Change Grow Live, the largest provider of drug and alcohol support services in the UK, was facing challenges in managing and analyzing its vast data. The organization, which supports over 100,000 individuals annually, was relying on Microsoft Excel for creating monthly and quarterly reports to monitor its progress. However, the manual and repetitive nature of processing and analyzing data in spreadsheets was proving to be tedious and error-prone. Furthermore, the organization's rapid growth over the past decade had stretched Excel's capabilities to its limits. The additional ad hoc analysis requests were also straining the resources of the central team of data analysts and scientists, preventing them from focusing on the organization's future or deriving deeper insights from the data.

Deutsche Börse Group, a global financial services company, saw an opportunity to transform the large volumes of stock data, previously considered as 'exhaust' of their trading business, into a significant revenue contributor. The company decided to invest in data science to sell not only raw data but also more advanced content. Despite having invested in on-premise architecture in the past, Deutsche Börse Group realized the need to build its new data science center in the cloud to leverage the cloud's flexibility and scalability. However, the company faced a challenge. Business users required specific transformations to be made to the data before it could be migrated to the cloud, but they did not want to overload the already busy IT team with requests. Furthermore, Deutsche Börse Group wanted to prevent their highly-trained data scientists from spending most of their time on data cleansing and preparation tasks, even after the data migration.

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.

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.

Coca-Cola, a global beverage company, has been seeking ways to balance its rich history with a relentless pursuit of innovation. The company has been using Alteryx, a data analytics platform, for various projects including geospatial intelligence, time series forecasting, and predictive modeling. One of the significant challenges was to gain insights from the Coca-Cola Freestyle machine, a touch screen fountain that allows users to create their own mixture of flavors. The company wanted to predict components that would require maintenance and understand the best product mix in different regions of the country.

Hyatt Corporation, a global hospitality company, was facing challenges in increasing its corporate sales, which contributes to over 60% of revenue for its full-service hotels. The business environment was dynamic with high competition, long sales cycles, and complex customer relationships. The company was looking for ways to target and expand its share of the business. The challenges included limited customer insights, no global ranking, manual data extracts, and long durations for data refresh and ad-hoc analysis.

The Colorado Hospital Association (CHA) was facing a significant challenge in the form of the opioid epidemic. Opioids, traditionally used to treat pain in the emergency department, were contributing to prescription abuse and serving as a gateway to illegal drugs. According to data from the Colorado Department of Health Care Policy and Finance, one Colorado resident died every 36 hours from overdose in 2015. From a data perspective, the situation was messy. The CHA needed to join emergency department data with claims base data to see what was happening across hospitals. Each question seemed to lead to another, creating a complex and challenging situation.

Veritone, Inc., an artificial intelligence technology company, was facing challenges in managing and analyzing large datasets for their advertising campaigns. The company provides tools and SaaS products to help TV and radio stations prove advertising attribution to their clients. However, they were using Excel spreadsheets for all their data blending, which was not only time-consuming but also limited in terms of flexibility to connect to different data sources. This process was subject to delays and slowed their ability to deliver enhanced analysis and reports to their clients. Additionally, Veritone wanted to develop a foundational Uplift Study to show the effectiveness of broadcast advertising compared to digital. This required analyzing and correlating data points from 250 radio and TV campaigns, a task that proved to be daunting due to the sheer volume of data.

The Washington State Department of Health was faced with the challenge of managing and analyzing a massive influx of data from various sources such as hospitals, schools, and clinics due to the COVID-19 pandemic. Their traditional processes were overwhelmed and the use of virtual machines did not provide a solution. The department's data systems, which had been underfunded for the past 50 years, were built for single purposes, overly customized, and lacked interoperability. This resulted in a lengthy and complex process to clean, transform, standardize, and restructure data before it could be queried. The lack of tools to simplify or centralize this process led to a long time to insight and a great amount of duplicative work done by agency analysts.

