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.

CUNA MUTUAL Group, a financial services company with $5B in revenue and $44.3B in assets, was facing a significant challenge in managing their sales data. The company was heavily reliant on spreadsheets for reporting, with 70 people depending on these reports on a weekly basis, resulting in the creation of 4,000 spreadsheets annually. The data frequency was also an issue, as salespeople were receiving weekly information, while real-time data was available in Salesforce. The process of increasing data frequency was unsustainable and inefficient. Additionally, the company was using a point-based sales compensation system, which was driving the wrong behavior among employees who were focusing on maximizing points rather than revenue.

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.

The Port of Melbourne (PoM) in Australia is mandated to track all shipping containers that enter and exit every five years. This data is crucial for ensuring the right infrastructure, industrial land, planning controls, and policy settings are in place to support efficient supply chains. However, the PoM was using over 57 independent groups to track the data in more than 60 different formats. This process was not only time-consuming, requiring hundreds of hours of manual work, but also inefficient, with a forecasting rate below 30%. Furthermore, they were unable to successfully perform a match analysis. The state government in Melbourne, Australia, therefore, contracted the machine learning (ML) team at GHD, a global consulting company, to improve these container supply chain processes.

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.

Grant Thornton, a global business advisory, tax & audit firm, identified a need for payroll assurance services in New Zealand, where the government has complex and progressive employee entitlement laws. With a workforce of nearly 3 million, tracking and ensuring proper compensation for various leave scenarios is a significant challenge. Since the implementation of these laws in 2003, payment miscalculations, payroll code inconsistencies, and human errors have been prevalent. Grant Thornton initially built a model for one of New Zealand’s largest cleaning companies, covering 21,000 employees and 6 billion rows of timesheet data. However, the complexity of the entitlement definition in the Holidays Act made the architecture and model performance challenging, limiting them to process only one week at a time for each employee over their employment period. The first iteration of the three bespoke models took 3.5 days to accurately process calculations.

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.

UK-based consultancy Integratis, which provides tailored solutions in strategy development and business planning to private, public, and third sector clients, was facing challenges in optimizing data in the highly scrutinized environments of public and third sector organizations. The company was growing rapidly and needed to strengthen and differentiate their customer offerings while maintaining their core value of data-led decision making. As the business grew, so did the need for integration with multiple systems and applications. The team at Integratis was becoming increasingly frustrated with the limited capabilities of Microsoft Access and Excel, and the lack of consistency this offered when collaborating across teams. They needed a system that would allow them to collaborate effectively with full traceability at every step, especially in the public sector where transparency is crucial due to stringent and often unpredictable auditing demands.

The COVID-19 pandemic introduced new disruptions to the international team of Jones Lang LaSalle (JLL), a global player in the real estate industry. Lockdowns impeded collaboration and knowledge sharing between traditionally co-located team members. The high rate of change in commercial real estate markets put pressure on the organization’s ability to make data-driven decisions quickly. In response to these pressures, JLL created a new team: Work Dynamics BI & Performance. This group discovered that different JLL teams were operating at varying levels of analytics maturity, often directly correlating with the size of the given team. A primary issue was inconsistent tooling. Sharing work and knowledge across teams was difficult without a shared framework or language. Team members had to perform low-value manual tasks to translate work across teams, which further degraded performance.

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 Supply Chain Analytics team at an American healthcare provider was facing a significant challenge. They were spending a considerable amount of time pulling and prepping data, which left minimal time for actual analysis. The team was primarily using Excel, which was not only time-consuming but also hindered any potential cost savings for the department. The team wanted to automate the process but lacked the necessary expertise. While they were proficient in Excel and Tableau, they had limited experience in automation or coding. After researching various software, they found that most required extensive knowledge of Python and R, which they did not possess. They discovered Alteryx, which was user-friendly and had extensive out-of-the-box functionality.

