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Symphony Care Network, a healthcare provider with 34 care facilities, was facing a significant challenge in tracking the exposure and treatment of COVID-19 among its patients and employees. The existing systems did not provide a consistent way to track this critical information, leading to inefficiencies and potential gaps in care and safety measures.

The Siemens Gas and Power Division launched a productivity offensive for power plant service in 2016. The objective was to analyze areas where unnecessary costs, such as excessively high material expenses, could be prevented. With several thousand various measures, more than 5,000 different data sets, and over 8,000 employees in the business unit, the investigations were complex and time-consuming. The analyses were nearly obsolete by the time the results were available. The first step was to switch to Tableau to improve the visualization. However, the actual problem with the time-consuming and complicated provision of data was still not solved.

SKOPOS, a technology company based in Europe, was seeking a solution to prepare, enrich, and model data for visualization in Tableau. The goal was to help its team visualize survey data in a more efficient and effective manner. The company was dealing with a large volume of data, over 2 million survey responses, and needed a way to analyze this data in near-real-time. The challenge was to find a solution that could handle the complexity and volume of the data, and deliver the desired results.

Takenaka Corporation, a major general contractor founded in 1610, has a wealth of architectural data spanning over 300 years. However, turning this vast amount of data into usable information for modern architectural design was a significant challenge. The data was stored in various formats and was difficult to manage and use effectively. The company wanted to use this data to expand the possibilities of architectural design and to integrate it with the thoughts and feelings of people today. The company was using Tableau, a Business Intelligence tool, to visualize this data, but it was not sufficient for their needs.

7-Eleven was facing a challenge in managing vendor data and gaining insights into their promotions. The process of requesting a vendor report or initiative was time-consuming, taking up to 100 hours. The company needed a solution that could be used by a business user on a daily basis without much technical support. They needed to get results quickly to share with external business associates and provide multiple capabilities at a marginal cost.

Ares Management, a leading global alternative investment manager, was facing challenges in automating their processes and breaking down silos between their teams. They were also struggling with providing better access to insights. One of their teams was spending two months to complete a process once a year, which was highly inefficient and time-consuming.

MUFG Bank's Global Financial Crimes Division (GFCD) was facing challenges in handling massive data processing and diversified IT systems. The bank, being a megabank, had a large number of transaction data. In Anti-Money Laundering (AML) operations, the staff had to judge each case that the AML system detected with their own eyes, one by one, if it was truly suspicious. Also, detecting new high-risk transactions was a challenge since it required a significant workload. Another challenge was the insufficient human resources of data scientists who can perform exploratory analysis. They needed exploratory data analysis skills to spot global financial crimes and take action quickly. However, there were not enough data scientists currently in the Japanese labor market.

ADL Digital Lab’s platform, Augusta, needed a flexible and efficient analytics automation technology that could integrate with any data source that could help drive the platform’s efficiency and security. The goal was to find a solution that could enhance the platform's capabilities and deliver better results for both ADL and their clients.

Experian Marketing Services was facing challenges in providing its clients with high-quality, highly customized reports in short time frames. The company was dealing with large, inconsistent client data files that spanned terabytes of data and contained a variety of data formats. The legacy system used for processing the data often required intervention from engineering and delivery resources to meet customer requirements. This process was lengthy and involved custom coding, multiple and complex analytical tools, and expensive data transformation resources. Experian wanted to lower processing and analysis costs, produce final products for clients more quickly, and improve overall customer satisfaction.

Great Clips, the world’s largest and fastest-growing salon brand, was facing challenges in its growth strategy. The company's success is based on rapidly opening new stores in the right locations and markets. However, the process of identifying these locations based on potential customer base, demographic trends, and sales impact on existing franchises was taking too long and was expensive. The process required analyst resources to manually access, integrate, and analyze multiple sources of data to produce a report for a single location. This delay was risking the loss of prime sites to competitors and alternative real estate interests. Additionally, costly external contractors were being used to deal with the backlog.

