case study in tableau

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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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 .

Have an idea? Let's discuss!

Cyclistic Case Study Using Spreadsheets, SQL and Tableau

In this case study, I analyze historical data from a Chicago based bike-share company in order to identify trends in how their customers use bikes differently. The main tools I use are spreadsheets, SQL and Tableau. Here are the highlights:

Tableau Dashboard: Cyclistic Bikeshare in Chicago

Slides: Where Rubber Meets Road in Converting Casual Riders to Cyclistic Members

GitHub: Cyclistic Case Study Repository

A more in-depth breakdown of the case study scenario is included below, followed by my full report.

Cyclistic is a bike-share company based in Chicago with two types of customers. Customers who purchase single-ride or full-day passes are known as casual riders , while those who purchase annual memberships are known as members . Cyclistic’s financial analysts have concluded that annual members are much more profitable than casual riders. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships.

The marketing analytics team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, the team will design a new marketing strategy to convert casual riders into annual members. The primary stakeholders for this project include Cyclistic’s director of marketing and the Cyclistic executive team. The Cyclistic marketing analytics team are secondary stakeholders.

Defining the problem

The main problem for the director of marketing and marketing analytics team is this: Design marketing strategies aimed at converting Cyclistic’s casual riders into annual members. There are three questions that will guide this future marketing program. For my scope on this project, I will anlyze the first question:

1) How do annual members and casual riders use Cyclistic bikes differently? 2) Why would casual riders buy Cyclistic annual memberships? 3) How can Cyclistic use digital media to influence casual riders to become members?

By looking at the data, we will be able to first get a broad sense of certain patterns that are occurring in the two different groups. Understanding the differences will provide more accurate customer profiles for each group. These insights will help the marketing analytics team design high quality targeted marketing for converting casual riders into members. For the Cyclistic executive team, these insights will help Cyclistic maximize the number of annual members and will fuel future growth for the company.

Business task

Analyze historical bike trip data to identify trends in how annual members and casual riders use Cyclistic bikes differently. #

Data sources

We’ll be using Cyclistic’s historical bike trip data from the last 12 months, which is publicly available here . The data is made available by Motivate International Inc. under this license . The data is stored in spreadsheets. There are 12 .CSV files total:

It is structured data, organized in rows (records) and columns (fields). Each record represents one trip, and each trip has a unique field that identifies it: ride_id . Each trip is anonymized and includes the following fields:

Bike station data that is made publicly available by the city of Chicago will also be used. It can be downloaded here . In terms of bias and credibility, both data sources we are using ROCCC:

Reliable and original: this is public data that contains accurate, complete and unbiased info on Cyclistic’s historical bike trips. It can be used to explore how different customer types are using Cyclistic bikes.

Comprehensive and current: these sources contain all the data needed to understand the different ways members and casual riders use Cyclistic bikes. The data is from the past 12 months. It is current and relevant to the task at hand. This is important because the usefulness of data decreases as time passes.

Cited: these sources are publicly available data provided by Cyclistic and the City of Chicago. Governmental agency data and vetted public data are typically good sources of data.

Data cleaning and manipulation

Microsoft excel: initial data cleaning and manipulation.

Our next step is making sure the data is stored appropriately and prepared for analysis. After downloading all 12 zip files and unzipping them, I housed the files in a temporary folder on my desktop. I also created subfolders for the .CSV files and the .XLS files so that I have a copy of the original data. Then, I launched Excel, opened each file, and chose to Save As an Excel Workbook file. For each .XLS file, I did the following:

• Formatted as custom DATETIME
• Format > Cells > Custom > yyyy-mm-dd h:mm:ss
• Calculated the length of each ride by subtracting the column started_at from the column ended_at (example: =D2-C2 )
• Formatted as TIME
• Format > Cells > Time > HH:MM:SS (37:30:55)
• Calculated the date of each ride started using the DATE command (example: =DATE(YEAR(C2),MONTH(C2),DAY(C2)) )
• Format > Cells > Date > YYYY-MM-DD
• Entered the month of each ride and formatted as number (example: January: =1 )
• Format > Cells > Number
• Entered the year of each ride and formatted as general
• Format > Cells > General > YYYY
• Calculated the start time of each ride using the started_at column
• Calculated the end time of each ride using the ended_at column
• Calculated the day of the week that each ride started using the WEEKDAY command (example: =WEEKDAY(C2,1) )
• Formatted as a NUMBER with no decimals
• Format > Cells > Number (no decimals) > 1,2,3,4,5,6,7
• Note: 1 = Sunday and 7 = Saturday

After making these updates, I saved each .XLS file as a new .CSV file.