The operations department of one of Latin America's largest banks was facing significant challenges with data processing. Prior to implementing Alteryx, the bank relied heavily on Access and Excel for data processing, which involved a lot of manual labor and was time-consuming. The data received was unstructured, often coming in spreadsheets and text files, leading to inefficiencies and a higher propensity for errors. The bank needed to automate multiple processes across different departments to improve efficiency and the quality of results, thereby eliminating duplicate errors and efforts.

The case study revolves around a business process outsource company that provides an End User Billing platform for insurance companies. The platform interfaces with numerous internal and external data sources, primarily focusing on coverage history and Cash Distribution. The system serves multiple client organizations, each with their own custom business rules, state-specific and federal rules, and variances due to implementation/on-boarding processes. The complexity of data sources, business rules, and manual interventions often leads to discrepancies. The rapid change in data sources and rules/regulations from state and federal regulators further increases risk in new development/enhancements in software. The Billing system generates billing from coverage history, i.e., the record of what coverage you have and the rates and other attributes. Issues arise in the regular updating of coverage history due to late arriving rate changes and other things that affect the elements that drive billing. Further adjustments after the initial bill can be done in error or done incorrectly. Billing operations is regularly challenged to react to discrepancies that are found between coverage history and Cash distribution data.

Natural disasters pose a significant challenge to healthcare companies, particularly in terms of timely response and communication with members. A North American payer was grappling with this issue, seeking to improve their response system to better support their members during such crises. The challenge was compounded by the fact that since 1980, the United States has experienced 246 severe weather events, each causing over $1 billion in damages. These disasters affected hundreds of thousands of members, leaving them stranded with limited access to coverage, medication, or medical equipment. The company needed a solution that could deliver timely notifications to members and provide on-site support once they were safe.

A leading nuclear energy solutions provider in the United States was facing significant challenges in managing and analyzing their data. The data in the nuclear energy industry is highly fragmented, making it difficult to match solutions with the specific needs of customers. The company was using large, disparate datasets in Excel that included information on specific machinery parts, instrumentation and guidance controls, and reverse engineering products. However, the datasets were too large and limiting, and there was no traceability to find answers to their own questions within Excel. The company was under pressure to improve their data management and analysis processes, but they lacked a dedicated analytics solution to help them achieve this goal. The ideal solution would be a technology that could combine spreadsheets and database information quickly and efficiently, and allow the company to learn from their actions within the solution and understand the impact on final results.

Lilly, a global healthcare leader, faced significant challenges in operationalizing data insights on clinical trials and setting up web-based dashboards to track progress. The team needed to accurately monitor, plan, and forecast patient status across all phases of clinical trials. This required understanding how patients were enrolled in trials and tracking their status over time. Lilly had complex manual processes in place using SQL, MS Access, and XLS to integrate 20 different data sets in S3 for a single study. They were manually executing SQL queries eight times a day to update reports and dashboards. However, these processes were siloed, leading to minimal collaboration. The challenge was to streamline and automate data flows downstream to enable business analysts, while controlling costs and efficiencies across all clinical sites.

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.

Merlin Properties, a leading Spanish Real Estate Investment Trust, faced a significant challenge due to the number of assets and the complexity of the data to be processed. The company needed a way to analyze and audit its financial and non-financial information efficiently. The starting point was a dynamic and complex business, with many different technologies coexisting and a large amount of data from multiple sources. The aim was to aggregate external supplier data sources and bring them right up-to-date, in order to have information from all suppliers in real-time. This posed a problem for Merlin Properties on three levels: extracting and processing data, visualizing that data in a more structured and organized way, and carrying out detailed data analysis to help with decision making.

The National Broadband Network (NBN), an Australian government-owned organization, was facing a significant challenge with its data analytics team. The team was spending 80% of their time on data preparation and only 20% on actual analytics. This imbalance was leading to frustration among the highly skilled analysts who knew they could deliver more insights if they had more time. The business leaders were also facing challenges in managing stakeholder expectations due to the limited insights they could provide. The goal was to flip this '80-20 rule' and enable the data scientists and the analytics community to spend 80% of their time on providing actionable insights with business value.