Southern States Cooperative, a large farmer-owned cooperative in the United States, wanted to optimize its marketing efforts, particularly its high-value direct marketing activities. However, the company faced significant challenges. It lacked a way to consolidate customer and marketing data from multiple sources across the company for analysis. It also lacked sophisticated tools to drive marketing analytics, relying instead on generic productivity tools like Microsoft Excel. This lack of access to relevant data meant that direct mailings were not targeted effectively, leading to unnecessary expenditure.

TargetSmart Communications, a political data firm, was tasked with building voter propensity models and scoring national files for political fundraising campaigns for the Obama for America Campaign. The company needed to process millions of records of data from dozens of data sources, including national, state, county, and city records, to identify voters, track their tendencies, and accurately count and call upon unregistered voters prior to an election. The company was using open source software solutions to merge multiple databases, two of which consisted of 200 million voters each. However, the lack of a universal format for government data for various voter categories and the need for continuous data aggregation, updating, cleansing, and enhancement with demographic profiling data presented significant challenges. The company's choice of data processing tools was leading to a troubling lack of efficiency, accuracy, and reliability.

AAA National wanted to better support its member clubs in selling additional products to existing members, finding new members in each of their territories, and ensuring that brick-and-mortar office locations were optimally located based on club member demographics and drive-times. The process of gathering and analyzing the necessary data was time-consuming and complex, often taking up to three days to generate a complete and accurate data set for analysis. The data was sourced from different systems, making it difficult to match and blend with third-party demographic and census data. The use of multiple tools to work with the data further complicated the process and increased the potential for errors.

Rent-A-Center, a leading retail company in North America, was facing challenges in creating maps for their 3,000 stores. The process was manual, involving a lot of clicking, joining, queries, and sub-queries. This was time-consuming and inefficient, taking up to 12-1/2 weeks to complete. The company needed a solution that could streamline this process and make it more efficient.

Schneider Electric, a global specialist in energy management, was facing a challenge in identifying its high-potential customers and deploying sales resources efficiently. The company's manual approach to determining how and where to deploy its sales resources was time-consuming and inefficient. Sales operations and more than 20 sales managers would collaborate with the analysis team to collect data and evaluate numerous factors related to each customer account, including account size, vertical market, and growth and purchase history. This process was not only slow but also resulted in the sales team waiting for the information it needed to start the new year, often not receiving it until February.

Novus, a leading advertising agency, has been experiencing significant growth, expanding both its client base and the range of services it offers. As a result, the complexity of data usage and reporting has increased, both internally and externally. The company was seeking a fast and efficient way to process the hundreds of variables involved in planning and measuring effective media campaigns. The continuous evaluation of success and application of optimization techniques across a universe of potentially more than 3,000 publishers for each client became cumbersome. It was increasingly important to be able to track progress and outcomes in an automated way.

The rise of e-commerce has led to the closure of many brick-and-mortar stores, with Americans spending over $400 billion a year online. However, the online shopping experience is largely transactional, lacking the human interaction and emotion that comes with physical shopping. Hush, a top social commerce app in the United States, aims to bring the social aspect of shopping to the digital world. The company believes that shopping is a social experience and that many purchases are experiential and emotional. This is particularly true for beauty products, Hush's focus, where people want to interact and talk with like-minded individuals about what they're buying.

Amaysim, Australia's largest MVNO with over 600k customers, was dealing with a massive amount of data. They had over 10 billion call data records to be analyzed, with 20-30 million call data records added daily. The velocity and complexity of data were high, with multiple data sources including Livechat, Zendesk, call data records, Google analytics/Website data, Point of sale data, and Exact target. The company had a small analytics team of three people covering a wide span of functions. They needed a solution that would enable line of business users to quickly build on a baseline of analytics, solve their own specific business problems quickly, and not have to wait on Business intelligence teams.