BigQuery: further data cleaning and manipulation via SQL

Since these datasets are so large, it makes sense to move our analysis to a tool that is better suited for handling large datasets. I chose to use SQL via BigQuery .

In order to continue processing the data in BigQuery, I created a bucket in Google Cloud Storage to upload all 12 files. I then created a project in BigQuery and uploaded these files as datasets. I’ve provided my initial cleaning and transformation SQL queries here for reference: initial_setup_query.sql

The results from the COUNT DISTINCT query for each table are very interesting. We can see that the three summer months have the highest trip counts, followed by alternating spring and fall months before ending with winter months:

Create quarterly tables

In order to perform analysis by season, let’s combine these tables. We’ll create Q1, Q2, Q3 and Q4 tables for analysis. We’ll have two Q1 tables– one for 20221 and one for 2022 – since we have FEB/MAR data from 2021 and JAN data from 2022:

• Table 1) 2021_Q1 -> FEB(02), MAR(03)
• Table 2) 2021_Q2 -> APR(04), MAY(05), JUN(06)
• Table 3) 2021_Q3 -> JUL(07), AUG(08), SEP(09)
• Table 4) 2021_Q4 -> OCT(10), NOV(11), DEC(12)
• Table 5) 2022_Q1 -> JAN(01)

We’ll first create 2021_Q2 and then repeat for the remaining four tables:

Clean and transform day of week

Some additional data cleaning is needed on the new table. First, we’ll update the format for day_of_week from FLOAT to STRING . Then, we’ll change the values from numbers to their corresponding day names (i.e. 1 = Sunday, 7 = Saturday. We’ll start with 2021_Q1 and repeat for the remaining four tables:

Delete old tables

Now that we have our tables organized into quarters, we can delete the original monthly tables from BigQuery. We no longer need the monthly tables since the data is available in the quarter tables. Also, it costs money to store these datasets in BigQuery.

Analysis #1: Exploratory

2021_q1 - quarterly data exploration.

We’ll select a few columns from 2021_Q1 to preview in a temporary table. This will help give us an idea of potential trends and relationships to explore further:

The above query returned 278,119 rows. That is the number of recorded trips we have data for in this quarter. Let’s dive deeper into those trip totals.

Total trips

We’ll create total columns for overall, annual members and casual riders. We’ll also calculate percentages of overall total for both types:

Of the 278,118 total trips in 2021_Q1, 66% were from annual members while 34% were from casual riders.

Average ride lengths

How does average ride_length differ for these groups?

We can see that casual riders average about 23 more minutes per ride. That seems like a pretty big difference. What influence are outliers having on these averages? Let’s investigate.

Max ride lengths

We’ll look at the maximum values for ride_length to see if anything extreme is influencing the casual rider average:

As we suspected, the casual riders average ride_length was significantly impacted by at least one outlier. The longest trip duration for casual riders was 528 hours, or 22 days. Meanwhile, the longest for annual was about 26 hours.

Let’s take a look at the top 100 highest ride_length values for casual riders to confirm there is more than one outlier affecting the average:

Median ride lengths

Since there are more than a few outliers impacting the average, we’re going to use median instead of average. Median will be more accurate for our analysis:

Now we see a much closer number, with 18 minutes for casual riders and 10 minutes for annual members.

Busiest day for rides

Let’s see which day has the most rides for annual members and casual riders:

Unsurprisingly, Saturday is the most popular day for both annual members and casual riders.

Median ride length per day

Let’s look at the median ride lengths per day for both annual members and casual riders. Since Saturday is the most popular overall, do we think it will also have the highest median ride length?

Very interesting! The median ride length for casual riders on the top five days (SUN, SAT, MON, TUE, WED) is nearly double the amount for annual members on their top five days (SAT, SUN, MON, TUE, WED).

Total rides per day

Let’s look at total rides per day. We’ll create columns for overall total, annual members and casual riders:

Start stations

Next, we’ll look at the most popular start stations for trips. We’ll again include columns for overall, annual member and casual rider totals per start station:

We can begin to see some interesting patterns in the start station data. It looks like casual riders and annual members tend to favor different regions for beginning their trips. By updating the ORDER BY function to sort by casual DESC and member DESC in two separate queries, we can compare the top ten start stations for both:

Wow! There is only one start station that cracks the top ten for both lists. The Clark St & Elm St start station is ranked #1 for annual members and #10 for casual riders. The casual riders seem to favor stations near the water like Lake Shore Dr & Monroe St and Streeter Dr & Grand Ave , while annual members frequent start stations in the River North neighborhood like Dearborn St & Erie St and Kingsbury St & Kinzie St .

