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12 Tableau Case Studies to Inspire You in 2024

• Business Intelligence , Cloud Migration

As we venture into 2024, the need for robust data visualization tools has never been more critical. This is where Tableau becomes a game-changer in data analytics and visualization. Migrating to Tableau is not just a step toward better data handling; it’s a leap toward transformative business insights and decision-making.

With its intuitive interface and powerful analytics capabilities, Tableau has been revolutionizing how companies interact with their data. From small startups to global conglomerates, the stories of transformation are as varied as they are inspiring.

In this blog post, we bring you Tableau case studies to inspire you in 2024. Each case study is a testament to the versatility and impact of Tableau, showcasing how different organizations have harnessed this tool to drive growth, efficiency, and innovation. Whether you are considering migrating to Tableau or looking to maximize its potential, these stories will provide valuable insights and actionable lessons.

Tableau Case Study 1: Bentley Motors – Revolutionizing Luxury Automotive with Tableau

Bentley Motors, a paragon of luxury automotive excellence, recognized the imperative for a digital transformation to stay ahead in a rapidly evolving industry. Embracing Tableau self-service analytics marked a pivotal shift in their approach, leading to transformational changes aligned with their future-focused Bentley100 strategy. This strategy, targeting an all-electric vehicle portfolio by 2030, required a data-driven backbone to meet the challenges of an industry in flux.

Tableau’s impact is evident in various facets of Bentley’s operations. In manufacturing, Tableau’s drillable dashboards have revolutionized process management, enabling quick identification and resolution of issues, leading to more efficient production lines. Similarly, in the realm of customer experience, Bentley leverages Tableau to amalgamate and visualize customer data, enhancing personalized customer journeys and interactions. This unified data approach ensures each Bentley customer feels uniquely understood and valued.

Read the full case study on how Bentley Motors uses Tableau self-service analytics to drive transformational change as it looks to a sustainable future.

Tableau Case Study 2: Trajektory – Revolutionizing Sports Data Analytics with Tableau

Trajektory, a Chicago-based sports data startup, embarked on a journey to redefine how sports organizations manage and leverage their data. Specializing in aggregating diverse data sources into a singular portal, Trajektory faced the challenge of effectively visualizing this data to value sponsorship assets accurately and in real-time. Their goal was to provide sports teams with the tools to create comprehensive, insightful reports for sponsors, vendors, and partners, but they lacked the necessary infrastructure to integrate their data with Tableau’s powerful visualization software.

A significant milestone in this partnership was the development of a custom-built portal, integrating Tableau for front-end data visualizations. This integration was pivotal in enabling Trajektory to build and refine their own dashboards, enhancing the final presentations for their clients. The portal provided a secure, single-point access for clients to view all their data, a crucial feature for data-driven decision-making in sports organizations.

Read the full case study on how Trajektory uses Tableau to effectively visualize its data to value sponsorship assets accurately and in real time.

Tableau Case Study 3: Verizon – Enhancing Customer Experience with Tableau

Verizon, a leader in providing broadband Internet, TV, and landline services, embarked on a significant endeavor to enhance customer experience using Tableau. Faced with the challenge of managing vast amounts of data generated daily, Verizon’s Analytics Center of Excellence (ACE) team aimed to optimize operations across various functions like call centers, digital platforms, and dispatch services.

By integrating data from diverse sources like Hadoop, Teradata, and Oracle, Verizon’s ACE team developed over 1,500 Tableau dashboards. These dashboards were essential in ingesting billions of rows of data, thereby reducing customer service analysis time by 50% across multiple teams. This effort led to a remarkable 43% reduction in call volume and a 62% decrease in technical dispatches for certain customer cohorts, significantly enhancing the efficiency of their operations.

Read the full case study on how Verizon uses Tableau to reduce support calls by 43%, enhancing customer experience.

Tableau Case Study 4: Empower Physical Therapy – Streamlining Operations with Tableau

Empower Physical Therapy, a rapidly expanding therapist-led company with 40 locations across the United States, faced a significant challenge in managing and presenting their financial data. Their growth demanded a sophisticated solution for sourcing, integrating, and reporting financial data, particularly given the need to handle sensitive patient information in compliance with HIPAA regulations.

Key takeaways from this case study include the development of HIPAA-compliant Tableau dashboards, the delivery of actionable reports that streamlined office processes, and the successful integration of multi-platform technologies to create reports that underscored the company’s value to its investors.

Read the full case study on how Empower Physical Therapy streamlined its operations with Tableau.

Tableau Case Study 5: Mercado Libre – Fostering a Data-Driven Culture with Tableau

Mercado Libre, Latin America’s leading online retailer, embarked on a transformative journey to establish a data-driven culture across its rapidly growing organization. With over 30,000 employees and operations in multiple countries, the company faced the challenge of upskilling its workforce to harness the power of data effectively.

The solution involved a comprehensive approach to data culture, integrating technology, data collection methods, and cultural changes. A key component was the implementation of Tableau, which saw a 5x increase in adoption with 12,000 active users, 9.4K data sources, and 9.5K workbooks. This significant shift in data analytics capability was instrumental in redefining Mercado Libre’s business processes and enhancing the quality of employees’ working lives.

Read the full case study on how a robust upskilling program empowers transformation to a data culture at e-commerce leader Mercado Libre.

Tableau Case Study 6: Splunk – Enhancing Efficiency and Performance with Tableau Cloud

Splunk, a San Francisco-based leader in data-to-everything platforms, embarked on a strategic migration to Tableau Cloud, driving significant efficiency and dashboard performance improvements. This shift was part of Splunk’s commitment to reducing server administrative burdens and embracing a cloud-first approach, in line with their SaaS-based business model.

The migration to Tableau Cloud was a pivotal decision for Splunk, aiming to alleviate the increasing strain on server capacity and performance due to rapid growth in Tableau adoption. The transition streamlined their data visualization workflows and aligned with their software-as-a-service ethos, reducing the need for dedicated IT operations and data engineering management for the platform.

Read the full case study on how Splunk migrated to Tableau Cloud to eliminate server admin overhead and drive better dashboard performance.

Tableau Case Study 7: NYU Langone Health – Advancing Healthcare with a Data-Driven Culture Featuring Tableau

NYU Langone Health, a top-ranked U.S. hospital, has successfully built a data-driven culture, leveraging Tableau visual analytics to enhance healthcare quality and efficiency. This transformation has been instrumental in improving their national medical school ranking and boosting their NIH research funding portfolio.

By leveraging Tableau visual analytics, the institution has improved its operational efficiency and financial management and elevated the quality of patient care.

Read the full case study on how NYU Langone Health builds a data-driven culture featuring Tableau visual analytics.

Tableau Case Study 8: Cervey – Enhancing Healthcare Technology Solutions with Tableau

Cervey, a provider of technology solutions for pharmacies, Pharmacy Benefit Managers (PBMs), and Long Term Care facilities, faced a significant challenge in deploying Tableau dashboards to their clients. The primary concerns were ensuring HIPAA compliance for sensitive patient data security and developing visually appealing dashboards for enterprise-level customers.

The Tableau dashboards effectively integrated and streamlined daily operational processes, facilitating quick and secure access to essential data for pharmacy transactions and clinical workflows. This enhancement in their software solutions significantly elevated Cervey’s value proposition to their existing customer base and bolstered their attractiveness to potential new clients.

Read the full case study on how Cervey enhanced its healthcare technology solutions with Tableau.

Tableau Case Study 9: Carter’s Inc. – Fostering Data Literacy for Retail Excellence

Carter’s Inc., a leading children’s apparel company based in Atlanta, Georgia, embarked on a significant journey toward digital transformation and data literacy. Facing the immense challenge of managing 50 terabytes of enterprise data and shipping approximately 700 million units annually, Carter’s initiated a strategic shift to modernize its data infrastructure and analytics processes.

To cultivate data literacy across the organization, Carter’s established a Tableau CoE. This initiative focused on improving departmental data literacy, fostering comfort and enthusiasm in using data analytics technology. The CoE provided training and engagement programs, department-focused projects, and one-on-one sessions with data experts. This approach was particularly effective during the COVID-19 pandemic, enabling rapid adaptation to new supply chain and inventory management challenges.

Read the full case study on how Carter’s built a center of enablement with Tableau.

Tableau Case Study 10: UN World Food Programme – Data Literacy Driving Global Food Security

The United Nations World Food Programme (WFP), the world’s largest humanitarian organization, has embarked on a transformative journey to enhance global food security through a robust data-driven approach. Operating in over 120 countries and territories, WFP’s mission to deliver food assistance and build resilience among food-insecure communities is a colossal task, involving the support of over 100 million people annually.

WFP established a Tableau CoE focused on promoting data literacy and analytics usage across the organization. The CoE’s strategy included collaboration, peer leadership, and attracting top talent to drive data literacy. The data literacy program enabled WFP teams to use Tableau dashboards effectively for various projects, including strategic interventions in food-insecure regions.

Read the full case study on how the UN World Food Programme promotes organization-wide data literacy to advance food security worldwide.

Tableau Case Study 11: Mobile Tech RX – Revolutionizing Auto Repair with Data Analytics

Mobile Tech RX, a pioneer in the automotive repair software industry, embarked on a transformative journey to revolutionize the traditional, paper-based estimating and invoicing systems in auto repair and body shops. Founded in 2014, the company recognized the inefficiencies in the auto reconditioning industry and aimed to streamline the process with a comprehensive software solution.

The goal was to replace outdated pen-and-paper systems with a modern, digital solution. Many auto shops lacked organized internal reporting sources and struggled to utilize historical data effectively. Mobile Tech RX successfully launched a professional, well-styled software product, significantly enhancing the operational efficiency of auto repair shops. Since its launch, the Mobile Tech RX app has seen substantial growth in its active user base, becoming a leading solution for internal reporting and analytics in the auto repair industry.

Read the full case study on how Mobile Tech RX revolutionized auto repair with Tableau data analytics.

Tableau Case Study 12: Texas Rangers – Data-Driven Strategies Enhancing Fan Experience

The Texas Rangers, a renowned American baseball team based in Arlington, Texas, has made significant strides in utilizing data to enhance fan experiences and optimize operations. With a history dating back to 1972 and a large fan base, the organization faced the challenge of modernizing its data strategy to maintain competitive advantage and provide exceptional fan experiences.

By leveraging real-time data, the team optimized gameday operations. For instance, they tracked vehicle entries to manage parking efficiently and monitored gate entries during giveaway nights to enhance fan satisfaction.

Tableau was integrated into the Salesforce Service Cloud to address fan queries more effectively. This allowed for better visualization of frequently asked questions and improved communication with fans.

Read the full case study on how the Texas Rangers’ data strategy hit a home run with fans.

Modernize Your Data & Analytics Insights With Tableau and XeoMatrix

Modernizing your data and analytics insights with Tableau is a strategic move that can significantly transform your data management and analysis capabilities. By centralizing data, enabling real-time insights, scaling seamlessly, promoting collaboration, and prioritizing security and governance, your organization can gain a competitive edge and drive innovation in the dynamic digital landscape.

XeoMatrix offers expert assistance in modernizing your Data & Analytics infrastructure. Our team of skilled professionals can guide you through the implementation of a modern data stack, leveraging powerful tools like Tableau to unlock the true potential of your data-driven insights.  Contact us today  to embark on your journey toward data-driven success.

About XeoMatrix

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Top Tableau Case Studies – JP Morgan, Lenovo & Lufthansa

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We know how to work with tableau and what are its benefits. But, do you know where is tableau used in real-world? Which companies are benefiting from tableau? How is tableau solving the problems of industries? Here, we are going to throw some light on the usefulness of Tableau in different sectors and domains of the industry. In this blog, we have provided Tableau Case studies of three companies: JP Morgan Chase, Lenovo, and Lufthansa airlines to uncover the strengths of it.

Let’s get started!

Tableau Case Studies

Below are well known Tableau Case Studies, which are going to help you in practice and interview.

1. JP Morgan Chase & Co.

J.P. Morgan Chase & Co. is a leading multinational bank and financial services company based in the USA. It is the largest investment bank in the US and sixth largest in the world.

The challenge

The amount of data produced by the company increased with increasing growth and expansion of business of the firm because of successful mergers and acquisitions. This led to the need for a robust self-service data governance and analytics solution . Initially, JPMC had an IT-owned analytics set-up which they planned to change into a business-owned system12. The IT department used standard tools like Excel and SQL Server for data analytics and reporting. But these tools were eventually proved inefficient as they caused confusion and obscurity in data governance due to data replication.

After this, the company switched to BI tools Cognos and Business Objects, but those tools too were not able to meet the company’s requirement. What the company needed was a data governance tool taking care of business aspects like data access, data analysis, IT governance, and business priorities of the team .

Implementation

JPMC deployed Tableau as its core self-service data governance tool at an enterprise level. JPMC with the help of COE (The Center of Excellence) team facilitated the process of Tableau adoption to their users by recruiting a team of 8 trainers who trained 1200 new developers and analysts to work on Tableau. The training program involved online as well as classroom learning sessions. The initial user base was 400 Tableau Server users in 2011 which has grown into a family of 30,000 users today.

Skilled Tableau business users work in teams into different departments and sectors of JPMC. Currently, there are about 500 teams using Tableau for analytical and data governance purposes.

The changes

Deploying Tableau for data governance in JP Morgan Chase was a successful step as it brought many positive changes.