VF Corporation, a highly diversified, branded lifestyle apparel, footwear, and related products company, wanted to improve corporate profitability, support retail expansion, and maximize the performance of the company’s more than 100,000 SKUs at more than 10,000 retail locations. In 2009, the company's RFSM organization assumed responsibility for the company’s customer relationship management (CRM) efforts with its retailers. While the company already had an internal CRM database, it also needed to correlate and analyze this data along with huge volumes of data from a number of other disparate internal and external sources. The company’s existing solution was not scalable or reliable enough for a company the size of VF Corporation. The existing solution did not allow VF Corporation to include spatial information, which was becoming increasingly important to the company and its retail expansion efforts.

Chipotle’s real estate managers were spending a significant amount of time interacting with static datasets to analyze potential revenue and targeted markets for prospective restaurant sites. The company needed a more efficient way to deploy timely sales forecasting data and its complex site-location selection model to its brokers. The existing system was time-consuming and the relevant up-to-date data was not always readily accessible to all employees. The key to securing prime real estate for restaurants is the speed at which new locations are targeted and developed. Chipotle was seeking a system that could more efficiently deploy sales forecasting tools and its complex site location selection model in a browser instead of requiring employees in the field to search, select, and input data into multiple laptop programs.

DatabaseUSA, a leading provider of business and personal mailing lists, email lists, sales leads, and related database products, was facing a challenge in maintaining the quality and accuracy of its data. The company was dealing with raw data from hundreds of different sources and multiple directories, including data from utility companies and call data from its call center. To manage this, the company had to hire individuals with high-level data expertise, which was costly. Moreover, every time new data was introduced, the company had to develop a significant amount of new, low-level code to get the data into its database successfully and accurately. This process was not only expensive but also difficult to scale.

Rosenblatt Securities, a premier independent institutional brokerage and investment banking boutique firm, was facing a challenge in performing sophisticated data analyses for its clients. The firm was dealing with millions of rows of execution records and market tick data, which often took days or even weeks to process. The firm was using Tableau visualization software since 2006, but many processes included blending different heterogeneous datasets and running multiple analytic models, which could take days or even weeks of preprocessing time and still required manual error-sampling. The firm was faced with a decision: to spend thousands of dollars on a specialized ETL tool to interact with its structured and unstructured data sources, or to build such a tool internally.

The University of Dayton needed to quickly process thousands of pieces of student data from different systems across the university, enabling it to recruit, enroll, and retain the students most likely to succeed at the institution. Rising costs and increasing government involvement in higher education are bringing a greater focus on student outcomes. At the same time, data is available from an increasing variety of sources and departments—internal and external, structured and unstructured. University of Dayton is leveraging publicly available census data, high school academic records, college entrance exams, student activity and transcript data, and postgraduation information.

Brookson faced several challenges in their operations. They had no visibility across departments, which led to a high amount of time being spent on creating spreadsheets. They also had a high volume of internal work orders and a siloed customer experience. These challenges were hindering their ability to deliver customer experience excellence. They needed to improve accuracy and timeliness, reduce investment in administration, ensure tasks were done right the first time, and become less reactive and more proactive.

7-Eleven, a multinational convenience store chain, was seeking a solution that could be used by business users on a daily basis without much technical support. They needed to get results quickly to share with external business associates, and provide multiple capabilities at a marginal cost. The company wanted to bring all of their vendor data sources together into one output to get deeper insights into their promotions to see what’s working and what’s not.

Ryohin Keikaku Co., Ltd. operates retail stores centered on MUJI, and moreover, product planning, development, manufacturing, wholesaling and merchandising. The company administers 1,029 stores worldwide, in breakdown 479 stores in Japan and 550 stores in overseas respectively (as of August 2020). In 2017, the company introduced Tableau as a BI (Business Intelligence) tool to visualize and analyze various kinds of data, including customer information and sales results, for the purpose of sales metric analysis. As everything continued going to plan in 2018, the desire to utilize the reports that were being used in Japan overseas became more entrenched. However, when Ryohin Keikaku created intermediate data for Tableau on the core system program, only the developers can perform maintenance. So, it is difficult to respond to requests from users to make improvements quickly. They also lacked of people who had the required specialist knowledge to participate in the global roll-out of Tableau report creation during the project to reconstruct the global system.