An initial hypothesis for casual riders could be that they tend to favor start stations near the water and close to tourist attractions because they use bikes for weekend entertainment. An initial hypothesis for annual members could be that they tend to favor start stations in downtown, retail areas because they are using bikes for their work commutes and shopping trips.

Quarterly data exploration (cont.)

Instead of walking through each quarter like we’ve done for 2021_Q1, I will instead provide links to the full SQL files. The queries used are similar to the ones above:

• analysis_2021_Q1.sql
• analysis_2021_Q2.sql
• analysis_2021_Q3.sql
• analysis_2021_Q4.sql
• analysis_2022_Q1.sql

I’ll included some high-level quarterly analysis notes in the next section.

Analysis #2: Summary

Full_year - trends, relationships and insights.

In order to analyze all twelve months together, we’ll combine the five quarterly tables into one table. The queries used to accomplish this are included here for reference. I’ve also provided the SQL file used for full year analysis: analysis_full_year.sql .

For a summary and overall visualization of my full year analysis, please visit the Tableau Public dashboard I created here: Tableau Dashboard: Cyclistic Bikeshare in Chicago . I will also highlight some of the interesting trends and relationships I discovered below.

Annual Members vs Casual Riders

Seasonal trends

Summer vs winter.

The busiest time of year for overall bike trips is Q3– July, August and September. This makes sense because these months are mainly summer time. Bike riding is better suited for warmer weather, which is also why we see a major drop-off in total rides during the winter months of Q1– January, February and March.

Annual members outnumbered casual riders in every quarter except Q3. Interestingly, the annual members nearly doubled the casual ridership in Q1 and Q4 while only slightly edging them out in Q2.

Median ride length

We can see that casual riders consistently have longer rides than annual members.

Day of week

Which days of the week have the highest number of rides for casual riders vs annual members? Let’s look at the mode for each quarter and for the full year:

Casual riders were extremely consistent, with Saturday revealing itself as their preferred day of week for each quarter and across the full year. Meanwhile, the annual members looked to favor the middle of the week for their bike use. The most popular day for them acrosss the full year was Wednesday . Let’s see how the total rides for each day stack up for both groups:

How about median ride length per day of week for both groups?

A few fascinating insights from the above chart:

U-shape pattern Sunday and Saturday are favored by both groups for longer rides, while ride duration decreases towards the middle of the week before increasing again on Friday. This results in a u-shape trend for both groups in the above chart, although it is much more dramatic for casual riders.

Range differences For annual members, difference between their longest day and their shortest day is 1 minute and 44 seconds. For casual riders, difference is 4 minute and 57 seconds. That is a 185.58% increase in difference for casual riders.

Annual members: day-to-day consistency The annual members may have shorter ride lengths when compared to casual riders, but they are extremely consistent with their bike use day-over-day.

Casual riders: weekend warriors The daily median ride length for casual riders is consistently higher than that of annual members. The range of their ride length duration varies at a greater amount than that of annual members. Sundays and Saturdays stand out as their longest ride days.

Do members and casual riders have different preferences for bike type? Are classic bikes more popular than electric bikes?

We can see that classic bikes are favored by both groups. Let’s look at the percentages of bike type use within each group:

Looking at the above, we might ask what exactly is a docked bike and why are only casual riders using them?

We can now see from the above charts that docked bikes are the culprit for the outliers affecting our ride length averages from earlier in our analysis. This is something we should discuss with our team further and address.

Start and end station use

In the Tableau Dashboard I created, which is again available here , there is a worksheet that allows the exploration of start and end station use by members, casual riders and combined overall rides. The snapshot below is from the overall view. While interacting with the dashboard, we can see that casual riders have a higher max than annual members. Annual members have a lower max, but we can see more colors represented across the member map versus the consistent coloring across the casual map. This tells us that rides by members are more distributed across stations while rides by casual riders are more top heavy in that a huge chunk are happening at the same few stations.

Stakeholder presentation and dashboard

I’ve provided links below for my dashboard and shareholder presentation, which includes the following:

• A summary of my analysis
• Supporting visualizations and key findings
• Three recommendations based on my analysis

Presentation: Where Rubber Meets Road in Converting Casual Riders to Cyclistic Members

Tableau Fundamentals Case Study

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Tableau launches generative AI assistant, updates platform

The longtime analytics vendor made a copilot for data preparation generally available with similar tools for data cataloging and dashboard development and analysis expected soon..