• The marketing operations team could analyze customer data to track customer journey and preferences which helped them in deciding website design, promotional materials and launching new products like Chase mobile app.
• Financial and branch managers used Tableau apps to analyze customer data in order to provide better customer banking experience.
• Tableau has given self-service analytical capabilities to a whole lot of people taking care of different operations such as traders, risk analysts, compliance team, operations analysts, sales analysts, etc.
• JPMC was able to reduce manual reporting time. Earlier, the team took months to create reports but with the help of Tableau, detailed reports were made in weeks. Tableau has saved a lot of the company’s valuable time and has shifted the focus from report generation to analyzing the reports, gaining meaningful insights into data and efficient decision-making.
• Tableau has enabled JPMC to establish stronger customer relationships by integrating customer’s data with the line of business aspects such as products, marketing, services and creating common data sets. Thus, Tableau acts as a front-end tool to maintain customer relations.
• The marketing teams at JPMC can analyze population data using Tableau to determine optimal targets to launch new campaigns.
• To ensure customer satisfaction by analyzing customer activities and behavior using call center metrics and website analytics.
• JPMC’s retail branches also use Tableau dashboards to gain a better understanding of the market and improve their business.
• Tableau successfully created a bridge between IT and business by providing apps and dashboards for risk analysis and compliance data usage. It also functions as per the government regulations.

2. Lenovo India

Lenovo designs develop, manufactures and market its product like-PC, laptop, tablet, mobile phones, servers, etc. Today, in 160 countries Lenovo expanded its empire.

Creating reports in Excel was tiresome and required a team of 8 to 10 people for adoption to other divisions and regions. In addition to this, the analytics team spent six to seven hours creating one weekly report so, imagine what an overwhelming task it was to create 30 reports or more.

With Tableau, time spent on creating reports is much lesser than creating reports manually. Teams were able to deliver reports much faster, sometimes even reporting on daily or hourly basis.

The time saved is used in carrying out analysis and drawing insights from the information.

Initially, Lenovo deployed an eight-core instance of Tableau Server which they quickly scaled up to a 16-core server. The executives introduced Tableau in Lenovo India as a means to analyze and govern data on a selected set of business use cases and scenarios to help in decision making. But sooner than they realized, Tableau became an integral part of the company’s functioning. The company experienced a cultural shift where the approach to business and growth was more data-centric and data-driven.

• Lenovo India’s BI Analytics & Visualization team created an interactive and flexible Tableau sales dashboard for departments to use it for ad-hoc analysis and reporting.
• Lenovo has over 55,000 employees and a customer base spread across 160 plus countries. More than 10,000 users access Tableau dashboards.
• Tableau has increased Lenovo’s efficiency by 95% with approximately 3000 users are using it in about 28 countries by now.
• Lenovo’s e-commerce team was able to analyze customer engagement patterns to improve brand perception and increase revenues.
• The human resource department converted 100 static reports into dynamic and interactive Tableau dashboards which gave users and analysts a new perspective into solving matters.
• The team is easily able to connect to data sources like Amazon Web Services and Hortonworks Hadoop Hive. Along with this, a wholesome analysis is possible through a dashboard where data is integrated from more than 30 data sources such as social media, customer surveys, retailer websites, online shopping sites, etc.
• Lenovo supports self-service analytics where every user can conduct an individual analysis on the set of data concerning their domain of activity and suiting their site roles. Every Tableau user has identification credentials stored in the local identity store of Tableau using which they can access and work on Tableau dashboards using single sign-on process.
• Lenovo experienced lucrative growth in e-commerce by using Tableau to analyze customer experience by fetching data from Lenovo’s unified customer intelligence platform; LUCI Sky.

3. Lufthansa

Lufthansa is a global aviation company which has used Tableau for its growth and benefit.

Around 2016, Lufthansa group felt the need for a more centralized, standardized and uniform reporting solution. Because what they had at that time was not a uniform group reporting system. Each department had their individual reporting system and required a heavy contribution from the IT department in terms of time and effort to bring together reports from different departments. Along with this, the time and efforts that went in data preparation were high and teams were not able to give enough time for data analysis and interpretation. It also affected reporting as it was manually done and was time-consuming. What the Lufthansa group needed at that time was a uniform, standardized, group-wise self-service business intelligence solution that had easily accessed data from multiple sources, prepared it quickly and created reports instantly.

Convinced by Tableau’s proof of concept, Lufthansa selected Tableau as their tool of choice. It was initially deployed in the sales department but was soon a part of the entire firm spreading across departments. Within a month, the number of Tableau users reached to 80 Tableau Desktop users. Currently, there exist more than 500 people using Tableau reports and analyzing via Tableau Server and dashboards.

• Increase in the company’s efficiency by 30% as data preparation and report generation time has reduced significantly. The data preparation to data analysis ratio which was 80:20 earlier, is changed to 60:40 which is a positive change due to Tableau.
• The time saved from fast data preparation is utilized in self-service data analysis, pattern discovery, drawing insights and report generation. All of which is crucial in a company’s growth by taking premeditated decisions based on facts.
• Enabled users to connect to all sorts of data sources used by Lufthansa such as Oracle , Teradata, SAS, etc.
• Reporting processes automated and standardized across the enterprise to ensure uniformity. In addition to the following uniform standards, Tableau also has key performance indicators throughout the group.
• Tableau increased the autonomy of users by the virtue of self-service analysis for specialist departments and enabled the company of a more efficient day-to-day analysis of their business and related activities.
• Tableau has made Lufthansa free from the bounds of IT department and made it independent in its functioning.

So, this was all about 3 major Tableau Case Studies or its success stories.

We discussed the Tableau Case Studies of three companies; JP Morgan Chase, Lufthansa Airlines and Lenovo. We saw in all of the three case studies how Tableau has helped these companies for growth. The business intelligence capabilities of data analytics help companies see current and future trends and steer their business accordingly.

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How to Learn Tableau

Learning Tableau can be challenging, but it's a valuable skill that can transform your career in data analysis. This article will guide you through a structured approach to learning Tableau, helping you avoid common pitfalls and develop essential data visualization skills.

When I first started with Tableau, I was eager to create impressive data visualizations. However, my initial excitement quickly turned to frustration. I spent hours trying to figure out which Tableau product I needed and struggled to connect to meaningful data sources. I was frustrated that I wasted so much time before ever even creating a visual!

But I didn't give up. Instead, I took a step back and developed a structured approach to learning Tableau. This change in strategy not only improved my skills but also opened up new opportunities in my career. I created a dashboard that significantly reduced manual data analysis time for my team, allowing us to focus on strategic decision-making. This experience showed me the true power of Tableau when used effectively.

In the following sections, we'll explore this structured approach to learning Tableau that will help you overcome common challenges and develop the skills needed to excel in data visualization.

Why learn Tableau?

Tableau has become essential for data-driven decision-making in many industries. As companies increasingly rely on data visualization for insights, Tableau skills are now in high demand. This trend is creating new opportunities for those who can use this tool effectively.

The job market for Tableau professionals is booming with over 90,000 jobs listing Tableau as a essential skill . This shows how valuable this skill has become. What's behind this trend? It's the massive increase in data generation. We're expected to create 50 times more data this decade than in the last one. As a result, there's a growing need for professionals with Tableau jobs who can make sense of all this information.

Why most new learners fail

With such strong reasons to learn Tableau, why doesn't everyone learn it? Because many beginners face challenges when starting out with this powerful data visualization tool.

Flashy software demos can set the bar high, leading to frustration when beginners face the reality of creating effective visualizations. This disappointment, coupled with the need to produce accurate insights rapidly, can be overwhelming.

On the technical side, some common hurdles include:

• Grasping complex concepts like LOD expressions and table calculations
• Understanding data blending
• Creating dynamic pagination buttons

These features are often mentioned as some of the most challenging Tableau concepts for new learners.

So, how can you overcome these obstacles? Here are some practical tips:

• Follow structured learning paths
• Engage in beginner-level challenges
• Participate in the Tableau community for support
• Practice consistently with real-world data
• Be patient and allow time for skill development

Remember, becoming proficient in Tableau takes time and effort. But with the right approach, you can build your skills and confidence step by step. Stay persistent, and you'll be creating impressive visualizations before you know it.

The problem with most learning resources

Have you ever felt lost after completing a Tableau tutorial? You're not alone. Many learners struggle to find resources that explain the 'why' behind the 'what' in Tableau.

Personally, I had a really hard time finding learning materials that covered the 'why' behind the 'what' in Tableau. I followed tutorials, but at the end of them I didn't feel confident that I could explain any of what I did to someone else.

This frustration is common. Most Tableau tutorials focus on creating impressive dashboards without explaining the underlying principles or data thinking processes. When I had to build a dashboard for my job, I felt unprepared. I didn't know where to start or how to approach the data I was working with.

The structure of many Tableau courses contributes to this problem:

• They teach advanced concepts too soon, overwhelming beginners
• Short courses don't allow enough time to practice skills
• Instructors can be underwhelming
• Key topics like data preparation are often overlooked

It's clear that we need a better way to learn Tableau - one that balances technical skills with practical application and data thinking. This approach would help you feel more confident when working with Tableau in real situations.

An easier way to learn

Here's a strategy you can use to improve your Tableau learning journey:

• Begin with online tutorials for basic skills
• Practice with sample datasets to create simple charts and dashboards
• Join Tableau communities for tips and advice from experienced users
• Apply your new skills to real-world scenarios like data exploration and tracking key metrics

This approach helps you connect learning goals with practical applications, which reinforces concepts and builds proficiency. But what do each of these pieces look like? In the following sections, we break down how to learn Tableau in five steps:

Step 1: identifying motivation

Why learn Tableau? It's an important question to ask before you start. Having a clear reason to learn this data visualization tool can significantly boost your chances of success.

Learning any new skill takes time and effort. When you understand why you're putting in that effort, you're more likely to persist, even when faced with challenges. This is particularly true when you're learning independently, without the structure of a traditional classroom.

Tableau offers several exciting possibilities that attract new learners. Here are a few examples:

• Data Science: Create interactive dashboards that transform complex data into clear visual stories.
• Business Intelligence: Combine multiple views into a single dashboard for easier dataset comparison.
• Advanced Analytics: Use features for segmentation, cohort analysis, and what-if simulations to explore data in new ways.
• Data Storytelling: Craft impactful visualizations across various industries, making complex information more accessible and engaging.

How can you determine your own motivation for learning Tableau? Consider these questions:

• What specific data visualization problems do you want to solve?
• How will Tableau skills help you reach your personal or career goals?
• Which industries or areas interest you most for data visualization?

Take a moment to think about these questions. Try to focus on one or two areas that genuinely excite you. This focus will help maintain your motivation as you learn.

When your reason for learning Tableau aligns with your interests and goals, you'll be better prepared to overcome obstacles and apply Tableau's features to real-world situations that matter to you.

Step 2: learning the basics quickly

Learning Tableau doesn't have to be complicated. In fact, I find that learners who try to learn the complexities too early are often the ones who burn out from frustration. Instead, focus on the basics for a short period of time, and then move on.

To start, focus on these key skills:

• Understanding data types and structures
• Connecting to data sources
• Creating basic visualizations
• Using filters and calculated fields

The best way to learn these skills is through hands-on practice. Start by finding a learning resource that provides a starting dataset and walks you through the basics. This practical approach helps you understand core elements like data structure, calculated fields, and filtering options. Begin with simple charts, then gradually move to more complex visualizations. Humans process visual data more efficiently than raw numbers, so this method can accelerate your learning.

To help you get started, here are some beginner-friendly resources:

• Tableau's official website : Offers free training videos and web-based courses for various skill levels
• Tableau Community : Provides tutorials and guides specifically for beginners
• Dataquest's Data Visualization with Tableau path : A concise learning path with four introductory courses

Consistent practice is key to developing your Tableau skills. Be patient with yourself during the learning process. Most beginners can grasp Tableau's fundamentals within 2-6 months through dedicated practice and structured learning paths. These foundational skills will contribute to more advanced data visualization capabilities as you progress.

By concentrating on these core skills and using the recommended resources, you'll establish a strong foundation in Tableau. This approach will enable you to create meaningful visualizations and prepare you for more advanced techniques in the future.

Step 3: work on guided projects

Once you have a handle on the basics of Tableau, it's time to jump into guided projects. They're designed to build your confidence and reinforce what you've learned, all while tackling real-world challenges.

Here's a list of beginner-friendly Tableau projects to kick things off:

Act as a data analyst exploring conversion funnel trends for a company's leadership team. Using Tableau, you'll build interactive dashboards that uncover insights about which marketing channels, locations, and customer personas drive the most value in terms of volume and conversion rates. By applying data visualization best practices and incorporating dashboard actions and filters, you'll create a professional, usable dashboard ready to present your findings to stakeholders.

Step into the role of a data visualization consultant for Adventure Works. The company's leadership team wants to understand the differences between their online and offline sales channels. You'll apply your Tableau skills to build insightful, interactive data visualizations that provide clear comparisons and enable data-driven business decisions. Key techniques include creating calculated fields, applying filters, utilizing dual-axis charts, and embedding visualizations in tooltips. By the end, you'll have a set of powerful Tableau dashboards ready to share with stakeholders.

Take on the role of a data analyst for Dataquest to prepare their online learning platform data for analysis. You'll connect to Excel data, import tables into Tableau, and define table relationships to build a data model for uncovering insights on student engagement and performance. This project focuses on essential data preparation steps in Tableau, providing you with a robust foundation for data visualization and analysis.

These projects span various industries. They'll help you practice key Tableau skills like data organization, visualization creation, and workflow management. As you work through these community-driven initiatives, you'll build a solid foundation for handling real-world data analysis tasks.

How do structured projects benefit you? They allow you to apply Tableau concepts to actual scenarios, boosting your confidence and expertise. For example, an e-commerce case study on product availability trends gives you hands-on experience with genuine business data.

