Flyer Enterprises, a student-run business program at the University of Dayton, was struggling with its traditional methods of analyzing data. The organization was manually entering numbers from flat files generated in internal operating systems, which was not only time-consuming but also prone to errors. The high turnover rates typical for a student-run business meant that the organization couldn’t devote adequate time toward training employees on its complicated analytical processes. Even after consolidating its data into a Data Warehousing environment, Flyer Enterprises was still lacking a method of viewing and analyzing its business information.

Jacksonville State University's Office of Institutional Research and Assessment, a team of four, was tasked with collecting and analyzing organizational data to optimize operations across nearly 100 academic and non-academic departments. The team struggled with making credit hour productivity visible to departments and administrators, which involved identifying trends in student preference in the academic schedule. The goal was to create optimal course calendars from term to term based on a solid understanding of the data. However, the use of spreadsheets for this task proved to be inefficient and time-consuming, preventing the team from producing meaningful results in time for optimal decision-making. The team was unable to easily identify trends, such as the popularity of 8am classes over the same course at 3pm, leading to missed opportunities to adjust the schedule to meet the needs of the diverse student body.

The Ingenuity Media Group, a part of The Martin Agency, heavily relies on measurement capabilities to gauge how external audiences perceive their clients’ online advertising and interactive media, the overall success of campaigns and, most importantly, quickly identify and correct any issues with a campaign. Speed, accuracy and flexibility are vital to the creation of useful reports, and how they are presented helps showcase the Ingenuity Media Group’s work by quickly identifying which brands and campaigns are effective or, alternately, what changes need to be made to increase their effectiveness. However, producing these analyses with Ingenuity Media Group’s traditional processes was an extremely time-consuming task. Analysts were forced to spend hours processing data and converting it into visual form, creating an Excel document, converting to PDF before finally sending the information to the client. Preparing weekly, bi-weekly or monthly reports took hours.

First Choice Power, a Texas-based electric company, was struggling with the challenge of pulling together a centralized data warehouse from multiple large transaction-based data systems. The company was looking for a reporting software package that could sit on top of this data warehouse and provide meaningful insights to the executive team. The transactional-based data systems where they housed all their data were not designed for reporting, making it difficult to produce meaningful reports for the executive team. The company was also looking for a solution that could help them understand customer behavior patterns and make strategic business decisions.

The Danish Olympic Committee was struggling with data accessibility and analysis. They had a lot of reporting and 'gatekeepers', but the data was not easily accessible to athletic consultants. These consultants work with about 61 different federations for various sports, and the federations needed to work with the committee. The committee had 26 Tableau Desktop users, but they were facing problems with spreading their data. They were also working on cube data, which presented its own set of challenges. The committee started with a small group of around eight people working on and testing Tableau, and now it's being used by many federations. They have 70 staff members, so 26 is actually a lot. The main goal from the beginning has been to sustain the work between the consultants and the federations.

Norbridge, a management consulting firm, competes against larger firms for the same client opportunities. They rely on fact-based analysis and deep domain expertise to set them apart. However, while their analysts are comfortable with raw pivot table output from Microsoft Excel, most of their clients are not. Their projects often require weeks or even months of data collection, cleaning, and analysis, interspersed with client workshops for joint evaluation and interpretation of the findings. They were looking for a way to respond to analytical opportunities that could rapidly and convincingly connect clients to the key insights in their data. The primary criteria in evaluating analysis applications were: it had to run efficiently on stand-alone laptops, it had to be intuitive so that new analysts could learn the basics quickly, and it had to effectively output visual images to Microsoft PowerPoint.

Predicta, an online marketing intelligence services company, was facing a challenge with its product development and customer service. The company's developers were spending a significant amount of time creating filters, drill-downs, graphs, and other capabilities into the company’s product interfaces. This was diverting a large share of developmental resources from creating new features. Additionally, Predicta’s current analytical capabilities required technical knowledge, causing customer questions to be directed to IT instead of the analysts, further impacting IT’s productivity. Predicta recognized that it needed to improve reporting capabilities to enhance the customer experience and make the best use of resources.