• Eric Avidon, Senior News Writer

Tableau's first generative AI assistant is now generally available.

Tableau earlier this month launched its second platform update of 2024, which included revealing that the first two of its generative AI assistants will be generally available before the end of July and a third will be released in August.

The first of those -- Einstein Copilot for Tableau: Prep -- became generally available on July 10.

The vendor first revealed plans to develop generative AI capabilities in May 2023 when it unveiled Tableau Pulse and Tableau GPT.

Pulse, an insight generator that automatically monitors data for metric changes and uses natural language to alert users, became generally available in February . Tableau GPT, meanwhile, was renamed Einstein Copilot for Tableau and moved into beta testing in April .

Now, Tableau is rolling out its Copilots. Following Einstein Copilot's for Tableau: Prep, Einstein Copilot for Tableau: Catalog is expected to be generally available before the end of July with Einstein Copilot for Tableau: Web Authoring to follow by the end of August.

With the launch of generative AI assistants that will enable customers to use natural language to prepare and analyze data, Tableau joins data management and analytics vendors such as AWS, Domo, Microsoft and MicroStrategy that have already made generative AI assistants generally available.

Numerous others, including Qlik , data engineering specialist DBT Labs and data preparation vendor Alteryx, have unveiled plans to develop similar assistants but have not yet moved them out of preview.

Beyond keeping pace with its rivals in releasing generative AI tools, Tableau's generative AI capabilities are largely in step with those being developed by other vendors, according to Doug Henschen, an analyst at Constellation Research. And in some ways, Tableau's generative AI capabilities -- which extend to data cataloguing -- go beyond what other analytics specialists are providing.

"Tableau is going GA later than some of its competitors, but capabilities are pretty much in line with or more extensive than what you're seeing from others," Henschen said.

Beyond revealing that its generative AI assistants are about to be released, Tableau 2024.2 includes such features as making Pulse embeddable in applications.

Based in Seattle and a subsidiary of CRM giant Salesforce, Tableau is a longtime analytics vendor. Tableau's first 2024 platform update was highlighted by the launch of Pulse while its final 2023 update featured new embedded analytics capabilities.

Tableau's latest

Generative AI assistants are proliferating because of their potential to enable non-technical workers within enterprises to work with data and enable data experts to be more efficient.

Largely due to the complexity of analytics platforms, which require coding to carry out most tasks and data literacy training to interpret data, analytics growth within organizations has long been stagnant with studies showing that only about one-quarter of all employees regularly work with data.

Vendors have attempted to break through that barrier by simplifying the use of their tools with natural language processing (NLP) and low-code/no-code features. The NLP features, however, are limited by small vocabularies that require highly specific business phrasing while low-code/no-code features are limited because they only enable basic tasks.

As a result, little progress has been made toward more widespread use of analytics, which has been shown to lead to growth better than decisions made without the use of data.

Generative AI has the potential to change that. Large language models (LLMs) such as ChatGPT and Google Gemini have vocabularies as large as any dictionary. In addition, they are able to interpret intent. As a result, they enable the true natural language interactions that open data exploration and analysis for non-technical users and reduce time-consuming coding requirements for data experts.

In response to the improvements in generative AI technology marked by OpenAI's November 2022 launch of ChatGPT, many data management and analytics vendors have since made generative AI a focal point of their product development .

Tech giants AWS, Google and Microsoft have all invested heavily in generative AI as have specialists such as Tableau and its peers. And following the better part of a year when many vendors unveiled plans to develop generative AI capabilities but did not make them generally available, now a wave of tools are hitting the market.

Those that are now generally available are having their desired effect, according to Henschen.

"In almost all cases I’m seeing improved productivity for existing users and a marginal broadening of usability for business users," he said.

Now generally available, Einstein Copilot for Tableau: Prep enables customers describe calculations in natural language which the tool interprets and replies with a calculation that can be applied to a user's data to add calculated fields to their Tableau Prep flows.

Before the launch of the generative AI assistant, creating formulas for calculated fields in Tableau Prep required expertise in objects, fields, functions and limitations that could only be carried out by those with significant training.

Once released later this month, Einstein Copilot for Tableau: Catalog will enable users to add descriptions for data sources, workbooks and tables with one click. A "Draft for Me" button instructs the tool to generate a description of a data asset which can then be reviewed by the user to edit and then publish to help make data more easily discoverable in Tableau's data catalog.