Where to find more project ideas

Need more inspiration? Check out these resources:

• Tableau Projects : A list of 10 Tableau project ideas
• Makeover Monday : Provides weekly data visualization challenges
• Tableau Community Projects : Features ongoing projects with practice datasets

When selecting projects, consider your interests and career goals to help keep you motivated and develop relevant skills. Focus on projects that genuinely interest you and contribute to your growth in data preparation, visualization, and dashboard creation.

By working on practical, real-world Tableau projects, you'll quickly see the value of your new skills. Moreover, these projects will help you build a strong portfolio to showcase your expertise to potential employers. Remember, the goal is to apply your knowledge to solve real problems. So, choose projects that challenge you and align with your career aspirations.

Step 4: build your own Tableau projects

Now that you've learned the basics of Tableau, it's time to put your skills to the test. Building your own projects is a great way to apply what you've learned and gain practical experience. Let's explore how you can get started with your own Tableau projects.

Finding engaging project ideas

Coming up with interesting project ideas can help you stay motivated as you learn. Here are three ways to find compelling Tableau projects:

• Build on Previous Work: Take a guided project you've done and add new features or look at the data from a different angle.
• Analyze Your Interests: Use public data related to your hobbies to create visualizations you're excited about.
• Explore Current Events: Find datasets on trending topics to create timely and relevant visualizations.

Taking part in data visualization challenges can help you improve your skills through hands-on practice. These projects not only boost your technical skills but also help you develop your own approach to data visualization.

Overcoming challenges

Transitioning from guided projects to working on your own projects can be tough at times. It's an important part of the transition, but the frustration can be really hard to handle. So how can you deal with these hurdles without giving up? Here are three ways to overcome obstacles:

• Break tasks into smaller steps: Split your project into manageable parts to avoid feeling overwhelmed.
• Set achievable goals: Create clear, realistic objectives for each project. Celebrate small wins to keep yourself motivated.
• Ask for help: When you get stuck and have exhausted resources, turn to forums and communities to see if others have advice.

Remember, sticking with it is key when learning new skills. Try to see challenges as chances to grow rather than setbacks.

Finding help when you need it

When you run into problems with your Tableau projects, these three resources can help:

• Tableau Community Forums : Ask for help from experienced users and find answers to specific problems. You can post questions and get expert advice here.
• Official Tableau Documentation : Use Tableau's guides for step-by-step instructions on fixing common issues.
• Online Tutorials: Check out video tutorials on YouTube for specific Tableau functions and problem-solving tips.

When troubleshooting, start by clearly describing the problem. Then, look for solutions using specific error messages or keywords related to your issue. Don't be afraid to ask for help in the Tableau community if you can't find a solution on your own.

Building your portfolio

Your Tableau projects are key to creating a strong portfolio that shows off your skills to potential employers. Each project you finish demonstrates your ability to work with real data, solve problems, and create meaningful insights. As you work on different projects, you'll develop your own style of data visualization that can make you stand out in the job market.

If you're using Tableau Public, taking advantage of the built-in profile feature is another great way to build a portfolio. All of your vizzes are viewable via a URL, which can be a great way to showcase your skills on a resume. Of course, with all Tableau Public vizzes, ensure your data does not contain sensitive information or work you would prefer not to show others.

Make sure to document your process, including the challenges you faced and how you solved them. This information adds depth to your portfolio and shows your problem-solving skills. By consistently working on your own projects, you'll not only get better at using Tableau but also create a compelling body of work that can open doors to career opportunities in data analysis and visualization.

Step 5: work on more advanced projects

Finally you've built up a strong Tableau skillset and now it's time to level up with more complex projects. This step is crucial for developing advanced data visualization abilities that employers value.

Ready to push your skills further? Consider these project ideas:

• Build a financial analysis dashboard with interactive visualizations
• Create a customer churn prediction model and visualize the results
• Develop a real-time website analytics dashboard
• Design a supply chain optimization dashboard using mapping
• Recreate a complex data analytics project showcasing multiple Tableau features

These projects will challenge you to apply advanced Tableau concepts and expand your data visualization skills. Remember, practice with real-world datasets is key to reinforcing your learning.

By working on these advanced projects, you'll not only improve your Tableau skills but also build a portfolio that impresses potential employers. This combination of technical proficiency and practical experience can give you a significant advantage in your data analysis career.

You've made it! By working through this guide, you've gained a valuable strategy for learning Tableau, which can open doors to exciting career opportunities in data analysis and visualization.

So, what's next? To keep growing your Tableau skills:

• Focus on real-world projects you care about
• Join the Tableau Community for support and inspiration
• Check out Dataquest's comprehensive Tableau courses and paths

Remember, becoming skilled in Tableau is an ongoing journey. Each visualization you create and each problem you solve brings you closer to becoming a data expert. Whether you're a student aiming to launch your career or a professional looking to upskill, your growing Tableau abilities will help you:

• Make informed, data-driven decisions
• Tell compelling data stories
• Advance your career in the expanding field of data analysis

Stay curious, keep practicing, and don't shy away from challenges. Your efforts in mastering Tableau will pay off, helping you stand out in the job market and excel in your data-focused role.

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Tableau Fundamentals Case Study – Calwest E-Commerce Overview

In this course, you’ll act as a data visualization analyst for Calwest E-commerce. In that role, you’ll be fulfilling a set of business requirements to build several visuals and dashboards in Tableau.

Using what you learned in Tableau Fundamentals, you’ll build basic visuals, apply formatting, and create interactive functionality. Your creations will help you to draw insights and answer important business questions.

Tableau Fundamentals Case Study Learning Objectives

• Create a data model in Tableau
• Build  basic calculations and formulas
• Create  basic visuals in Tableau
• Apply formatting to create a clean and branded experience
• Add interactivity to a dashboard
• Answer e-commerce business questions using a dashboard

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Data Visualization with Tableau: 4 Best Case Studies to Know

Discovering the commercial benefits with data visualization..

Visualizing the information is more convenient than delving into the complex data table collections because the human brain easily digests the graphics, unlike the Excel spreadsheets due to their lack of information overload. That is why more and more people realize how to apply Tableau data visualization in the business context and discover the commercial benefits. In this article, you will find the examples of Tableau business cases with an ample outcome because of data visualization.

Impacting the businesses

Data visualization increases the revenue for enterprises. How, though? First of all, it allows to obtain the profound insights when answering the data-related questions: instead of trying to structure the messy information, one can easily observe trends in revenue, costs, customer count, conversion rates, and the other e-commerce metrics (MAU, DAU, CPC, CPA, LTV).

By visualizing the information, it’s easy to find inefficiencies, to determine seasonality and optimize the company’s strategy through development of profitable directions. Also, visualizing the data is the way of guaranteeing the objective source of truth for all levels of leadership and providing all of the departments with the up-to-date and truthful information. An example of efficient visual representation can be found down below:

Source: Tableau Online

Tableau, a suitable solution

Tableau is a good choice for the users in need of cross-platform reports (on tablets, smartphones, or desktops). Tableau is easy to use and is suitable for sharing the data with all the members of the company. At the same time, it is convenient for processing the large sets of information, regardless of the amount of sources.

In fact, Tableau leverages an extensive set of data connectors, such as MySQL, Google Analytics, Google SpreadSheets, Excel, CSV files, and others. Thus, it provides the users with advanced analytical dashboard capabilities along with assisted formula editing, forecasting, clustering, and flexible deployment options (in-cloud, on-premises, and online).

Coca-Cola: shaping the essence of analytics

Coca-Cola , the largest beverage company in the world, looked how to replace its daily 45-minute manual data reporting process. Previously, the team spent a considerable amount of time trying to connect over 200 million lines of data from over 100 different systems into single storage to then build one usable dashboard. To boost the efficiency and carry out a real-time data, Coca-Cola adopted Tableau. Because Tableau consolidates the data from multiple sources, various teams at Coca-Cola can now actively comprehend the metrics, including the budget, delivery operations, and profitability in a matter of few clicks. Simultaneously, the sales department can now access the data from the remote locations by using the iPads, which increased overall timeliness. Finally, the executive reports automatically refresh each day at 5:45 am, unlike the previous times.

Coca-Cola Tableau case study

Lenovo: a 95% increase in efficiency across the company

Lenovo, a global technology company, aimed to optimize its analytics experience across all the departments and worldwide offices. Previously, Lenovo operated with one single sales report that was delivered to 28 different countries. When different regions or company’s divisions wanted to adopt the report to extract the most valuable data, it required a commitment of eight to ten individuals and led to a massive number of on-hold tasks for the analytics team. In turn, Lenovo decided to use Tableau to orderly structure the data all across the company. As a result, Lenovo got a flexible dashboard with all the sales that can be adapted for the ad-hoc analyses, which also led to 95% efficiency improvement across 28 countries. With the help of Tableau dashboard ideas, Lenovo gathered the engagement metric, thus crafting a better experience and collecting more revenue.

Lenovo Tableau case study

LinkedIn: empowering 90% of the Sales Team

LinkedIn, a largest professional networking website, wanted to synchronize all the data across its internal databases ( Google Analytics , Salesforce.com , third-party tools). Previously, one analyst at LinkedIn would handle daily sales request from over 500 salespersons, which created a reporting queue of up to 6 months. To fix the issue, LinkedIn decided to use Tableau to centralize the spread out data and develop a series of customer access dashboards. As a result, thousands of individuals nowadays can access the Tableau Server on a weekly basis, which constitutes 90% of the LinkedIn sales force. With the interactive real time dashboards in Tableau, one can easily predict churn and track the current performance, which eventually created more revenue through the proactive cycle of sales.

LinkedIn Tableau case study

Bookimed: Building real-time analytical dashboards

Bookimed, a Ukrainian service for searching the best medical solutions worldwide, wanted to make an x2 increase in revenue by year. To do so, the companies that use Tableau would have to make reasonable decisions based on data. We understood that previously managers had issues with evaluating hypothesis and tasks prioritization because of the manual filtering through the information. Usual data analysis required next steps:

• Making a task for IT-department to load raw data in CSV file.
• Filtering this data by hand and exploring it to Excel.
• Manually linking data from Excel, CSV files to data from Google Analytics and Google AdWords.

In order to get quick and error-free insights from data and tracking the real-time state of the business, Bookimed decided to use Tableau and Tableau Online services. The main goal was to create the customizable for every department real-time dashboard system. To fulfill these tasks, we did undergo three steps:

• Connected Google Analytics, Google Adwords and MySQL to Tableau.
• Set-up Dashboards for every department at Bookimed.
• Additionally, data root labs incorporated Amazon Redshift + Amazon Kinesis for advanced data gathering and more sophisticated data analysis.

Here are some of the first graphics of the company (numbers are random and for visualization purpose only):

Graphics and Visualizations (Tableau). Source: Bookimed.com

Now, Bookimed.com has the system that delivers all the relevant data and information to every member of the company in real-time mode. Simultaneously, Bookimed got all of the following:

• Reduced the business analysis time from 1 week to 2 hours and ensured the data-driven decision making among employees.
• Build based on data reasonable growth plan for increasing the revenue by 10% monthly .
• Acquired the data visualization infrastructure that can be easily modified, scaled, and changed according to the business needs.

Other Industries where Tableau is used

Healthcare analytics.

• BJC , a company that provides healthcare to residents of Missouri and Illinois, reduced supply chain expenses from 23.5% to 19% .
• Seattle Children’s, a pediatric hospital, saved more than 40000 clinical hours each year and $100000 with demand flow.

Education Analytics

• Des Moines Public School District received an ability to quickly detect high-school students with the likelihood to drop-out .
• The University of Notre Dame got able to perform data analysis using Tableau 10x times faster .

Government Analytics

• The city of Tallahassee measured workload sewers’ efficiency and made utility decisions that improved productivity by 30% .
• Surrey County Council reduced analysis time from days to hours to track intervention at youth clubs.

Marketing Analytics

• Allrecipes, a largest digital food brand, increased the mobile site visits from 8 percent to more than three-fourth of total .
• PepsiCo cut analysts time by 90% .

Tableau Insurance Analytics

• EY, a professional risk management organization, saved clients millions of dollars and prevented fraud .
• MA Assist, the UK-based property services firm, cut insurance claim duration by 15% and improved business efficiency by 20% .

High Technology Tableau Analytics Examples

• GoDaddy, an international web hosting firm, scaled 13TB of data governance and and optimized product experience for over 17 million customers.
• Ancestry.com , a largest online resource for searching family history, visualized billions of rows of data for strategic decision-making .

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18 January 2024

4 Tableau business cases that really deliver

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Have you ever gotten a new tool – for instance a Swiss Army knife or other multi-tool – and taken it out of the box, then thought: “this is great… but what am I actually going to use it for?” You may find yourself wondering the same thing about Tableau. While you might already be aware of some of Tableau’s powerful data analytics possibilities, you may still not know how your organization in particular can make the most of it. That’s why this article will feature four prominent Tableau business cases, to show you just how this platform can make a dramatic difference. 

The four Tableau use case examples we’ll cover here will be focused on the following industries/sectors:

• Consumer goods – making better decisions in trade promotions 
• Retail – optimizing stock levels and maximizing on-shelf availability 
• Nonprofit – measuring and increasing an organization’s impact 
• Technology – better targeting Software as a Service (SaaS) sales

Let’s start with how Tableau can enable better decision-making for consumer goods businesses: 

1. Making better decisions in trade promotions

We live in a world where your customers are only a click away from choosing a competitor over you. Today’s companies need the richest data insights they can get to keep up with their rivals. However, data is often siloed in a wide range of different applications, systems and spreadsheets, making it difficult to see everything in context and get a sense of the big picture. This was the challenge our client, Beyond Better Foods , was facing. They needed a 360-degree view of the customer and a single source of truth.  

