Using Amazon QuickSight for Cloud BI with ML Capabilities 

Forty-six of the 50 top telecasts of 2017 were the NFL games (National Football League) with an average of 15.8 million viewers through the season. What made it very interesting was the kind of statistics about the game and the players NFL was able to present using an advanced system to collect data and draw insights from it.  

NFL uses Next Gen Stats (NGS), a sophisticated tracking technology to collect data about the players in its 32 teams, playing in different stadiums at any given moment. It uses RFID devices embedded in the shoulder pads of the players to identify the location of the player within inches and calculate the speed and direction in which they are moving. This wealth of information shared through its multiple media partners hooks its more than 180 million fans worldwide. NFL partners with Amazon Web Services (AWS) to leverage its data using QuickSight for generating sophisticated analytics and machine learning models to interpret the gameplay, enthrall viewers, and improve the quality of the game.  

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Amazon QuickSight is a cloud-based, machine learning-powered business intelligence tool that uses natural language for exploring data using interactive dashboards. It helps business users identify patterns and outliers through relevant visualizations without the need for IT admins or experts to generate charts and graphs. It breaks silos by accessing data stored in different sources to provide a holistic view.

QuickSight Features

One of the key features of QuickSight is ML Insights, which enables advanced data analytics solutions for deeper insights by leveraging AWS’s machine learning (ML) and natural language capabilities. Using these powerful, out-of-the-box features, users can identify hidden trends and outliers, determine key business opportunities, and forecast trends to accelerate growth. It does not require technical know-how or ML experience to use the tool, thus empowering business users with the tool to generate their insights quickly and easily to make timely decisions. 

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Contextual visualization is made possible by QuickSight’s machine learning capabilities that assess the meaning and relationships between the different business data. This helps improve the accuracy of the response to queries, supported by relevant visualizations. 

The tool can be trained for different functions and using the language and terms relevant to your organization. This helps to customize and increase the relevance of the insights. Machine learning also helps to improve the answers to the questions over time. 

Amazon QuickSight allows the seamless embedding of interactive dashboards and visualizations into applications. This helps to personalize the user interface for reports and dashboards. This also allows for a consistent user experience across the application. 

The QuickSight Enterprise Edition allows the embedding of rich, interactive dashboards in wikis, apps, and portals without the need to call embedding APIs. It does not need server deployments or infrastructure licensing, allowing authorized end-users to access the dashboards instantly. With the QuickSight Enterprise Edition, custom-branded reports can be shared over emails to increase credibility.  

Ad-hoc analysis and ML capabilities help with detecting anomalies, forecasting trends, and customizing natural language queries to generate dashboards and reports. This also increases avenues for differentiating and monetizing your applications. 

Being serverless, QuickSight analytics workflows can be managed programmatically, be it setting up a multi-tenant architecture to isolating data between multiple users, automating deployments, shifting dashboards across accounts, or managing access for users with Single Sign-On (SSO).

Benefits of QuickSight

Some of the key benefits of QuickSight include

Scalability: Using robust AWS APIs, developers can deploy scalable embedded analytics for thousands of users.  

Collaboration: Being cloud-based, QuckSight allows data visualization and insights to be shared across the organization using the web, email, mobile, or embedded applications. 

Consistency: Admins can ensure consistent, scalable performance. Periodic updates help improve performance without version conflicts, any downtime, or compatibility issues.  

Cost-Effective: QuickSight’s pay-per-session pricing makes it a cost-effective solution even for large-scale deployments.  

Forecast Trends: Interactive, point-and-click what-if analysis using business metrics helps businesses predict future trends and make informed decisions. 

Auto-Narratives: Customizable auto-narratives that can be woven into dashboards provide deeper context for improving actionable insights. 

Self-Service: End-users can generate their own reports with technical know-how to accelerate the speed of decision making 

Simple Data Pipelines: With Amazon SageMaker integration. sophisticated ML can be integrated to generate models without needing complex data pipelines 

Compliance: The solution provides end-to-end data encryption for data and encryption at rest for data in SPICE, ensuring security, governance, and compliance. 

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Use Cases of Amazon QuickSight

Some of the use cases of Amazon Quicksight include doing sentiment analysis to understand customer views and customize products and solutions to improve customer delight. It can help create focused campaigns and targeted promotions to increase sales.  

The forecasting ability can help predict demand and manage production schedules effectively to reduce costs, and wastages, and improve profitability. It can help with increasing operational efficiency by identifying areas of improvement and strengths. 

QuickSight can be used across industries and in organizations of any size. It is a next-gen solution that can scale as the company grows, thereby protecting investments and increasing RoI. 

Indium Approach to QuickSight

Indium Software is an Amazon partner that has a team of experts in AWS solutions including QuickSight to help businesses break barriers to innovation. Our team of data engineers have experience working across industries and across functional areas, which helps to develop solutions that incorporate best practices for optimum result. 



Author: Indium
Indium is an AI-driven digital engineering services company, developing cutting-edge solutions across applications and data. With deep expertise in next-generation offerings that combine Generative AI, Data, and Product Engineering, Indium provides a comprehensive range of services including Low-Code Development, Data Engineering, AI/ML, and Quality Engineering.