In August, Einstein Copilot for Tableau: Web Authoring will enable users to explore data in natural language directly from Tableau Cloud Web Authoring. The tool will interpret instructions to produce visualizations, formulate calculations and suggest recommended follow-up questions to enable deeper data exploration.

Given the reduction in coding the Copilots enable, Tableau's generative AI assistants will "absolutely" serve their intended purpose of making experts more efficient and enabling more people withing organizations to work with data, according to Mike Leone, an analyst at TechTarget's Enterprise Strategy Group.

" Whether for an expert or someone [SS1]   [EA2]  just getting started, the goal of Einstein Copilot is to boost efficiency and productivity," he said.

For example, data experts can now streamline complex data modeling and predictive analysis or automate routine data prep tasks while generalists can engage with user-friendly interfaces to visualize and analyze.

Of particular benefit is that Tableau is planning to provide generative AI assistants for different parts of its overall platform, Leone continued.

"One of the pieces I like about Einstein Copilot is the unique value it can provide in clear delineation stages of the data and AI lifecycle with Prep, Catalog and Web Authoring," he said. "And it enables Tableau to continue expanding into other areas based on customer feedback and adoption."

Henschen, meanwhile, noted that the generative AI assistants for Tableau Web Authoring and Tableau Prep are relatively similar to the generative AI assistants being introduced by other analytics vendors.

However, adding a generative AI assistant for data cataloguing represents some differentiation among analytics specialists.

" Einstein Copilot for Tableau: Catalog is unique to Tableau among analytics and BI vendors" Henschen said. "But it’s similar to GenAI implementations being done by a few data catalog vendors."

In addition to the generative AI assistants, Tableau's latest platform update includes the following:

• Embedding for Pulse, a web component that enables developers to embed Pulse in the work applications where business users do their work so they don't have to toggle between those applications and the Tableau environment to get generative AI-powered insights.
• Multi-Fact Relationships, a feature that lets users relate disparate datasets that have shared dimensions such as time or geography to perform multi-fact analysis and includes guidance and tips to help users work with underlying data models.
• Viz Extensions, a tool that enables customers to develop their own data visualization type and add it to their visualization library.
• The addition of Tableau to Apple devices, including two Mac options.
• Subrange Extract Refreshes, a tool that allows users to refresh data for small subsets within a larger dataset to reduce the cost of keeping relevant data current.
• Data Connect to help data engineers and administrators move on-premises or private cloud data into Tableau Cloud using Tableau Bridge.

Among the non-Copilot capabilities, making Pulse embeddable is perhaps the most significant, according to Henschen. Most generative AI capabilities still require customers to use them in a vendor's environment, he noted. Extending them to work applications will make them more effective.

"I'm excited to see the new Embedding for Pulse option being introduced in the 2024.2 update," Henschen said. "The ability to embed Pulse insights within the day-to-day applications promises to open up new possibilities for making insights actionable for business users."  

Also noteworthy is Multi-Fact Relationships, according to Leone.

By enabling users to relate datasets that have shared dimensions, they can use larger volumes of data to inform applications, including traditional AI and generative AI models that require large amounts of high quality data to minimize AI hallucinations and be accurate.

"Multi-Fact Relationships are a fascinating area where Tableau is really just getting started," Leone said. "As businesses seek to build trust in GenAI and reduce hallucinations, providing ways to improve accuracy, insights, and context goes a long way."

A price to pay

While Tableau has now launched its first generative AI assistant and two more will be made generally available in a matter of weeks, the vendor has not yet revealed prices for using the Copilots and other related features.

The generative AI assistants are only available through a bundle called Tableau+, which the vendor calls a premium Tableau Cloud offering. It was introduced in a blog post in June.

Beyond the generative AI assistants, Tableau+ includes Data Connect, advanced management capabilities, simplified data governance and data discovery features, and integration with Salesforce Data Cloud , among other features.

Generative AI requires significant compute power and is costly, Henschen noted. As a result, it's not surprising that Tableau customers will have to pay extra to use the generative AI assistants.

Microsoft, like Tableau, hasn't yet made public the cost of using its Power BI Copilot while AWS charges extra for Amazon Q capabilities in QuickSight , Henschen continued. Some others, meanwhile, are offering generative AI capabilities for free -- for now -- to try to attract new users.

"Customers will want to understand the cost implications of adding these new capabilities," Henschen said. "The reality is that GenAI is compute intensive. I have no doubt that customers will have to pay for these capabilities sooner or later in one way or another. At this point cost picture and, certainly, the cost versus value picture with GenAI features just isn't clear. ... Time will tell."

Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.

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