With Salesforce Tableau software, companies like our client can now bring together all their data, from all their multiple disparate data sources, in one place, to optimize their commercial strategies. They can create sophisticated embedded visualizations and dashboards to present data related to distribution, product categories, supply chain orders and more. All easily at hand whenever decisions need to be made. 

That’s not all. Tableau’s predictive analytics capabilities can be used to predict matters such as future demand and optimal discount levels. This means retailers are better equipped to quickly adapt to changing market conditions and audience behavior. Businesses can now seize opportunities, avoid pitfalls and serve the customer with greater agility than ever. As for Beyond Better Foods, they’ve been able to save 70% of the time staff spend creating reports and forecasts, and even made a six-figure saving thanks to the errors they’ve been able to eliminate.  

If you’d like to learn more about how we helped Beyond Better Foods to make the most of Tableau (which was at that time called Einstein Analytics) you can watch this short walkthrough video after filling in a few details. 

2. Optimizing stock levels and maximizing availability

Every retailer knows the vital importance of stock levels. Stockouts mean frustrated customers who may not ever return. If a retailer isn’t able to maintain the right levels of on-shelf availability for products their customers want, that can result in missed sales. Furthermore, it can cause a decrease in repeat business and customer loyalty, as shoppers go to competitors to make their purchases instead. For fast-moving consumer goods (FMCG) retailers dealing with perishable products or “fast” fashion, optimizing stock is especially vital.

Fortunately, Tableau provides businesses with new abilities to predict when out-of-stock events are due to happen. One example is the stockout alert system our Tableau team created for a retail industry client in the UK. By applying Tableau’s analytics capabilities to sales data, retailers can see patterns in the frequency of sales of a particular product. Then, based on the time that has elapsed between sales, they predict the likelihood of out-of-stock events and an alert can automatically be created. In-store staff can then be notified to investigate this possible out-of-stock and take action to correct it if necessary. With this solution, inventory levels fell by 10% in average, and the number of customers lost to product stock-outs was cut in half.

If you would like to explore the benefits of implementing this technology at your organization, with the help of our Salesforce analytics consulting services, let’s talk. 

3. Measuring and increasing an organization’s impact  

For nonprofits, perhaps more than any other organizations, it can be prohibitively time consuming to make the most of their data and measure their impact. Few can spare the budget for a dedicated data analyst to analyze interactions with the people they serve, in all their notes, questionnaires and even audio/video recordings. In the end, there is a missed opportunity to get a better sense of the “big picture” and – perhaps most importantly – the work they do for each individual person, day to day, hour to hour.

Our third-sector client Noise Solution works to help young people through the power of music. They wanted to use Tableau to gather, process and analyze data that would show their impact. To help them reach this goal, VRP developed a solution and a custom AppExchange app harnessing AI-driven technologies such as natural language processing (NLP), text mining and speech-to-text.  

Now, Noise Solution has gained an unrivaled insight into how music turns into well-being. They can access sentiment and intent for every intervention, determining whether a participant feels positively or negatively about their experience. Moreover, they can see and understand the change they’re doing in the world with a “live” overview of their impact. And, thanks to all the time saved in manually analyzing interactions, they’re now able to help 20% more young people than previously. 

If you’d like to explore how we helped Noise Solution to harness the power of Tableau in this way, read this case study. 

4. Better targeting Software as a Service (SaaS) sales

When a sale is made, there are often other products or services that may also be useful and desirable to the customer. Salespeople are usually well aware of the upsell possibilities when it comes to cross-selling companion products and will suggest these when making a sale. But have you ever discovered less-obvious products that go well with what you sell? 

Technology companies like us use Tableau’s powerful capabilities to make the most of every sales opportunity. Let’s look at a real-life example taken from our pipeline – a Salesforce Sales Cloud customer in the manufacturing industry. Our account manager immediately thought about Field Service as an ideal companion product. But by analyzing patterns in purchase data, along with attributes of a predetermined target customer archetype, Tableau revealed that the less obvious Pardot would also make an equally good companion product. Our Account Manager used this insight to suggest that complementary product to the customer. 

We did all the above using Tableau and tools such as Einstein Next Best Actions and Einstein Product Recommendations – all products in the Tableau family and seamlessly integrated. With these kinds of solutions, organizations like ours are connecting with every customer’s unique needs by better serving everyone in the customer base. Enhancements like this have been key drivers in our two-digit growth last year. For all kinds of businesses, Tableau can be used to surface new, hitherto undiscovered patterns in product data and historical sales data, showing connections that can be very valuable for sales purposes. 

If you’re interested in bringing this technology and these kinds of insights to your own sales efforts, we can discuss it together. 

Tableau business cases for a range of requirements

Since the days of Salesforce Analytics Cloud, Tableau has long offered great capabilities to organizations, and the ability to integrate Tableau into Salesforce provides even more value and potential. The relatively new Salesforce-Tableau synergies mean that whether you already use the world’s #1 CRM or you’re considering implementing it, there’s never been a better time to start making the most of Salesforce. Do you want to make a business case for Tableau at your own organization and gain stakeholder buy-in? We hope you found a relatable example in this article. Even if not, there’s sure to be a case study using Tableau that can make all the difference in the conversation. If you’d like to explore the possibilities, and how our Salesforce analytics integration services can help you make the most of them, just get in touch. 

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4 Real-World Tableau Use Cases

In the world of making decisions based on data, organizations need to stay informed about the latest technology to make smart choices, enhance operational efficiency, and stay ahead. In this blog, we’ll learn about Tableau, the advantages of using it for business analytics, and its most recent updates.  

What is Tableau?

Tableau is a powerful data visualization and business intelligence tool that allows users to connect to various data sources, create interactive and shareable dashboards, and gain valuable insights. It enables individuals and organizations to analyze and visualize data in a user-friendly and it is designed to be easy to understand and use without needing a lot of extra explanation. It’s user-friendly and makes it easier to understand complex information and make informed decisions. Tableau supports a wide range of data sources, provides a variety of visualization options, and promotes a self-service analytics approach, allowing users to explore and interact with their data to discover meaningful patterns and trends.  

Key features of Tableau

User-Friendly Interface: Tableau is easy to use for both experienced data professionals and newcomers in analytics. It has a simple drag-and-drop feature that lets users create visualizations without needing advanced coding skills. This makes it quick to learn and encourages people in different roles to explore and understand data on their own. Whether you’re a data scientist or a business user, Tableau’s easy-to-use interface makes working with raw data enjoyable and straightforward, helping you uncover valuable insights.  

Data Connection: Tableau offers a wide range of data connections, allowing users to link to various data sources. Whether it’s databases, spreadsheets, or cloud-based data, Tableau provides strong connectivity options, making it versatile for diverse data environments.  

Drag and Drop Functionality: Tableau has a notable feature with its drag-and-drop functionality, with these features, users can effortlessly drag and drop data elements into the interface to create interactive and dynamic visualizations. This user-friendly approach simplifies the process of building insightful dashboards without the need for complex coding.  

Diverse Visualization Options: Tableau offers a diverse selection of visualization options, including charts, graphs, maps, and more. This diversity allows users to select the most effective way to represent their data, ensuring that insights are communicated clearly and effectively.  

Real-Time Analysis: Tableau makes real-time analysis easy. Users can analyze and visualize data in real-time, providing timely insights. This feature is priceless for organizations that require the latest information to make informed decisions.  

Collaborating and Sharing: Tableau promotes collaboration among teams by allowing users to share their visualizations and dashboards. Whether through Tableau Server or Tableau Online, team members can access and interact with shared insights, enabling a collaborative and data-driven decision-making culture.  

Advanced Analytics Integration: Organizations aiming for deeper insights can easily integrate Tableau with advanced analytics tools. This integration helps users to include statistical models, machine learning algorithms, and scripts in R or Python, enhancing the platform’s analytical capabilities.  

Security Features: Security is a top priority for Tableau. This platform offers strong security features to manage data and ensure the privacy of confidential information. Integration with existing authentication systems enhances overall data protection, making Tableau a secure and reliable solution for managing valuable business data.  

Mobile Accessibility: Tableau provides mobile accessibility through dedicated applications, allowing users to access and engage with visualizations seamlessly on smartphones and tablets. This feature helps users stay connected to valuable data insights, enabling on-the-go access to critical information.   

Types of Services

Tableau services offer a range of services, that help to create, share, and manage data visualizations. Tableau offers services as follows:  

Tableau Desktop: Tableau Desktop, is offered by Tableau, and serves as both an authoring and publishing tool. It helps users to create interactive and shareable data visualizations, dashboards, and reports. With Tableau Desktop, users can connect to various data sources, perform data analysis, and design impactful visualizations using a user-friendly drag-and-drop interface.  

Tableau Server : Tableau Server is an enterprise platform. It helps people work together and share Tableau content within an organization easily. It provides a centralized location for publishing & accessing Tableau dashboard reports. This makes it easy for everyone to work together and for the company to grow smoothly.  

Tableau Public : Tableau Public is a free version of Tableau that allows users to create, share, and explore visualizations with the public. Visualizations created with Tableau Public are stored on Tableau’s public servers and are accessible to anyone.  

Benefits Of Tableau Services  

Improved Decision Making: Tableau helps users transform complex data into useful information by creating easy-to-understand visualizations. This makes it easier for everyone to make better decision making across various domains, as data becomes easily understandable and accessible.  

Enhanced Operational Efficiency: Tableau makes it easy to analyze and report data, which helps organizations work better. People can quickly get important information, and this helps organizations make their processes better and improve using a data-driven approach.  

Enhanced Student Engagement: In schools, Tableau helps teachers connect with students using data. Teachers can look at how students are doing, find patterns, and make lessons more personalized. This makes learning more interesting for students and helps them do better in school.  

Data-Driven Innovation: Tableau helps to create a culture that uses data-driven innovations. Organizations can look at trends, find patterns, and discover insights that lead to creative solutions and strategies. This helps them stay competitive in fast-changing environments.  

Cost Savings: Using Tableau to analyze data smartly can save money. Organizations can find ways to do things better, use resources wisely, and make processes work more smoothly. This helps cut down on unnecessary spending and makes the overall financial performance better.  

Data Integration Tool: Tableau serves as a strong data integration tool, allowing users to connect to various data sources. It lets users connect to many data sources without any problems. This helps to provide a comprehensive view, supporting a more holistic decision-making approach.  

4 Tableau Use Cases

Unlock Deeper Customer Insights with Tableau  

Struggling with siloed, disjointed customer data? Tableau integrates data from across your business – sales, marketing, service, finance, etc. – for a unified 360 customer view.  

Tableau’s easy ad hoc analysis empowers anyone to uncover rich insights like changing behaviors and preferences. Unearth insights like:  

• Which customer segments drive the most revenue and churn the fastest?  
• How do customer journeys differ across channels?  
• Which marketing campaigns deliver the highest conversion at the lowest cost?  

Tableau makes it easy to create interactive dashboards with advanced visualizations. Showcase customer metrics and segments so everyone has visibility. Marketing optimizes engagement strategy. Sales pursues high-value targets. Service enhances experiences.  

With Tableau, you fully understand customers to offer personalized experiences. This strengthens engagement, satisfaction and loyalty.  

Boost Product Quality with Tableau  

• Connect Product and Machine Data for Insights  

Ingest sensor, machine, and operations data into Tableau for analysis. Get visibility into product performance spanning design, manufacturing, and usage.  

• Monitor Health and Usage in Real-time  

Create dashboards to track metrics like reliability, defects, usage patterns and more. Monitor products actively in the field to detect emerging issues.  

• Identify Anomalies and Predict Failures  

Leverage analytics capabilities to flag anomalies in data that point to potential product failures or degradation. Take proactive steps to address.  

• Inform Engineering and Manufacturing  

Share actionable intelligence with R&D, engineering and manufacturing teams. Rapidly diagnose root causes and continuously improve product quality and reliability.  

Accelerate Supply Chain Operations with Tableau

Supply chain data is complex. Inventory, logistics, vendors, partners…how do you connect the dots? Tableau ingests all this data for end-to-end visibility.  

Easily track KPIs like cycle times, on-time delivery, and costs with interactive dashboards. Drill into delays and bottlenecks. See where to optimize.  

Tableau enables predictive capabilities like forecasting demand, predicting risk, and simulating scenarios. Now you can model the impact of changes before implementing.  

Mobile analytics keeps supply chain teams connected. Execs have visibility from anywhere. Teams coordinate faster with data at their fingertips.  

Enhance Cybersecurity with Tableau

• Ingest and Analyze Security Telemetry  

Connect security data from across IT environments. Network, endpoints, cloud, identities – get holistic visibility.  

• Detect Threats with Behavior Analytics  

Profile normal behavior to spot anomalies indicative of threats. Analyze patterns across users, devices, networks.  

• Accelerate Incident Response  

Create intuitive dashboards for security ops teams. Visually track threats and response processes. Add context to incidents.  

• Tighten Security Across Hybrid Environments  

Monitor on-prem, cloud, and hybrid deployments. Quickly uncover hidden gaps and vulnerabilities. Tighten policies, controls, and configurations.  

With Tableau, your business becomes proactive. You sense demand shifts, risks, and disruptions faster. This means responding swiftly to demand and changes. The result? Faster, leaner, operations.  

How Beyond Intranet Helps With Tableau For You  

Amazing use cases. But are you struggling to realize Tableau’s full potential? Our Tableau experts guide you to success. We architect fully customized solutions tailored to your needs, unifying siloed data for complete visibility.   

Our consultants optimize performance, fine-tuning speed and efficiency. We simplify deployment and onboarding, enabling intuitive self-service analytics. With decades of collective experience, we implement best practices for data security, governance and agile development.   