Creighton University was facing challenges in analyzing their data to identify potential markets for prospective students and track the performance of their counselors. They were also required to provide analytic data to their president and board related to applications, admittance, and enrolling students. They needed to monitor their competitors and understand various factors related to the admission process. Before implementing Tableau, they were using SQL servers and Microsoft Access to extract and report data. However, this process was cumbersome and did not provide the level of detail and flexibility they needed. They lacked a data warehouse, leading to consistency issues and difficulties in tracking changes from one year to the next.

Cars.com, a leading online car shopping platform, wanted to provide its salespeople with easy-to-access information about how their customers’ ads performed on the site. They also wanted to share similar information directly with its advertisers and internal business users. The company’s data warehouse and business intelligence team gathers and interprets site visitor data from the 11 million monthly visitors. Based on traffic volume, there is a significant amount of data stored in its 12-terabyte Teradata warehouse appliance. The company faced challenges in efficiently analyzing and visualizing this data for multiple audiences. Additionally, Cars.com best practices require that the more than 600-person sales team be able to do all of its work from within the cloud-based Salesforce solution.

ManpowerGroup, one of the largest HR companies in the world, realized that it held a great deal of valuable data. However, it was not leveraging this information to drive competitive advantages for itself or its clients. The company needed to develop data-driven business review documents that could be used to drive strategic conversations. However, users asking the IT team to script and run reports from the Cognos Enterprise business intelligence solution faced a long queue. If the resulting document answered the wrong question, or users developed new questions after seeing the results, the process had to be repeated. This approach produced inconsistent answers to a question depending upon who you asked. These homegrown solutions also carried the risk of errors. Manpower has offices in more than 80 locations across the globe. While North American data is housed in a data warehouse, data from other locations could be provided in a number of different formats. Blending information from these disparate data sources into a cohesive customer presentation was a long, manual process.

SIGMA Marketing Insights, a marketing services company, was facing challenges with its analytics turnaround time. The data management and discovery process was time-consuming, often taking weeks for large and complicated data sets. Additionally, the company struggled to communicate its findings in an easy-to-digest format, often resorting to sending flat files or creating lengthy PowerPoint presentations. Customers also expressed a desire for self-service business analytics, which SIGMA was unable to accommodate effectively. The company realized that providing customers with self-service analytics could not only speed up the process but also foster ongoing conversations with its customers.

Zulily, a daily deals site for moms, babies, and children, was facing challenges in managing its rapidly growing data. The company's Senior Director of Technical Operations, Don Allen, was tasked with supporting and managing Zulily’s growth through technology. The company's unique business model, which involves launching up to 5,000 SKUs every day, each live for only three days, posed significant analytics and reporting challenges. The company needed a solution that was flexible, fast, and scalable. The Manager of Business Intelligence (BI) for Zulily, Aaron Duke, and Zulily BI Developer Alex Rainey were often pulled away from higher-value work to spend days designing and running reports for internal users. They needed a solution that would empower users while ensuring the integrity of Zulily’s databases.

Millard Public Schools, a school district in Omaha, Nebraska, was facing the challenge of effectively utilizing student data to improve educational outcomes. The district had a wealth of data on its students, but it was difficult to analyze and interpret this data in a meaningful way. The data was scattered across multiple spreadsheets and it was time-consuming to merge and analyze it. The district needed a solution that could help them visualize and understand the data quickly and easily, enabling them to make timely decisions that could positively impact their students.

Granja Regina, a Brazilian agriculture and food products company, was struggling with gaining performance insight across its divisions. The company's ERP system, TopManager, connected with Microsoft SQL Server and PostgreSQL databases, and additional information was maintained in Microsoft Excel spreadsheets. However, the reporting capabilities of TopManager were not satisfactory. The manual process of generating reports was slow, tedious, and placed a heavy burden on IT resources. This process also diverted skilled analysts from more strategic work. The company's CIO, Bertolini, had security concerns about sensitive company data being distributed in a spreadsheet. In addition to databases and Excel spreadsheets, Granja Regina had data stored in third-party solutions, such as its human resources and agribusiness software.