Nielsen, a global leader in audience measurement, data, and analytics, faced a significant challenge in 2017. Their Business Intelligence (BI) process was disorganized, with many small, independent groups rather than clearly defined silos. The situation was further complicated by an aging BI solution that had been running silently in the background for nearly a decade. The subscription for this tool had ended years ago, and it was only a matter of time before it would shut off without warning. This system was unsustainable, and a solution was needed. The urgency of the situation was heightened when Nielsen's encoding verification solutions (EVS) department, which relies on next-day BI reports to track the encoding process and ensure correct broadcasting and encoding across all available markets, discovered that their reporting process was built on the very BI tool that was about to be turned off. They had less than 90 days to find a solution.

NOVUS NEXT, a division of NOVUS Media, specializes in multimedia planning and digital strategy using data and analytics tailored to their clients’ specific needs based on their local geography. The team was analyzing over 50,000 syndicated data points on top of their clients’ data and KPIs to help make informed decisions quickly. However, before the launch of the NEXT division, the team spent more time setting up everything to prepare for work, rather than getting work done. They were inundated with time-consuming ad hoc requests and needed a way to automate their processes to make their job sustainable and scalable. They needed a solution that could help them build a reusable framework that ties ZIP codes, client data, and consumer data points together to create meaningful local context to assist their media strategists in determining the why and how essential to good media plans.

PASHA Holding, a leading company in Azerbaijan with multiple subsidiaries, faced significant challenges in their financial and management reporting processes. The company was spending an annual $250,000 on consolidation software, which was not only costly but also complex to maintain and update. The software was time-inefficient due to its complexity and VPN connection requirements, and it had a steep learning curve, creating a risk of dependence on staff. Additionally, the system did not meet PASHA Holding’s financial reporting needs in terms of time and quality. The company also faced issues with their management reporting. The MS Excel files used were overloaded, making them difficult to open and work with. The manual data blending process was time-consuming, and the limited functionality of MS Excel resulted in inefficient data analysis. Furthermore, the manual transformation and consolidation process posed risks to the accuracy and quality of the reports.

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.

Pickup, a division of La Poste and DPDgroup, is the leading collection point network operator in France. It offers shipping services through nearly 10,000 online retailers in France and is available in 28 countries with over 58,000 collection points worldwide. The company is constantly developing its network to respond to the increasing number of parcels being shipped, a situation that has been exacerbated by the COVID-19 pandemic. Pickup uses geomarketing studies to adapt its network based on population density, aiming to offer the most convenient pickup shipping service for consumers. However, the company faced a significant challenge in managing its network. It needed a solution that could use data to update the network in real time, allowing it to identify precisely the areas that needed strengthening.

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.

The U.S. Census Bureau, a leading provider of quality data about the economy, has been relying on outdated manual processes and tools for data collection, processing, analysis, and reporting. The Bureau's data is crucial for allocating over $675 billion in federal funding to states, local communities, and businesses. However, the volume, velocity, and veracity of the Bureau's big data have been challenging to manage with legacy systems. Manual surveying and data processing are labor-intensive, time-consuming, and costly. The Bureau was in need of an innovative solution to digitally transform its data collection, analyses, and dissemination processes, particularly for the U.S. Construction Indicator. The goal was to reduce operational costs while improving the accuracy and quality of economic indicators.

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.

Roquette, a global leader in innovative plant-based ingredients, embarked on a digital transformation project in 2018 to revolutionize their production facilities and processes. The company faced the challenge of managing and processing large datasets from 250 production processes that emitted between 500 and 3000 records every 30 seconds. The manual processing of data was time-consuming and inefficient, hindering strategic decision-making. For instance, one team was spending 100 hours manually exporting 3000 Excel databases. Furthermore, the company's 25 production sites needed to consistently operate at the highest level, requiring the assessment of the Sigma Level, a statistical term used in manufacturing to measure how much a process varies from perfection. This task was time-consuming, requiring approximately one working week of data consolidation for each plant.

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.