Our breadth of technical expertise equips us to integrate Tableau into your existing tech stack for greater capabilities.   

Partner with Beyond Intranet- together we’ll maximize your Tableau ROI and become a data-driven organization.  

Wrapping Up  

Tableau delivers transformative capabilities for data-driven innovation and decision making. To fully harness its power, partner with Tableau specialists like Beyond Intranet . Our experts help you architect, implement, integrate and optimize Tableau to extract maximum value from your data.   

Let us guide you on the journey to becoming an insights-driven business.  

Let's Talk to Our Expert!

15 Tableau Projects for Beginners to Practice with Source Code

Sample Tableau Projects for Practice with Examples to help you master using the fundamental features and tools in Tableau for any data science project.

With the explosion of data in all industries, the need for user-friendly business intelligence tools to read them has also increased. Around 63,298 companies across the globe are reported to have been using Tableau, according to Enlyft.com . Its easy-to-use features and great compatibility with other software applications has helped  leverage the power of visualization in the IT world.

In the world of analytics, it is extremely important to pick up the skill of storytelling. Storytelling involves showing patterns in a given dataset and then trying to infer actionable insights from the same. With Tableau, the exercise of storytelling has become quite efficient and interesting at the same time. With so much data in hand, business intelligence tools like Tableau aid in giving a direction to the analysis so that one can present the correct inferences from the data.

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Tableau is a data visualization tool that can be used across different data-related profiles. These profiles include Data Engineers, Data Scientists, Data Analysts , and Business Analysts , to name a few. The stage at which Tableau can be used may vary from role to role and project to project. Most importantly, the tool is used in the exploration phase, where it showcases vast datasets in different visualizations. On the other hand, Tableau is also used to showcase inferences to end stakeholders and higher management.

Table of Contents

Beginner level tableau sample projects ideas, intermediate level tableau project ideas, advance level tableau project ideas, 15 sample tableau real time projects for practice in 2021.

With the need to learn Tableau as part of analytical skillset , it becomes essential to understand where to start and how to start simultaneously. This article is a one-stop solution for all data enthusiasts to understand Tableau and start working on some interesting datasets for tableau projects. The example Tableau projects for practice have been categorized into Beginner, Intermediate, and Advanced Level Tableau project ideas. 

1) Patient Risk Healthcare Dashboard

This beginner-level Tableau project idea is from the healthcare domain. This can be a part of a Data Analysis or Data Science project based on prediction-related analysis. 

The problem statement here is to analyze a massive dataset of patients in a particular hospital and, based on their information, predict and infer the risk of their health. You should then integrate all this analysis into Tableau for easy consumption of the end-users.  The dataset here has 17 variables for which a thorough analysis has to be done to get details about the patients at risk.

A sample of the dataset is given below for reference:-

Source :  powerbi

This project is highly critical, and hence accurate analysis has to be done for the same. Since the dataset is huge for this project, Tableau can be used in two ways. One is where the as-is information is taken, and the same is represented through visualizations and interactive capabilities. Another is where it is used to do exploratory data analysis , and then a model is built on the same to predict the risk for patients with similar health conditions.

One can use variables like age and gender to analyze categorical variables so that the appropriate gender and age category can take more precautions.

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2) Sales Forecast Analysis Dashboard

This sample Tableau project is a part of the Data Analysis and Data Science project since it involves forecasting. The problem statement here is to analyze the data of sales of a company. The main agenda of the project is to infer the past sales numbers of a company and then forecast their sales for the coming quarters and years. The dataset here has three variables for doing the analysis.

Source : Sap

With just three variables in the dataset, it becomes easier to analyze each variable and determine which store and type have given what kind of sales. You can use Tableau for creating simple bar charts on sales forecasts. Once that is done, modeling can be done to forecast the trends of sales in the future.

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3) Marketing Campaign Dashboard

An interesting Tableau project idea in the marketing domain that requires a lot of number crunching. The problem statement here is to analyze a dataset of marketing campaigns and visualize the performance of various marketing campaigns. The dataset here has six variables of multiple data types. A sample of the dataset is given below for reference:-

Source : Sisense

Marketing Analytics is one of the hottest topics across industries. With Tableau, one can easily analyze large datasets of various contacts and infer which segment type has been performing well. With the count and percentage numbers, a marketing analyst can show the aggregate level data on Tableau through area charts and simple pie charts as well.

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4) Product Availability Dashboard

This project is from the Product domain of the technology industry. The problem statement here is to analyze a dataset of product-related information. The project’s primary goal is to analyze the trends and showcase the availability of any product at any given point in time. The dataset here has 11 variables of different data types. A sample of the dataset is given below for reference:-

Source : Datapine

This project is an ideal case in an e-commerce industry where it becomes eessential to know if a particular product is in stock at any given moment or not. With all the variables given at the top, easily you can prepare a dashboard to analyze which product is searched the most and which product has the most stock. With URLs being in the dataset, drills capability can be built for a user to drill to the actual product to have a quick look at it.

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5) Flight Price Analysis Dashboard

This project is from the Airline Industry. The problem statement here is to analyze a dataset of flight-related information. The main objective is to consider different factors of a flight and infer accurate trends for flight prices. The dataset here has ten variables that involve date and time data types as well.  A sample of the dataset is given below for reference:-

This project allows an analyst to do a lot of exploratory data analysis to understand the pattern of flights with a higher price. With variables like route, one can analyze which routes cost more and also plot trend lines to see if a higher duration flight costs more. Once the pattern is understood,you can also implement models like Random Forest to do predictive analysis.

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6) Crime Analysis Dashboard

The problem statement here is to analyze a dataset of various crimes happening at a place. It should also showcase its intensity and provide some excellent action items to prevent them. The dataset here has eight variables . A sample of the dataset is given below for reference:-

Source : Powerbi

  The above dataset is also an excellent way to start your Tableau journey. With the above variables, Tableau can be used to see which area has the highest number of crime cases. This can be achieved by plotting a treemap on Tableau. Also, year-wise trends can be seen to infer if the cases have come down or have increased. With categorical analysis, numerical data types can also be used to see the intensity of the thefts across years and areas.

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7)  Air Quality and Pollution Analysis Dashboard

  This project is from the environment protection industry. 

The problem statement here is to analyze a dataset related to different air quality factors and pollution in a particular area. The primary aim is to get an understanding of its causes and take preventive measures. The dataset here has 12 variables for extensive analysis.

Source Link

Pollution and Air Quality is one of the most important challenges that leaders worldwide are trying to solve. With the above dataset and some knowledge about the similar industry, one can use the numbers of different gases to draw inferences about the composition of air in different cities and across different timelines. One can present the data visually and help the end-user infer from it based on their experience in this industry.

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8) Sales Pipeline Dashboard

This project is from the Sales domain of the technology industry. The problem statement here is to analyze a dataset of to understand how the entire sales funnel is moving. The objective here is to help Sales stakeholders understand how they are performing across different revenue segments. The dataset here has 13 variables which also involves currency-related inputs

For the Sales team, understanding their pipeline is of utmost importance. A business intelligence tool like Tableau can make visualizations using a funnel graph to see the entire sales pipeline. With such a view into the data, Sales stakeholders can make key decisions on their next move.

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9) Stock Exchange Analysis Dashboard

The problem statement here is to analyze a dataset of different stocks and derive meaningful information. This will help other individuals take correct decisions on investment. The dataset here has eight variables of integer data type. A sample of the dataset is given below for reference:-

This is one of the projects where the data volume is huge and the data change is very frequent. The Stock Market produces large amounts of data with more room for analysis. One can create various combinations of the area and trend charts to show how the index has been moving and what highs and lows they have made. Also, volume can be visualized with pie charts or bar charts.

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10)  Global Terrorism Analysis Dashboard

The problem statement here is to analyze a dataset of terrorism across the globe and then help different world leaders take corrective actions against any future attacks. The dataset here has 22 variables of different data types. A sample of the dataset variables is given below for reference:-

i_yearmonth

country_txt

specificity

alternative

alternative_txt

This Tableau dashboard above can be used extensively to infer which parts of the world have the most terrorist attacks. Also, with the event dates yearly, you can do analysis using line graphs to see the trend of these attacks. Prediction can be made to prevent any future attacks based on the analysis using Tableau. 

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11) Covid-19 Analysis Dashboard

The problem statement here is to analyze a dataset of covid cases worldwide and give real-time numbers for various regions. The dataset here has nine variables of different data types.

The Covid-19 pandemic has taken the world for a toss. With so many infections worldwide, it became extremely difficult for the government to measure the intensity of the pandemic. But with analytics and Tableau, various analysts were able to get government verified data and use them to show the real picture of the world. The above dataset is an ideal example of how you can use Tableau to visualize such volumes of data based on categorical or numerical data.

 12) Credit Card Fraud Detection Dashboard 

The problem statement here is to analyze different credit card transactions and understand the pattern of them to detect anomalies and identify fraudulent transactions. A sample of the dataset is given below for reference:-

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grid_3x3V2sort

grid_3x3V3sort

grid_3x3V4sort

grid_3x3V5sort

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grid_3x3V8sort

grid_3x3V9sort

-1.3598071336738

-0.0727811733098497

2.53634673796914

1.37815522427443

-0.338320769942518

0.462387777762292

0.239598554061257

0.0986979012610507

0.363786969611213

1.19185711131486

0.26615071205963

0.16648011335321

0.448154078460911

0.0600176492822243

-0.0823608088155687

-0.0788029833323113

0.0851016549148104

-0.255425128109186

With the increase in credit cards, credit card fraud has also increased. With the above dataset, one can do some exploratory data analysis of different transactions and try and find some pattern in the data. Tableau will be extremely helpful to visualize such complex transactions using boxplots and other methods to identify outliers.

13) Twitter Sentiment Analysis Dashboard

The problem statement here is to analyse a dataset of tweets on Twitter. The main aim is to analyze the sentiment of these tweets and take action accordingly. The dataset here has 20 variables of different data types. A sample of the dataset is given below for reference:-

Sentiment Analysis is one of the most common data analytics problems that has been solved for the social media industry. With huge volumes of data as above, an analyst can see the importance and impact of positive and negative comments given across time and various segments of the industry.

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14)  Account Management Dashboard

The problem statement here is to analyze a dataset of accounts and present it so that the account managers can easily identify the accounts and manage them easily. The dataset here has five variables for string and integer data types. A sample of the dataset is given below for reference:-

It is crucial for any product based company to have a clear picture of where each of their account and client stands. In the above dataset with just a few variables, one can see how many Platinum, Gold, Silver, and Bronze customers are there using a bar chart in the decreasing order. One can also see how each account is contributing to their overall sales by using a pie chart.

15) Video Industry Dashboard

The problem statement here is to analyze a video dataset and its pattern based on user experience. With such analysis, the objective is to identify the patterns of different human behavior and then target their audiences better. The dataset here has 17 variables of different data types

  This is another example of a recommendation system where based on a user’s browsing history, they are suggested similar videos to watch. The above dataset is quite complex in a CSV format, but with tableau, one can easily create bar, line, and pie charts to see how users see their video content across regions and timelines. Once the pattern is understood through exploration, machine learning can be used for predicting future behavior.

The above Tableau sample project ideas just serve as a start to your career in enhancing and learning Tableau skills. The best way to go about it is to do hands-on, taking different problem statements from different industries and extensively exploring all variables. Once you have worked on various excellent projects using Tableau, you will be all set to use these skills to solve real-world data science problems.

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Songwriters Are Getting Screwed by Streaming Even Worse Than They’d Thought, New Study Shows

By Jem Aswad

Executive Editor, Music

• Music Industry Moves: SZA and Babyface to Receive Big Honors at BMI R&B/Hip-Hop Awards; Sony Music’s Get-Out-the-Vote Campaign Returns 13 hours ago
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• Lin-Manuel Miranda, Eisa Davis Reimagine Classic NYC Cult Film ‘Warriors’ as Concept Album 21 hours ago

Back in the days when “music” meant CDs and vinyl, a songwriter could make a decent living : their percentage of a song or songs on the average $18.99, 12-song CD could add up quite nicely, as anyone with a credit on a blockbuster album like “Thriller” or “The Bodyguard” could attest.

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This situation is presented in sobering detail by a new study titled “Songwriters Take the Stage ” from London-based marketing intelligence and consulting firm MIDIA Research , written by former Billboard reporter Tatiana Cirisano and music researcher Fernanda Balzaretti. The data in the report is proprietary and thus Variety can only share a small amount, but there’s more than enough to make the point.

Of the over 300 songwriters surveyed for the study, only 10% earn more than $30,000 annually, while over half (54%) earn between $0 and $1,000. Some 67% of the respondents said the “lack of meaningful streaming income” is their primary challenge.

Why is this the case? In part, because of the discrepancy in what a songwriter earns of that $.004 versus the performer’s share: The songwriter gets an average of 9.5% while the artist receives nearly double that amount, according to separate MIDIA sources. (The report’s writers note that mostly lower-earning songwriters participated in the survey.)

Of that 14%, the songwriter gets 68%, the publisher 17% and the PRO 15%, the report states. But those numbers don’t take into account the fact that most hit songs these days have between 3 and 12 writers splitting that 68% of the 14% of $.004 per stream, and that they have managers and possibly other interested parties earning a percentage of their share. The human brain can’t even comprehend the infinitesimal amount the creators of most songs earn per stream.

The streaming economy is deeply unfair to most artists as well, but they have opportunities to diversify that non-performing songwriters do not: Touring — which is how most performers have compensated for the vast decline in income that accompanied the streaming era — merchandise sales, synchs (such as film/TV and advertising), endorsements and other ways to monetize their popularity.

