Montgomery County Government in Maryland faced a significant challenge during the COVID-19 pandemic as it sought to support the increased demand for services, particularly in managing the COVID-19 vaccination registration process. The county needed an immediate solution to relieve the pressure on its human contact center agents and deliver better engagement and service delivery for its MC311 Customer Service Center and Department of Health and Human Services (HHS). The goal was to offer residents a fast, easy, comprehensive, and equitable COVID-19 vaccination registration process. The challenge was compounded by the need to quickly augment its contact centers with AI capabilities to handle the surge in demand without requiring extensive developer expertise.

With 350 front-line employees working on power lines across the province, injury prevention is an enormous priority for FortisAlberta. Thus, before any work commences at a site, an injury prevention plan (IPP) must be created to identify the risks and hazards staff need to be aware of and the actions they must take to stay safe. This plan must remain dynamic throughout the lifecycle of the project site, as any change to the job could introduce new risks, requiring new plans of action. Before adopting a digital approach, FortisAlberta used a paper-based system to complete and manage IPPs. Supervisors had to fill out paper forms and send them by email to the Safety department within 30 days of project initiation. Recordings of meetings were captured on handheld audio recorders and cross-referenced with the paper forms during random periodic checks or following an incident. To share the IPPs, employees took photos or made photocopies of the completed forms and sent them for site auditing. This made it challenging to pull information together in time to be reviewed at team safety meetings. The paper-based process created several challenges. Handwritten IPPs were not always legible, which made it difficult to record accurate information. The check boxes on the IPP form did not accurately reflect the job’s scope of work, its roles and responsibilities, or its hazards and barriers. The paper forms required double entry, necessitating additional administrative work. And it could take as long as two weeks before forms got to the head office, if they didn’t get lost along the way.

The San Francisco Municipal Transportation Agency (SFMTA) faced significant challenges in managing and analyzing the vast amounts of data generated by its operations. The agency's traditional reliance on relational databases and middleware was proving inefficient and resource-intensive. The existing systems required extensive manual processing, such as pulling data from CSV files into Excel for analysis, which was time-consuming and labor-intensive. Additionally, the on-premises infrastructure demanded costly engineering hours for maintenance and upgrades, making it difficult to scale and adapt to the growing data needs. The SFMTA recognized the need for a centralized data management solution that could streamline processes, reduce administrative bottlenecks, and enable data-driven decision-making to improve transportation services and optimize revenue.

Five years ago, Jacksonville University (JU) faced significant challenges in accessing and utilizing data effectively. The process was cumbersome, requiring leadership to fill out forms and wait for the analytics department to provide static reports. This reactive approach hindered timely decision-making and limited the ability to leverage data for strategic purposes. The arrival of President Tim Cost in 2013, with his extensive experience in Fortune 500 companies, brought a new expectation for technology and data accessibility. The need for a more dynamic and proactive data culture became evident, prompting JU to seek a solution that would democratize data access and empower leaders, faculty, and staff to make informed decisions. The existing data processes were siloed, with different departments owning separate data sets, leading to inconsistencies and inefficiencies. Meetings with deans often resulted in conflicting data interpretations, as each department relied on its own data sources. This lack of a unified and authoritative data set hindered meaningful discussions and strategic planning. The challenge was to create a centralized and accessible data platform that would streamline data access, improve data accuracy, and foster a culture of data-driven decision-making across the university.

Rolls-Royce, a leading British brand in the manufacturing industry, is facing the challenge of transitioning to a net zero carbon future. The company recognizes the need to decarbonize rapidly to ensure its longevity and continue delivering excellence in its engine manufacturing. The challenge is compounded by the vast amount of flight data that needs to be processed and analyzed to optimize carbon output. The existing on-premises infrastructure was insufficient to handle the data volume and processing power required for this task.

Kinleigh Folkard & Hayward (KFH) faced the challenge of modernizing their IT infrastructure while ensuring robust security and mobility. With sensitive information across ten business divisions, the leadership was initially hesitant about cloud adoption. However, by 2018, their on-premises infrastructure was nearing end-of-life, prompting a reevaluation. The COVID-19 pandemic further disrupted their plans, necessitating a rapid pivot to remote work solutions. The need for effective communication and collaboration tools became paramount as lockdowns were announced, pushing KFH to fast-track their digital transformation efforts.

Most businesses generate a lot of data, but that data isn’t valuable for decision making if it’s not structured and analyzed. Analytics aren’t as effective if they are slow and expensive to produce. Inlitix was founded to offer a timely, easy-to-use, affordable BI as a service for small and medium-size businesses. They focused on interoperability across different ERP systems with different datasets and offered out-of-the-box reporting and analytics solutions. However, Inlitix found that it only needed more compute power at certain times, such as when it spins up development and QA environments or when production data is refreshed. It didn’t make sense to pay for a constant high level of compute resources during the times when they weren’t needed. To minimize the company’s compute requirements and maintain affordability, staff would manually adjust and optimize queries, which took time away from focusing on more strategic tasks and adding value to their application. Inlitix wanted to optimize the balance between cost and performance of its databases while ensuring it could quickly process data from customer ERP systems and refresh Power BI reports more frequently.

Intesa Sanpaolo Group, Italy's leading banking group, faced the challenge of improving its real estate operations and facility management to cut waste and reduce its environmental footprint. The bank manages thousands of buildings and is one of the largest real estate owners in Italy, with more than 40 million square feet of assets. The challenge was to reduce energy consumption, improve comfort, and optimize operational efficiency across its vast real estate portfolio. The bank needed a solution that would provide real-time visibility into HVAC and other systems in its buildings, enabling data-driven actions to be taken. The complexity of the bank's environment required a robust and secure solution, as data protection is a critical concern for the bank.

West Marine, the nation's largest retailer of boating and marine supply products, was facing a significant challenge in managing and analyzing their customer data. Despite having 240 stores nationwide, both on the retail and wholesale distribution side, they were heavily reliant on a third-party vendor for data management. This external database was not only costly but also disconnected, providing reports in Excel that did not offer a comprehensive view of the customer journey. The main issue was the inability to cleanse, enrich, and analyze their own customer data, which was hindering their mission to outfit, educate, and inspire anyone interested in the water lifestyle. The company was seeking a solution that would allow them to utilize their own resources, own their customer data, and trust their data with confidence.

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.

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.

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.

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 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.

Predict Crypto, a project by a student at the University of Colorado Boulder, aimed to predict, trade, and research the cryptocurrency markets. The project required a fully automated solution capable of dynamically pulling the latest data from a database, producing a new set of predictive models daily, and executing real trades on the live cryptocurrency markets every hour. The challenge was to navigate the volatile world of cryptocurrencies and observe patterns within the markets to make medium-term predictions. The goal was to cash in on these trends better than a simple 'HODL' (Hold On for Dear Life) strategy, which involves putting the investment away and forgetting about it for a couple of years.

The Russell Family Foundation (TRFF), a philanthropic organization committed to positive community impact, has set an ambitious goal to achieve a net-zero portfolio by 2030. This decision was made in alignment with their commitment to the UN-convened Net-Zero Asset Owner Alliance (NZAOA). Despite having already optimized its portfolio to be approximately 80% more carbon efficient than the MSCI benchmark through divestment from fossil fuels and investment in decarbonization technologies, TRFF recognized the complexity of the path to net zero. The challenge was not only about reaching their own net zero goal but also about setting an example for the broader philanthropic field. To achieve this, TRFF needed a comprehensive baseline of their carbon footprint, including all emission sources across their operations and their investment portfolio.