GC, the largest petrochemical company in Thailand and third largest in Asia, was facing challenges in its operations due to a lack of data-driven decision making. The company, which is involved in the manufacturing and distribution of a wide range of petrochemical products, had 70% of its business focused on engineers and chemical plants. However, the engineers were relying on piecing together data from different suppliers, which carried complex variables. This was not only inefficient but also hindered the company's ability to innovate and optimize its operations. Furthermore, the company was following traditional practices such as changing chemical catalysts at known intervals, without any data-driven reasoning. This was leading to unnecessary costs and resource wastage.

Health Care Program Advisors (HCPA), a boutique healthcare consulting firm based in Atlanta, Georgia, specializes in revenue cycle management, information systems, business intelligence, and clinical and operations performance excellence. They partner with prestigious hospitals and health systems across the nation. One of the significant challenges they face is assisting health systems in mitigating adverse financial consequences during electronic health record (EHR) implementations. EHRs are designed to maintain patient health records, streamline administrative tasks like scheduling and billing, and maintain accuracy and patient safety. However, implementing an EHR is a vast undertaking and can often leave health systems with significant financial issues if not managed properly. HCPA uses Alteryx for revenue monitoring before and after implementation, assisting health systems in understanding the effects of changes on revenue and offering insights necessary for accurate revenue capture. However, factors like revenue volatility, annual pricing adjustments, natural disasters, and the volume of historical data make establishing a performance baseline extremely difficult without advanced tools.

Joe Simpson, a sales professional at Alteryx, spends most of his day cold calling and messaging data professionals at large companies that are not yet using Alteryx. His challenge was to identify key targets for sales prospecting, which involved mining titles, roles, responsibilities, and locations. He also needed to identify prospects within driving distance for field events, those with titles most relevant to working with data, and to combine Salesforce Leads and Contacts for proper outreach. The process was complicated by the fact that many companies had different billing and physical addresses, and sometimes the primary address was a PO Box, which was not suitable for spatial analysis. Furthermore, the list of potential contacts could run into thousands, with titles ranging from CEO to Tax Intern, making it difficult to focus on those most likely to convert.

The Local Government Association of Queensland (LGAQ) was facing a significant challenge in managing and understanding their energy utilization, particularly in remote indigenous islands off the coast of New Guinea. The team had no clear understanding of their asset management or energy utilization for this area. They were using manual processes in Excel, Java, and MySQL for anomaly detection, which was time-consuming and inefficient. The team of four was doing the work of what twenty analysts should be doing, and they needed to increase their productivity drastically. The lack of a proper blueprint for energy saving and the use of multiple disparate points of data ingestion further complicated the situation.

PDPAOLA, an online jewelry company, was faced with the challenge of differentiating itself in a crowded market. The company's Shopify eCommerce platform provided high-level profit margin analytics, but PDPAOLA wanted to delve deeper into the data to uncover more granular insights such as net margins or contribution margins. As the company began to build out data pipelines using SQL on Google Cloud, it quickly realized that it would reach a scalability limit. Hiring additional SQL developers and training them on the company’s unique processes would require significant time and resources. PDPAOLA needed a platform that would increase automation, allowing it to scale without added expenditure.

PlusUp, a leading company in advertising consulting services on social networks, was facing a significant challenge in analyzing paid social media. The company was routing all social media data through Excel spreadsheets where it was standardized and stitched together for analysis. This manual, time-consuming process left PlusUp constantly playing catch up and spending more time on prep work than on the actual analysis. Furthermore, manual data preparation and cleaning is prone to error, which meant that PlusUp had to triple check its work. As PlusUp grew its business, it couldn’t see any obvious ways to scale this process except by hiring more analysts.