But most people would never recognize the most successful non-performing or even non-superstar songwriters, even though they might know and love a dozen of their songs. Small wonder, then, that among the 309 songwriters surveyed for the report, a whopping .04% — yes, 11 — identified themselves as only songwriters; most of the rest are also producers, performers, film/TV composers or jingle writers. And just 23% say they’re full-time songwriters.

(Note: A rep for Spotify took exception to many of the statements in this article, noting that its all-time payouts to music rights holders are over $50 billion; that in 2023 it set the record for the highest annual payment from any single retailer in history, a number that will be exceeded this year, and the same is true for publishers; that it pays out nearly 70% of the money it takes in; and that it does not control how labels, publishers and other intermediaries distribute the money they collect. The rep also noted that “On an absolute level, the number of artists and songwriters making money is higher than ever before: 50,000 artists are making nearly $70,000 from music; there weren’t even 50,000 artists able to participate in the industry in the CD era.” More information can be found on the company’s Loud and Clear website .)

How did it come to this? Put simply, because even though there is no music without a songwriter, they have the least leverage in the streaming economy. As the data above shows, record labels make a lot more money per stream than a publisher does — and because the companies that own the largest labels also own the largest publishers, it is in their interest to keep things that way, because they make a significantly larger profit on recorded music than they do on publishing. And the collaborative “writers rooms” where many of today’s hit songs are created don’t pay a salary or even a per diem — the songwriters only get paid if the song is recorded and released, which means they’re often paying out of their own pockets just to work.

Even producers have managed to leverage their situation better, often negotiating “points on the master” — a percentage of the recorded-music royalties — as part of their deal. Such arrangements are extremely rare for songwriters.

The end result is a situation that everyone knows is wrong but no one is able and/or willing to risk their own livelihood to fix (like a lot of other current existential crises). Some streaming services, like Apple Music and especially Tidal, do pay a higher rate than Spotify, but you have to rack up millions of streams to notice a difference between $.004 and $.0125. And songwriter groups have advocated for points on the master — British superstar singer Raye, who began as a professional songwriter, argued for it forcefully onstage at this year’s BRIT Awards — as well as a salary or per diem for writers rooms.

But the situation has gotten so dire that when a group of writers refuses to accept the going conditions — as a short-lived s o n g w r i t e r collective called the Pact did three years ago — there are plenty of others eager or desperate enough for work to cross their picket lines.

For more information read the MIDIA report (available here ), Variety ’s extensive past reporting on this subject , and contact SONA , NSAI or the 100 Percenters . And most of all, pay for your music, because there’s no art without artists.

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• Introduction
• Conclusions
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Viral load was measured by quantitative reverse transcriptase–polymerase chain reaction assay to assess viral shedding. Plaque-forming units were quantified on Vero cells ectopically expressing transmembrane serine protease 3 and human angiotensin converting enzyme 2 to assess virus viability.

a Mean difference and 95% CIs were estimated using linear regression controlling for time from index to swab collection, number of daily medications reported, and race/ethnicity. Hours of missed work is additionally adjusted for occupation.

Viral load over the time course of infection for vaccinated and unvaccinated participants with at least 3 specimens. Index date is defined as the date of illness onset or the date of specimen collection if the infection was asymptomatic. Negative days indicate infection prior to symptom start. Points are individual positive reverse–transcriptase polymerase chain reaction specimens. Specimens below 1 on the y-axis represent a viral load below the minimum quantifiable level and estimated from cycle threshold values. 10 6 is the maximum quantifiable by the assay. Curves are predicted mean viral load from bayesian linear models with time from index as a 3-knot natural spline. A, Overall viral count of origin strains and Delta variants are higher than Omicron variants, with mean differences of 0.26 (95% CI, 0.0-0.5) and 0.21 (95% CI, 0.0-0.4), respectively. B. Symptomatic COVID-19 infections had significantly higher viral count than asymptomatic infections (mean difference, 0.70 [95% CI, 0.4-1.1]).

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• Association Between BNT162b2 Vaccination and SARS-CoV-2 Infection Incidence Among Health Care Workers JAMA Original Investigation June 22, 2021 This cohort study estimates the association between Pfizer-BioNTech BNT162b2 vaccination and symptomatic and asymptomatic SARS-CoV-2 infections among health care workers more than 7 days after receipt of a second vaccine dose. Yoel Angel, MD, MBA; Avishay Spitzer, MD; Oryan Henig, MD; Esther Saiag, MD, MHA; Eli Sprecher, MD, PhD, MBA; Hagit Padova, MD, MHA; Ronen Ben-Ami, MD
• BNT162b2 Vaccination and Long COVID After Infections Not Requiring Hospitalization in Health Care Workers JAMA Research Letter August 16, 2022 This study examines the risk of long COVID following mRNA vaccination, compared with no vaccination, in health care workers in Italy who had COVID-19 infection. Elena Azzolini, MD, PhD; Riccardo Levi, MSc; Riccardo Sarti, MSc; Chiara Pozzi, PhD; Maximiliano Mollura, MSc; Alberto Mantovani, MD; Maria Rescigno, PhD
• SARS-CoV-2 Infection in Health Care Workers After a Single Dose of mRNA-1273 Vaccine JAMA Network Open Research Letter June 16, 2021 This cohort study of health care workers in a metropolitan Veterans Administration network examines the association of SARS-CoV-2 infection after receiving a single dose of mRNA-1273 vaccine compared with no vaccination. Kalpana Gupta, MD, MPH; William J. O’Brien, MS; Pamela Bellino, MA; Katherine Linsenmeyer, MD; Sucheta J. Doshi, MD, MPH; Robert S. Sprague, MD; Michael E. Charness, MD

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The HEROES-RECOVER Network. Association of mRNA Vaccination With Clinical and Virologic Features of COVID-19 Among US Essential and Frontline Workers. JAMA. 2022;328(15):1523–1533. doi:10.1001/jama.2022.18550

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Association of mRNA Vaccination With Clinical and Virologic Features of COVID-19 Among US Essential and Frontline Workers

• Original Investigation Association Between BNT162b2 Vaccination and SARS-CoV-2 Infection Incidence Among Health Care Workers Yoel Angel, MD, MBA; Avishay Spitzer, MD; Oryan Henig, MD; Esther Saiag, MD, MHA; Eli Sprecher, MD, PhD, MBA; Hagit Padova, MD, MHA; Ronen Ben-Ami, MD JAMA
• Research Letter BNT162b2 Vaccination and Long COVID After Infections Not Requiring Hospitalization in Health Care Workers Elena Azzolini, MD, PhD; Riccardo Levi, MSc; Riccardo Sarti, MSc; Chiara Pozzi, PhD; Maximiliano Mollura, MSc; Alberto Mantovani, MD; Maria Rescigno, PhD JAMA
• Research Letter SARS-CoV-2 Infection in Health Care Workers After a Single Dose of mRNA-1273 Vaccine Kalpana Gupta, MD, MPH; William J. O’Brien, MS; Pamela Bellino, MA; Katherine Linsenmeyer, MD; Sucheta J. Doshi, MD, MPH; Robert S. Sprague, MD; Michael E. Charness, MD JAMA Network Open

Question   Among a cohort of US frontline and essential workers infected with the original strain or the Delta or Omicron variants of SARS-CoV-2, is there a difference in COVID-19 symptoms or viral RNA load among those receiving mRNA vaccines compared with being unvaccinated?

Findings   In this prospective cohort study that included 1199 participants with SARS-CoV-2 infection, receipt of 2 or 3 mRNA vaccine doses before Delta infections and 3 mRNA vaccine doses before Omicron infections was significantly associated with milder COVID-19 (less frequently symptomatic, febrile, or medically attended or shorter duration of illness) compared with being unvaccinated. Receipt of 2 mRNA vaccine doses 14 to 149 days prior to either Delta or Omicron infection was significantly associated with lower viral RNA load.

Meaning   Among a cohort of US frontline and essential workers, recent vaccination with 2 or 3 mRNA vaccine doses, compared with being unvaccinated, was associated with attenuated COVID-19 symptoms and lower viral RNA load for Delta and Omicron variants of SARS-CoV-2 in some comparisons.

Importance   Data on the epidemiology of mild to moderately severe COVID-19 are needed to inform public health guidance.

Objective   To evaluate associations between 2 or 3 doses of mRNA COVID-19 vaccine and attenuation of symptoms and viral RNA load across SARS-CoV-2 viral lineages.

Design, Setting, and Participants   A prospective cohort study of essential and frontline workers in Arizona, Florida, Minnesota, Oregon, Texas, and Utah with COVID-19 infection confirmed by reverse transcriptase–polymerase chain reaction testing and lineage classified by whole genome sequencing of specimens self-collected weekly and at COVID-19 illness symptom onset. This analysis was conducted among 1199 participants with SARS-CoV-2 from December 14, 2020, to April 19, 2022, with follow-up until May 9, 2022, reported.

Exposures   SARS-CoV-2 lineage (origin strain, Delta variant, Omicron variant) and COVID-19 vaccination status.

Main Outcomes and Measures   Clinical outcomes included presence of symptoms, specific symptoms (including fever or chills), illness duration, and medical care seeking. Virologic outcomes included viral load by quantitative reverse transcriptase–polymerase chain reaction testing along with viral viability.

Results   Among 1199 participants with COVID-19 infection (714 [59.5%] women; median age, 41 years), 14.0% were infected with the origin strain, 24.0% with the Delta variant, and 62.0% with the Omicron variant. Participants vaccinated with the second vaccine dose 14 to 149 days before Delta infection were significantly less likely to be symptomatic compared with unvaccinated participants (21/27 [77.8%] vs 74/77 [96.1%]; OR, 0.13 [95% CI, 0-0.6]) and, when symptomatic, those vaccinated with the third dose 7 to 149 days before infection were significantly less likely to report fever or chills (5/13 [38.5%] vs 62/73 [84.9%]; OR, 0.07 [95% CI, 0.0-0.3]) and reported significantly fewer days of symptoms (10.2 vs 16.4; difference, −6.1 [95% CI, −11.8 to −0.4] days). Among those with Omicron infection, the risk of symptomatic infection did not differ significantly for the 2-dose vaccination status vs unvaccinated status and was significantly higher for the 3-dose recipients vs those who were unvaccinated (327/370 [88.4%] vs 85/107 [79.4%]; OR, 2.0 [95% CI, 1.1-3.5]). Among symptomatic Omicron infections, those vaccinated with the third dose 7 to 149 days before infection compared with those who were unvaccinated were significantly less likely to report fever or chills (160/311 [51.5%] vs 64/81 [79.0%]; OR, 0.25 [95% CI, 0.1-0.5]) or seek medical care (45/308 [14.6%] vs 20/81 [24.7%]; OR, 0.45 [95% CI, 0.2-0.9]). Participants with Delta and Omicron infections who received the second dose 14 to 149 days before infection had a significantly lower mean viral load compared with unvaccinated participants (3 vs 4.1 log 10 copies/μL; difference, −1.0 [95% CI, −1.7 to −0.2] for Delta and 2.8 vs 3.5 log 10 copies/μL, difference, −1.0 [95% CI, −1.7 to −0.3] for Omicron).

Conclusions and Relevance   In a cohort of US essential and frontline workers with SARS-CoV-2 infections, recent vaccination with 2 or 3 mRNA vaccine doses less than 150 days before infection with Delta or Omicron variants, compared with being unvaccinated, was associated with attenuated symptoms, duration of illness, medical care seeking, or viral load for some comparisons, although the precision and statistical significance of specific estimates varied.

There are limited data on the epidemiology of asymptomatic SARS-CoV-2 infections or mild to moderately severe COVID-19 disease, which constitute most SARS-CoV-2 infections and are believed to trigger and fuel SARS-CoV-2 outbreaks. 1 - 4 Although early reports from prospective community cohorts observed that COVID-19 vaccines attenuated COVID-19 symptoms, duration, and viral RNA shedding, 5 - 8 results have been mixed in recent studies, 9 - 11 which may be due to the waning of vaccine-associated immunity, 12 , 13 changes in virologic features and heightened immune evasion by the Delta 14 and Omicron variants, 15 or a combination of factors. Data are also limited from studies that conduct routine weekly testing for SARS-CoV-2 in the community or compare multiple SARS-CoV-2 strains within the same population.

This study describes a prospective cohort of essential and frontline workers in 6 US states with SARS-CoV-2 infection identified through weekly surveillance testing irrespective of symptoms and has 3 objectives. First, the study described COVID-19 by characterizing clinical and virologic features of infections with the original strains (origin), B.1.617.2 and all AY lineages (Delta), and B.1.1.529 and BA1 lineages (Omicron) and assessing associations between clinical characteristics and viral RNA load. Second, the study assessed the association of second and third doses of mRNA COVID-19 vaccines with the occurrence of symptomatic disease, symptoms, and viral load. Third, the study examined the potential transmissibility of COVID-19 by clinical presentation and vaccination status assessing viral viability by culture for the origin and Delta variant viruses.