IsCool Entertainment, a leading European social gaming provider, was facing challenges in understanding gamers’ preferences, behaviour, and needs. The company was using open source integration tools and commercial BI visualization software for decision-making. However, with the increase in user-generated actions and revenues, the company needed a more agile big data analytics solution. The company also needed a flexible approach to data visualization and presentation. The existing QlikView tool was not efficient as it required scripting for every report, causing delays in findings.

The immunovirology division of the CEA in Fontenayaux-Roses was facing a challenge in gaining a rapid visual understanding of its investigations. The organization’s mission is to explore and develop vaccine treatment strategies for chronic and emerging viral infections. However, the research groups relied on different and fragmented silos of research data. This led to the team devoting unnecessary time to compiling the data reporting—as opposed to actually analysing the data and experimenting with models to combat viruses. Antonio Cosma, a research scientist responsible of the FlowCyTech core of the CEA’s division of immuno-virology explains, “Our research was held up in the slow lane because we had to collect the data, and then prepare and adapt the spread sheets. After that, we then devoted hours to creating visually appealing graphs and bar charts to show the results.”

Schuberg Philis, a company that provides mission-critical computer IT systems and guarantees uptime to enterprise customers in the Netherlands, was looking for a way to support their service management processes with real-time information. They needed a tool that could handle different data sets, from KPIs and incident management to system performance. The company was using a variety of different products for reporting, from internal systems to Excel and traditional reporting systems. However, they found these methods to be time-consuming and repetitive, and they were looking for a way to take their reporting to the next level.

PepsiCo’s Collaborative Planning, Forecasting, and Replenishment (CPFR) team faced the challenge of reconciling disparate data from various sources, including warehouse inventory, store inventory, and point-of-sale inventory. Each customer had their own data standards, which didn’t correspond with each other or PepsiCo’s system. This made data wrangling a challenge and reports could take months to generate. The team primarily relied on Excel for analysis, creating large quantities of messy data. And the team had no efficient way to spot errors, leading to potentially costly outcomes. For example, a missing product from a report could result in inaccurate forecasts and lost revenue. The CPFR team needed a way to wrangle large quantities of disparate data. At the same time, the team needed a visual analysis tool that could help them make the most of PepsiCo data.

Rosenblatt Securities, a New York-based firm that provides institutional investors with advice and trade execution services, was looking for a way to improve its pre-trade and post-trade analysis. The firm wanted to be able to perform derived analytics on hundreds of different fields and visualize the data quickly and simply. They wanted to provide their traders and clients with insights on when to buy or sell a security. The firm was also looking for a tool that could handle large amounts of structured and unstructured data, including time series data.

Mercer, a global consulting, outsourcing, and investments company, was looking for tools to improve the graphical capabilities of their dashboards. The existing in-house products were not keeping up with the times. The company was transitioning from a P&L-centric business to focusing more on clients and client profitability. This shift required dealing with a much larger dataset and providing leadership with a high-level view of the data, directing them where to focus. This need necessitated a more interactive, graphical tool rather than a simple PDF listing of information on a report.

Alcatel-Lucent Shanghai Bell, a telecommunication and information enterprise in China, was facing challenges in analyzing network data. The company's professional services team was using Excel spreadsheets to sort data and manually chart and present the information. This process was time-consuming and the end deliverables did not provide many insights. The team couldn't see the full perspective of a communication network when they analyzed network data. Equipment assessments could take up to three months. The team needed a tool that could help them accurately identify the need for skills upgrades on the team and ensure that they are deploying staff with the right skill-sets to each on-site job.

amaysim, a leading Australian online-led Mobile Services Provider (MSP), faced challenges in analyzing and obtaining insights from the vast amount of data it collected. The company had over 10 billion call data records, with this number growing by more than 20 million call data records daily. The data was complex and came from multiple sources including Livechat, Zendesk, call data records, Google Analytics and more. The previous BI methodology required coding which could not easily scale to business users. Insights could only be sought by one or two individuals within the organization, limiting business gains due to resource constraints and the time-intensive process. The low productivity levels of the current solution prevented the team from fully realizing the value of their data.

