a sheet. This analysis is then used to create a dashboard.

By default, the dashboard is not public and only accessible to the dashboard owner. However, we can share dashboards in the following ways:

• Share dashboards with specific IAM users and groups.

• Share dashboards with specific users and groups or all the users in the QuickSight account. Note that these users or groups are different from the IAM users and groups.

• Copy the link to the dashboard and share it with others.

QuickSight integrations

QuickSight can be integrated with a variety of data sources such as RDS or S3, including AWS database and storage services, third-party SaaS tools such as Salesforce or Jira, JSON data, and custom data files such as .xls or .csv.

QuickSight Q

Quicksight Q is the natural language query feature in Amazon QuickSight. It allows users to ask questions about their data using conversational language rather than writing SQL queries or using complex business intelligence tools. With Quicksight Q, users can simply type or speak their questions in plain language, and QuickSight automatically generates the corresponding visualizations and insights based on the data available in the connected data sources.

This feature leverages natural language processing (NLP) and machine learning (ML) algorithms to understand user queries, interpret their intent, and generate appropriate responses. Quicksight Q helps to democratize data access and analysis by enabling non-technical users to perform ad-hoc queries and explore data insights intuitively without requiring specialized skills or knowledge of query languages.

Overall, Quicksight Q enhances the usability and accessibility of Amazon QuickSight, enabling users to derive actionable insights from their data more efficiently and effectively.

Use case: Performance analysis of a retail company

Consider a retail company that wants to analyze its sales performance across different regions, product categories, and time periods to make informed business decisions and optimize its sales strategy.

To visualize their analytics, they decide to use QuickSight. They can integrate data from various sources, such as transactional databases, CRM systems, and external market data, into QuickSight. To visualize key performance indicators (KPIs) such as total sales revenue, sales growth, average order value, and customer acquisition metrics, they design interactive dashboards in QuickSight.

Furthermore, to aid the non-tech users, they can leverage QuickSight’s ad-hoc querying capabilities to explore data interactively and ask natural language questions using Quicksight Q. These tables and insights can then be shared among stakeholders and teams for collaboration.