The HEROES-RECOVER network consists of large prospective cohorts of health care personnel, first responders, and other essential and frontline workers, defined as those who work at least 20 hours per week in an occupation involving regular contact within 3 feet of others, such as workers in the following sectors: education, agriculture, food processing, transportation, solid waste collection, utilities, government services, childcare, environmental services, and hospitality. The study was conducted in 6 US states (Arizona, Florida, Minnesota, Oregon, Texas, and Utah) with participants recruited through ongoing occupational cohort studies, health management organization membership, and university or hospital system employment using a stratified enrollment approach according to age and occupational category (eMethods in Supplement 1 ). 16 , 17 All participants provided written informed consent, and their common protocols and procedures were approved by the institutional review boards at participating sites. The data for these analyses were collected from December 14, 2020, (after authorization of mRNA vaccines) to April 19, 2022, with follow-up until May 9, 2022, reported. The analysis was conducted among participants who tested positive for SARS-CoV-2, excluding those with SARS-CoV-2 infections prior to the study start date, infected prior to completion of a primary mRNA COVID-19 vaccine series (<14 days after dose 2), infected either less than 7 days or at greater than or equal to 150 days after receipt of dose 3, or who received the Ad26.COV2.S (Janssen/Johnson & Johnson) vaccine (eFigure 1 in Supplement 1 ).

Participants reported sociodemographic information, including self-reported race and ethnicity, history of SARS-CoV-2 infection, and chronic medical conditions (using a combination of fixed categories and open-ended text). Race and ethnicity were assessed due to the potential for differential vaccine receipt by race and ethnicity. Active surveillance for symptoms associated with COVID-19 (defined as presence of 1 or more of the following: fever, chills, cough, shortness of breath, sore throat, diarrhea, muscle aches, or a change in smell or taste) was conducted through weekly text messages, emails, and reports obtained directly from the participant.

COVID-19 vaccination status was reported by participants via online surveys and verified through direct upload of images of vaccination cards at all sites. In addition, electronic medical records, occupational health records, or state immunization registries were reviewed by site staff in Minnesota, Oregon, Texas, and Utah.

Participants self-collected midturbinate nasal swabs, irrespective of symptoms, each week and at the onset of COVID-19 symptoms. Specimens were shipped on cold packs and tested by qualitative reverse transcriptase–polymerase chain reaction (RT-PCR) assay at the Marshfield Clinic Research Institute. Virus lineage was determined by whole genome sequencing 18 or imputed as the predominant circulating virus (eMethods in Supplement 1 ). To estimate viral shedding, viral RNA load (hereafter referred to as viral load ) was assessed by quantitative RT-PCR assays conducted at the Wisconsin State Laboratory of Hygiene.

To assess virus viability, infectious virus particles were quantified by determination of plaque-forming units (PFU) per milliliter on Vero cells ectopically expressing TMPRSS3 and human ACE2 19 at St Jude Children’s Research Hospital. Infected cell monolayers were stained and PFUs were counted visually. Specimens were eligible for PFU assessment if the RT-PCR cycle threshold was less than 30, virus lineage had been confirmed, and the viral load was greater than 1 log 10 RNA copies/μL (eMethods in Supplement 1 ). Attempts to culture Omicron variant viruses were not successful.

SARS-CoV-2 infection lineage and vaccination status were the primary exposures of interest. Virus lineage was categorized as origin (defined as viruses circulating prior to recognized variants), Delta variant, or Omicron variant. The infection index date was defined as the date of illness onset or specimen collection if asymptomatic. Vaccination status was categorized by doses received relative to the infection index date: 2 doses received within 14 to 149 days prior to infection, 2 doses received at least 150 days prior to infection, and 3 doses received within 7 to 149 days prior to infection. The comparator group was unvaccinated.

Clinical outcomes were presence of symptomatic COVID-19 and, among cases of symptomatic COVID-19, the number of symptoms (0 to 19), presence of fever or chills, illness duration, number of days sick in bed for at least half the day, 20 hours of work missed due to illness, and medical care seeking. Clinical outcomes were self-reported via online surveys sent to participants on reporting of COVID-19 symptoms. Additional online surveys were sent after 7 days if symptoms were ongoing and on illness resolution; asymptomatic participants did not receive illness surveys but confirmed absence of symptoms at the time RT-PCR test results were communicated.

Virologic outcomes were viral load and PFU counts from self-collected specimens. When viral load was available from more than 1 specimen per infection, the primary analyses referred to the specimen with the highest viral load. Viral load is reported as overall viral load, peak viral load, time until peak, and time until load was less than 1. Viral load range was 10 to 6 million U/μL. PFU range was 20 to 11 000 U/mL. Both outcomes were analyzed and reported as log 10 units.

For COVID-19 clinical outcomes, odds ratios (ORs) were estimated with logistic regression. Mean differences for clinical and virologic outcomes were estimated with linear regression. All models were adjusted for race, ethnicity, and number of daily prescription medications for potential association with vaccination status and outcomes (eTables 3 and 4 in Supplement 1 ). Occupation was included in adjusted models for the association between virus lineage and missed work, but could not be included in models assessing the association of vaccination status and missed work due to collinearity with vaccine exposure. Analyses of viral load and PFU counts were adjusted for days between index and specimen collection dates. Model assumptions, including linearity, collinearity, and equal variance for linear models, were assessed and met.

Similar to previously published methods, 21 viral load by days since onset was estimated using bayesian linear hierarchical models with virus lineage or presence of symptomatic illness as exposures. Time since index date was fitted as a 3-knot natural spline. Models were fit among participants with at least 3 positive RT-PCR specimens and at least 1 specimen with quantifiable load. Viral load was predicted for positive specimens with too little virus to quantify using the linear relationship between the qualitative RT-PCR cycle threshold value and viral load. A sensitivity analysis required at least 2 quantifiable specimens (eMethods in Supplement 1 ). Models converged with R-hat less than 1.05 and effective sample size greater than 100.

Two-sided 95% CIs for ORs that did not include 1.0 and mean differences that did not include 0 were considered statistically significant. All analyses were conducted with SAS software, version 9.4 (SAS Institute), and R software, version 4.0.2 (R Foundation for Statistical Computing). Because of the potential for type I error due to multiple comparisons, findings should be interpreted as exploratory.

Of 7223 participants enrolled in active surveillance, 1710 were excluded due to prior SARS-CoV-2 infections; of the remaining 5513 participants, 997 (18.1%) were primary health care personnel (defined as physicians, physician assistants, nurse practitioners, and dentists), 1785 (32.4%) were nurses and other allied health professionals, 1218 (22.1%) were first responders, and 1513 (27.4%) were other essential/frontline workers. Participants completed a median (IQR) of 90.5% (78.1%-97.0%) of weekly surveillance reporting and specimen collection. Ninety-five percent of illness surveys were completed, with a range of 94.0% to 98.5% completion across the vaccination status groups. A total of 1493 infections were identified by RT-PCR testing. After exclusion criteria (eFigure 1 in Supplement 1 ), the study sample consisted of 1199 unique infections confirmed by RT-PCR testing during the 70-week study period ( Table 1 ); these consisted of 14.0% origin, 24.0% Delta, and 62.0% Omicron infections. Among the 1199 infected participants, the median (IQR) age was 41 (35.0-50.5) years, 714 (59.5%) were women, 232 (19.3%) were Hispanic, 22 (1.8%) were non-Hispanic Asian, 35 (2.9) were non-Hispanic Black, and 871 (72.6%) were non-Hispanic White (eTable 2 in Supplement 1 ). At least 1 chronic medical condition was reported for 27.4% of infected participants, and 27.7% took at least 2 medications per day. Characteristics of the uninfected participants not included in this study are shown in Table 1 .

Of the 1199 participants with SARS-CoV-2 infections, 352 (29.4%) were unvaccinated, 72 (6.0%) received the second dose 14 to 149 days before infection, 374 (31.2%) received the second dose at least 150 days before infection, and 401 (33.4%) received the third dose 7 to 149 days before infection ( Table 2 ; eTable 1 in Supplement 1 ). Of those who received the third dose, 11 (2.7%) were immunocompromised by self-report; dose 3 was considered primary series vaccination for these individuals. All participants received the same vaccine product for dose 1 and dose 2: 280 (62.3%) received BNT162b2 (Pfizer-BioNTech) and 166 (37.2%) received mRNA-1273 (Moderna) vaccines. For dose 3, a total of 22 of 401 individuals (5.5%) received a different mRNA vaccine product than their primary series.

To describe the clinical characteristics of infection by each viral lineage, the 352 episodes of infection that occurred among unvaccinated individuals (among the 1199 total infection episodes) were examined ( Table 3 ); of these, 44 (12.5%) were asymptomatic and 24 (6.8%) had symptoms not in the COVID-19 case definition. Asymptomatic infections were significantly more commonly associated with Omicron compared with Delta variants (OR, 5.6 [95% CI, 1.6-19.6]). When symptomatic, 64.8% of participants with COVID-19 reported fever or chills; the percentage was significantly higher for Delta compared with origin infection (84.9% vs 71.8%; OR, 2.2 [95% CI, 1.1-4.8]). Medical care seeking was significantly less common among Omicron infections (24.7%) compared with Delta (47.3%) infections (OR, 0.39 [95% CI, 0.2-0.8]), but was not significantly different among Omicron vs origin (36.9%) infections (OR, 1.8 [95% CI, (1.0-3.2)]).

Among symptomatic participants, COVID-19 symptoms associated with infection due to the Omicron variant lasted a mean (SD) of 12.3 (9.1) days, which was 2.9 (95% CI, −5.5 to −0.3) days fewer than origin (mean of 15.6 days) infection and 3.6 (95% CI, −6.6 to −0.7) days fewer than Delta (mean of 16.4 days) infection. Participants with Omicron infection spent a mean (SD) of 2.6 (3.1) days sick in bed, which was a mean of 1.2 (95% CI, −2.5 to −0.0) days fewer than those with origin infection and 2.0 (95% CI, −3.4 to −0.6) days fewer than those with Delta infection. Further findings regarding the associations between clinical and participant characteristics are in Table 3 and eTable 3 in Supplement 1 .

Vaccinated and unvaccinated participants were examined to assess potential vaccine-associated attenuation of disease. Among participants with Delta infections, illness was milder among those who were vaccinated, although the precision of these estimates varied ( Table 2 ). Participants who received the second dose 14 to 149 days before Delta infection compared with those unvaccinated were significantly less likely to be symptomatic (77.8% vs 96.1%; OR, 0.13 [95% CI, 0.0-0.6]) and spent significantly fewer days sick in bed (2.3 vs 4.9; difference, −2.3 [95% CI, −4.4 to −0.2] days). Symptomatic participants who received dose 2  at least 150 days prior to Delta infection compared with those who were unvaccinated were significantly less likely to report fever or chills (70.4% vs 84.9%; OR, 0.39 [95% CI, 0.2-0.9]) or seek medical care (31.5% vs 47.3%; OR, 0.51 [95% CI, 0.3-1.0]) and missed significantly fewer hours of work (47.1 vs 62.8; difference, −15.2 [95% CI, −26.4 to −4.1] hours). Participants who received the third vaccine dose 7 to 149 days prior to Delta infection compared with those who were unvaccinated were significantly less likely to have symptomatic illness (72.2% vs 77.8%; difference, 0.13% [95% CI, 0.0%-0.7%]) and, when symptomatic, were significantly less likely to report fever or chills (38.5% vs 84.9%; OR, 0.07 [95% CI, 0.0-0.3]) and spent significantly fewer days symptomatic (mean [SD] of 10.2 [5.4] days vs 16.4 [8.6]; difference, −6.1 [95% CI, −11.8 to −0.4] days).

Significant differences in clinical presentation between unvaccinated individuals and those who received 2 vaccine doses were not observed among Omicron variant infections. However, symptomatic participants who received the third vaccine dose 7 to 149 days prior to Omicron infection compared with those who were unvaccinated were significantly less likely to report fever or chills (51.5% vs 79.0%; OR, 0.25 [95% CI, 0.1-0.5]) or receive medical care (14.6% vs 24.7%; OR, 0.45 [95% CI, 0.2-0.9]) and missed significantly fewer hours of work (mean [SD] of 26.6 [25.3] vs 35.9 [31.5]; difference, −11.1 [95% CI, −18.4 to −3.9] hours), although guidance on postinfection isolation and returning to work changed during the Omicron wave. Participants who received the third vaccine dose 7 to 149 days prior to Omicron infection were more likely to be symptomatic than those who were unvaccinated (88.4% vs 79.4%; OR, 2.0 [95% CI, 1.1-3.5]).

For 886 participants (73.8%), the specimen with the highest viral load was their first positive specimen collected. Mean viral load did not differ by demographic characteristics (eTable 4 in Supplement 1 ). Symptomatic infections compared with asymptomatic infections had a significantly higher mean viral load (mean difference, 1.3 log 10 copies/μL [95% CI, 0.3-1.6]; Figure 1 ); this difference occurred consistently for Delta and Omicron infections (eTable 5 in Supplement 1 ). For all studied lineages, mean viral load was significantly higher for infections with fever or chills (3.8 vs 3.4 log 10 copies/μL; mean difference, 0.4 [95% CI, 0.2-0.7]), those with 8 or more symptoms (3.9 vs 3.0 log 10 ; mean difference, 0.9 [95% CI, 0.6-1.3] log 10 copies/μL), and those with symptoms lasting at least 14 days (4.0 vs 3.5; mean difference, 0.6 [95% CI, 0.3-0.9] log 10 copies/μL) ( Figure 1 and eTable 5 in Supplement 1 ).

Participants with Delta and Omicron infections who received the second vaccine dose 14 to 149 days before infection had a significantly reduced mean viral load compared with unvaccinated participants (3 vs 4.1 log 10 copies/μL; mean difference, −1.0 [95% CI, −1.7 to −0.2] log 10 copies/μL for Delta; 2.8 vs 3.5 log 10 copies/μL; mean difference, −1.0 [95% CI, −1.7 to −0.3] log 10 copies/μL for Omicron) ( Figure 1 ).