Quantum Spatial, Inc. (QSI) was facing a significant challenge with their Enterprise Resource Planning (ERP) system. While the ERP was effective for many tasks, it was not capable of tracking historical data, specifically project Estimates-at-Complete (EAC). The inability to track EAC on a week-to-week basis made it difficult for the Finance team to understand variances in EAC and identify potential issues if a project EAC varied greatly from its baseline. Furthermore, the company's ERP and Customer Relationship Management (CRM) systems were not integrated, creating gaps in the project lifecycle data collection. This situation was further complicated by the fact that QSI had no formal Business Intelligence (BI) system in place, and the Enterprise Systems team was unfamiliar with Alteryx, a tool that could potentially solve their historical data problems.

Room & Board, a privately held, American retailer of modern home furnishings, faced challenges with data in marketing. The primary issue was the volume of data coming from different systems, which sometimes required real-time data streaming to feed decision management systems. Additionally, working across different departments and teams to get the necessary data and answers was a significant challenge. The shift towards digital analytics in the marketing department, due to the lack of in-person activities, made personalized email campaigns more important and relevant than ever. The company needed a solution that could blend data from different sources for a more personalized and effective approach, and speed up analytics to optimize marketing across channels and products.

SaskTel, a telecommunications company with over 100 years of experience, faced significant challenges in inventory forecasting. The company had been manually forecasting inventory, a process that was both time-consuming and inefficient. Byron Waugh, the Demand Planning and Forecasting Manager, had to manually extract data from SAP for each material number, analyze the history, and input the information into a spreadsheet. This process was not only tedious but also unsustainable given the 3,300+ active material numbers that SaskTel used. The company also faced supply chain issues due to the pandemic, with material lead times extending to 2.5-3 years. Previously, SaskTel would only order materials a few months in advance, leading to frequent rush orders and additional costs.

SearchKings, a digital advertising agency, faced three main challenges that they needed to address. The first challenge was the consolidation of data from different platforms, specifically Microsoft and Google, into a single view. This was crucial for them to have a comprehensive understanding of their data. The second challenge was leveraging data insights to better advise their customers regarding their investments. This was important for them to provide valuable advice to their customers and help them make informed decisions. The third challenge was enhancing their value proposition when working with larger organizations that required a flexible and scalable data feed as part of their solution. This was necessary for them to cater to the needs of larger organizations and provide them with a solution that could scale according to their needs.

The Covid-19 pandemic has led to a significant increase in remote working worldwide. As restrictions begin to lift, employers and employees face new challenges related to commuting safely and minimizing risk. This case study focuses on the imaginary example of Acme Corp, a company with around 1,000 employees across Greater London, operating from five different offices. The company needed to answer critical questions such as: What commute options are available for our employees if they wish to avoid using public transport? How can we efficiently allocate our employees between our multiple offices? How can we help employees avoid particularly busy stations or train lines? And how should we best consolidate the number of sites that we operate to minimize the impact on employees? These questions are not only relevant to Acme Corp but also to other organizations across various industries.

Philip Mannering, an analyst at ClarusOne Sourcing Service, was faced with the challenge of optimizing the production of chocolate bars to maximize profit. The problem involved determining the number of chocolate bars to produce, given the cost of each bar and the constraints of the available resources. The challenge was further complicated by the introduction of a second type of chocolate bar, which added another variable to the equation. The goal was to find the optimal solution that would yield the highest profit, taking into account the constraints of the available resources. This required a shift from descriptive and predictive analytics to prescriptive analytics, which not only predicts what will happen but also prescribes the best course of action.

Corsicana Mattress Company, one of the largest mattress manufacturers in the United States, was facing challenges in optimizing its shipping routes. The company, which had grown from a family-owned business, was grappling with inefficiencies in its logistics operations. The freight costs were high due to the nature of their product, and the logistics team was limited by their analytical capabilities. The company had instances of orders being fulfilled from non-default facilities, leading to increased costs and inefficiencies. In some cases, shipments were routed through facilities hundreds of miles away, leading to unnecessary costs and increased carbon emissions. The company lacked a system to audit default facility assignments, which was a manual and time-consuming process.

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.

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.

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.

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.

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.

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.