Spil Games, a company that publishes and distributes mobile games to over 100 million monthly users, was struggling to extract insights from its massive volumes of data. The company wanted to build dashboards based on multiple data sources quickly and easily to drive data-driven decisions on a daily basis. The data they wanted to learn more about included everything from game loading times and search engine advertising optimization, to user demographics. At the time, Spil Games already had a BI tool in place for dashboards, but it was inefficient and time-consuming, taking 44 clicks simply to update one dashboard.

Pacific Northwest National Laboratory (PNNL) was asked by the Department of Homeland Security to participate in a pilot program. The goal was to pilot an analytical application for the Immigration and Customs Enforcement (ICE) department that would leverage analysis techniques used for unstructured text analysis with those used in structured data analysis. PNNL’s primary objectives over the project were threefold: Provide a new capability for structured and unstructured data analysis, Evaluate/characterize ICE data, and Be deployable within eighteen months. PNNL wanted to address the question of mixed data: information typically found in structured forms with annotations, case files, reports, etc. They wanted to ensure data sharing and interoperability and to facilitate data exchange between specialized tools.

Blastrac Manufacturing was facing a challenge with its reporting method. The company did not have a consistent reporting method in place and, consequently, preparation of reports for the company’s various needs was tedious. Blastrac’s analysts each spent nearly one whole day per week extracting data from the multiple Enterprise Resource Planning (ERP) systems, loading it into several Excel spreadsheets, creating filtering capabilities and establishing pre-defined pivot tables. These massive spreadsheets were often inaccurate and consistently hard to understand, and they were virtually useless for the sales team, which couldn’t work with the complex format. In addition, each consumer of the reports had different needs—while some thought the reports were lacking detail, others thought there was too much.

AOL, a leading-edge web services company, was facing challenges with its data not being integrated with the corporate data repository. The company was dealing with a massive amount of data, with tens of millions of searches daily, each generating between 20 and 40 rows of data. This resulted in 400 to 800 million records every single day. The company was operating on a push model where the Business Intelligence (BI) team would have to manually pull and send out reports to those who needed them. This process was time-consuming and inefficient.

Kwantum Institute, a market research firm serving major global automotive manufacturers, was struggling with its survey analysis and reporting process. The existing process, which involved a combination of Excel pivot tables, charts, and VBA macros, was not only time-consuming but also produced static and lengthy reports. As a result, clients were losing interest and missing potentially important findings and results. The company needed a survey analysis and reporting tool that would not only improve its internal operational processes but also increase the quality of its customer offerings by structuring results visually. In addition, Kwantum required a solution that was able to access its massive ChoiceMonitor database (up to 200 million rows of data) without impacting the integrity of its other systems. Finally, given the ever-changing market conditions in the automotive industry, Kwantum’s clients required reports and dashboards that offered real-time information as well as the ability to further interact with the data to a degree that static Excel tables don’t allow.

SuperData, a market intelligence research provider specializing in the video game sector, was struggling with an Excel-based product that couldn't efficiently handle the data from over 40 million video gamers each month. The reports generated were multi-spreadsheet and required customers to dig for insights. The company needed a solution that could handle large data volumes and present insights in an easily digestible format. They also needed to ensure data security, as the data was gathered directly from game companies and shared anonymously. The company initially tried an open-source business intelligence solution, but it didn't meet their needs.

The increase of digitalization brings risks and opportunities alike, and the media industry is no exception to that. The NZZ data analysis team, led by Markus Barmettler, used a Microsoft Excel-based reporting solution in combination with ad-hoc analysis when necessary. In October 2014, the team chose to migrate to a Tableau solution in an effort to be able to quickly react to market opportunities. Before the NZZ team could reap the rewards of new and profitable insights, it needed to redefine and automate its in-house data collection process. “As we were coming from an Excelbased reporting system, we needed to first set up a data warehouse,” says Barmettler.