Viral load over the course of infection was estimated using a bayesian hierarchical model among 407 vaccinated and unvaccinated participants who contributed at least 3 specimens positive for SARS-CoV-2 (eTable 6 in Supplement 1 ). The time from SARS-CoV-2 detection to mean peak viral load was 2.5 days for all 3 virus lineages. Overall mean viral load for origin and Delta infections were higher than for Omicron (mean difference, 0.2 [95% credible interval (CrI), 0.0-0.4] log 10 copies/μL for origin infection; mean difference, 0.2 [95% CrI, 0.1-0.5] log 10 copies/μL for Delta infection) ( Figure 2 A). During the estimated peak, viral load did not differ by viral lineage. The estimated time of decline in viral load from peak to a count of less than 1 log 10 copies/μL was 11 days for both origin and Delta infection and 10 days for Omicron infection ( Figure 2 A).

Across viral lineages, the mean viral load for asymptomatic infections was lower than for symptomatic COVID-19 (mean difference, 0.7 [95% CrI, 0.4-1.1] log 10 copies/μL) ( Figure 2 B). Estimated peak viral load was also lower for asymptomatic infections (3.8 [95% CrI, 3.4-4.2] log 10 copies/μL) than for symptomatic COVID-19 (4.5 [95% CrI, 4.4-4.6] log 10 copies/μL) (eTable 6 in Supplement 1 ). The estimated time from peak viral load to a count of less than 1 log 10 copies/μL was 8 days for asymptomatic infections and 10 days for symptomatic COVID-19 ( Figure 2 B).

Counts of viable viral particles were only examined for unvaccinated participants infected with origin viruses and among both vaccinated and unvaccinated participants infected with the Delta variant ( Figure 1 ). Mean PFU was significantly higher for participants with symptomatic COVID-19 compared with those with asymptomatic infections (4.2 vs 3; mean difference, 1.9 [95% CI, 0.6-3.2] PFU/mL). Mean PFU was significantly lower among participants who received dose 2 at least 150 days before breakthrough infection or the third dose 7 to 149 days before breakthrough infection compared with unvaccinated participants (4.1 vs 4.8; mean difference, −0.8 [95% CI, −1.5 to −0.1] PFU/mL and 3.1 vs 4.8; mean difference, −1.6 [95% CI, −2.8 to −0.5] PFU/mL) ( Figure 1 ).

In this prospective cohort of health care personnel, first responders, and other essential and frontline workers followed up from December 14, 2020, to April 19, 2022, recent receipt of 2 or 3 doses of COVID-19 mRNA vaccine 14 to 149 days prior to Delta or Omicron BA.1 SARS-CoV-2 infection was variably associated with a milder clinical presentation, shorter illness duration, less medical care seeking, and lower viral load, although the precision of specific estimates varied.

The differences between Omicron and Delta infections in clinical and virologic characteristics observed may help explain the previously reported increased transmissibility of the Omicron variant. 3 , 22 , 23 COVID-19 symptoms associated with Omicron appeared to be milder and of a shorter duration than COVID-19 associated with Delta by many indicators. Additionally, milder symptoms that lasted fewer days and shorter duration of illness was associated with lower viral RNA shedding. In this cohort, 20.2% of infections due to Omicron were asymptomatic, similar to another recent report. 24 In contrast, only 3.9% of Delta infections were asymptomatic and were associated with a longer duration of illness. Across all viral lineages in this study, infected participants without symptoms shed viral RNA for about 12 days after peak viral load compared with symptomatic participants with symptoms, who shed viral RNA for about 18 days. During the period of detection, the viral load of Omicron BA.1 infections was similar to Delta infections and higher than infections with origin strains. Although viral RNA shedding cannot be directly attributable to transmission, the relatively high viral load of Omicron infections together with the higher frequency of asymptomatic infection supports previous studies suggesting an association with increased transmission, 21 , 25 particularly during the first 3 to 5 days when viral load peaked.

A study from the HEROES-RECOVER cohort previously reported that participants with origin and other pre-Delta virus infections (from December 2020 to April 2021) after at least 1 dose of mRNA COVID-19 vaccine had lower viral load, were less likely to have febrile symptoms, and had shorter illnesses with fewer days spent sick in bed compared with unvaccinated participants. 8 Similarly, in the current study, Delta-associated breakthrough infections 14 to 149 days after dose 2 had lower viral loads compared with those in unvaccinated participants; further, breakthrough infections 14 to 149 days after dose 2 or 7 to 149 days after dose 3 had shorter illnesses, with lower counts of viable and presumed infectious virus. For Omicron infections, only participants with a breakthrough infection after dose 3 showed consistent signs of vaccine attenuation of COVID-19, such as being less likely to have febrile symptoms or seek medical care. It is possible that the recall of immunologic memory that reduces viral replication and accelerates elimination of virally infected cells that may underlie vaccine attenuation of disease 26 wanes with time, similar to waning in neutralizing antibodies 27 and clinical effectiveness of mRNA vaccines. 12

This study has several limitations. First, although this was among the largest studies of its kind with routine surveillance testing for SARS-CoV-2, stratification by virus lineage and vaccine exposures resulted in relatively small subgroups, which reduced the precision of estimates. Second, although several relevant confounders were controlled for, unmeasured confounding in this observational study could have occurred. Limitations in sample size and the ability to adjust models for potential confounders made it particularly difficult to interpret unexpected findings, such as a higher percentage of individuals with symptomatic disease among those vaccinated with the third vaccine dose 14 to 149 days before Omicron infection compared with those who were unvaccinated. Third, findings regarding virologic features of COVID-19 are limited to virus present in the nasal cavity, self-collected with midturbinate nasal swabs. Therefore, possible differences between variants in viral RNA present in the full nasopharynx or lower respiratory system could not be examined. Fourth, due to methodological obstacles culturing Omicron variant viruses, virus viability could only be examined with origin and Delta viruses. Fifth, the detection of viral RNA and isolation of viable virus through virus culture are not direct measures of virus transmissibility, although evidence is emerging for SARS-CoV-2 25 and both are associated with transmission of other viral infections. 28 - 30 Sixth, findings on missed work hours should be interpreted with caution because guidance on postinfection isolation and returning to work changed over time and work-related isolation protocols were not considered. Seventh, participant-reported symptoms and duration of illness may be subject to recall and confirmation biases. Eighth, these findings may not generalize to all population in the US, especially given the relatively low percentage of participants who were not White.

In a cohort of US essential and frontline workers with SARS-CoV-2 infections, recent vaccination with 2 or 3 mRNA vaccine doses less than 150 days before infection with Delta or Omicron variants, compared with being unvaccinated, was associated with attenuated symptoms, duration of illness, medical care seeking, and viral load for some comparisons, although the precision and statistical significance of specific estimates varied.

Corresponding Author: Ashley L. Fowlkes, ScD, MPH, Influenza Division, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA 30333 ( [email protected] ).

Accepted for Publication: September 20, 2022.

HEROES-RECOVER Authors: Mark G. Thompson, PhD; Sarang K. Yoon, DO, MOH; Allison L. Naleway, PhD; Jennifer Meece, PhD; Thomas P. Fabrizio, PhD; Alberto J. Caban-Martinez, DO, PhD, MPH; Jefferey L. Burgess, MD, MS, MPH; Manjusha Gaglani, MBBS; Lauren E.W. Olsho, PhD; Allen Bateman, PhD; Jessica Lundgren, RN, BSN; Lauren Grant, MS; Andrew L. Phillips, MD, MOH; Holly C. Groom, MPH; Elisha Stefanski, BS; Natasha Schaefer Solle, PhD, RN; Katherine Ellingson, PhD; Karen Lutrick, PhD; Kayan Dunnigan, MPH; Meredith G. Wesley, MPH; Kyley Guenther, BS; Angela Hunt, MLT, ASCP; Josephine Mak, MPH; Kurt T. Hegmann, MD, MPH; Jennifer L. Kuntz, PhD; Adam Bissonnette, MS; James Hollister, MS; Spencer Rose, BS; Tyler C. Morrill, MS; Karley Respet, BS; Ashley L. Fowlkes, ScD, MPH; Matthew S. Thiese, PhD, MSPH; Patrick Rivers, MPP; Meghan K. Herring, MPH; Marilyn J. Odean, MS; Young M. Yoo, MSPH; Matthew Brunner, BS; Edward J. Bedrick, PhD; Deanna E. Fleary, MsC; John T. Jones, MSPH; Jenna Praggastis, BS; James Romine, PhD, MPH; Monica Dickerson, BS; Sana M. Khan, MPH; Julie Mayo Lamberte, MSPH; Shawn Beitel, MSc; Richard J. Webby, PhD; Harmony L. Tyner, MD.

Affiliations of HEROES-RECOVER Authors: Centers for Disease Control and Prevention COVID-19 Response Team, Atlanta, Georgia (Thompson, Grant, Mak, Fowlkes, Yoo, Jones, Dickerson, Lamberte); Rocky Mountain Center for Occupational and Environmental Health, Department of Family and Preventive Medicine, University of Utah Health, Salt Lake City, Utah (Yoon, Phillips, Hegmann, Thiese, Brunner, Praggastis); Kaiser Permanente Northwest Center for Health Research, Portland, Oregon (Naleway, Groom, Kuntz); Marshfield Clinic Research Institute, Marshfield, Wisconsin (Meece, Stefanski, Bissonnette); St Jude Children's Research Hospital, Memphis, Tennessee (Fabrizio, Webby); Leonard M. Miller School of Medicine, University of Miami, Miami, Florida (Caban-Martinez, Solle); Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, Arizona (Burgess, Ellingson, Hollister, Bedrick, Romine, Khan, Beitel); Baylor Scott and White Health, Temple, Texas (Gaglani, Dunnigan, Rose); Texas A&M University College of Medicine, Temple (Gaglani); Abt Associates Inc, Rockville, Maryland (Olsho, Wesley, Morrill, Herring, Fleary); Wisconsin State Laboratory of Hygiene, Madison (Bateman, Guenther); St Luke’s Regional Health Care System, Duluth, Minnesota (Lundgren, Hunt, Respet, Tyner); College of Medicine - Tucson, University of Arizona, Tucson (Lutrick, Rivers); Whiteside Institute for Clinical Research, St Luke’s, Duluth, Minnesota (Odean).

Author Contributions : Drs Thompson and Ms Grant had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Thompson, Yoon, Naleway, Meece, Fabrizio, Caban-Martinez, Burgess, Gaglani, Olsho, Grant, Phillips, Mak, Hegmann, Lutrick, Hunt, Fowlkes, Thiese, Herring, Respet, Bruner, Mayo Lamberte, Beitel, Webby, Tyner.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Thompson, Yoon, Caban-Martinez, Burgess, Grant, Lundgren, Fowlkes, Thiese, Jones, Dickerson, Mayo Lamberte.

Critical revision of the manuscript for important intellectual content: Thompson, Yoon, Naleway, Meece, Fabrizio, Caban-Martinez, Burgess, Gaglani, Olsho, Grant, Phillips, Groom, Bateman, Stefanski, Schaefer Solle, Ellingson, Dunnigan, Wesley, Mak, Hegmann, Kuntz, Guenther, Bissonnette, Lutrick, Rose, Morrill, Hunt, Fowlkes, Thiese, Hollister, Herring, Respet, Yoo, Bruner, Rivers, Fleary, Odean, Praggastis, Bedrick, Romine, Khan, Beitel, Webby, Tyner.

Statistical analysis: Thompson, Grant, Mak, Thiese, Yoo, Jones, Bedrick.

Obtained funding: Thompson, Yoon, Burgess, Olsho, Thiese.

Administrative, technical, or material support: Thompson, Yoon, Naleway, Meece, Fabrizio, Caban-Martinez, Burgess, Gaglani, Olsho, Phillips, Groom, Bateman, Stefanski, Schaefer Solle, Ellingson, Wesley, Lundgren, Hegmann, Kuntz, Bissonnette, Lutrick, Morrill, Hunt, Fowlkes, Thiese, Hollister, Herring, Respet, Bruner, Rivers, Fleary, Praggastis, Dickerson, Mayo Lamberte, Khan, Beitel, Webby, Tyner.

Supervision: Thompson, Yoon, Meece, Olsho, Lundgren, Hegmann, Kuntz, Bissonnette, Thiese, Bruner, Praggastis, Webby.

Conflict of Interest Disclosures: Dr Naleway reported receiving research funding from Pfizer and Vir Biotechnology outside the submitted work. Drs Webby and Fabrizio are funded by the National Institute of Allergy and Infectious Diseases, National Institutes of Health (grant no. HHSN2272201400006C), and ALSAC. Dr Gaglani reported receiving grants from Janssen and Pfizer and serving as co-chair of the Infectious Diseases and Immunization Committee, Texas Pediatric Society, Texas Chapter of the American Academy of Pediatrics. Dr Hegmann reported receiving personal fees from the American College of Occupational and Environmental Medicine/Reed Group for work as an editor of peer-reviewed guidelines, including COVID-19 guidelines, outside the submitted work. Dr Kuntz reported receiving research grant funding to the institution from Vir Biotechnology, Pfizer, and Novartis outside the submitted work. No other disclosures were reported.

Funding/Support : Funding was provided in whole or in part by federal funds from the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention (CDC), under contract numbers 75D30120R68013 awarded to Marshfield Clinic Research Laboratory, 75D30120C08379 to University of Arizona, and 75D30120C08150 awarded to Abt Associates, Inc.

Role of the Funder/Sponsor: The CDC was involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer : The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Data Sharing Statement: Data will be available by CDC when objectives of the research are complete.

Additional Information: HEROES-RECOVER Network includes Arizona Healthcare, Emergency Response and Other Essential Workers Surveillance Study (HEROES) and Research on the Epidemiology of SARS-CoV-2 in Essential Response Personnel (RECOVER).

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