Verato, a startup specializing in patient identity resolution and master person index, recognized the need for higher-quality data in the healthcare market. The company uses a unique cloud-based solution that employs algorithms, or Referential Matching, to match vendor databases with patient data. This solution helps hospitals match medical records and patient data with greater accuracy, improving patient care. However, Verato faced a significant challenge in analyzing vast volumes of patient identity data. The company had no standard tool in place to analyze millions of records quickly and efficiently. Much of the work was being done in Python scripts, which did not allow for ad-hoc data analysis. Furthermore, Verato had to process ad-hoc requests on tight deadlines, with each customer providing 600 million rows of data. The company needed a solution that could automate workflows, process data inputs in a timely fashion, and connect to their Slack files and Mongo database.

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.

Monoprix, a major French distribution chain, was in the process of digitalizing its operations and wanted to leverage data to optimize its promotional campaigns. The company was looking for a solution that could forecast the sale of promotional products, anticipate supplies, reduce stock shortages, and analyze customer behavior. The challenge was to find a tool that could be used by business analysts without the need for coding skills. The company was also undergoing a complete overhaul of the architecture of its promotional system, which required the industrialization of its promotion production.

The Department of Defense (DoD), the largest business entity in the United States, faced significant challenges in managing and analyzing its vast data resources. With a 2019 budget of $686 billion and an estimated 3.4 million employees, the DoD generates an enormous amount of data. However, the defense sector's unique business drivers, policies, constraints, and operational challenges often make it difficult for traditional consultancies, solutions providers, and product vendors to understand and address its needs. The DoD's existing data collection, storage, and analysis processes were heavily paper-driven and lacked the necessary frameworks for effective data management and analysis. The data existed in hundreds of different places and lacked standardization across various lines of business. This situation made it difficult to increase collaboration, information sharing, and data-driven action. The challenge was to change the perception of data within the DoD and highlight the value it could provide.

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.

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.

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.

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.

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.

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.

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.

Adidas was facing a challenge with the repetitive task of updating PowerPoint presentations with new data. The process involved downloading data from a database, storing it in Excel, and manually copying and pasting it into PowerPoint. This process was not only tedious but also prone to errors. The company was looking for a solution that could automate this process, saving time and reducing the possibility of errors.

The Medicare Marketing department at an American health insurance company was facing a significant challenge due to the lack of a data solution for pulling and outputting non-mandated marketing campaign data, particularly for email campaigns to existing Medicare members. The company had no CRM tool or database, and the data source systems operated in silos, making it difficult to access and consolidate data for campaign creation. The company was primarily focused on acquisition efforts, with no retention efforts in place. The goal was to start promoting electronic communications as a cost-saving initiative and to begin dialoguing with members outside of the CMS mandated materials. The department was tasked with collecting data from these communications to give to vendors for message delivery and trend reporting.

Ansaldo Energia, a nearly 200-year-old company manufacturing generators and turbines for thermoelectric power plants in over 90 countries, faced a significant challenge with data quality. The company's main source of resource planning data, SAP, was being polluted with inaccurate data from various departments, negatively impacting the supply chain. This inaccurate data often led to incorrect orders, disrupting the manufacturing schedule and the planning department’s budget. Guglielmo Mantero and his team at Ansaldo Energia were tasked with creating a set of KPIs to ensure the accuracy of their ERP and SCP data. However, they needed a new method to monitor these KPIs, focusing on data quality and the real impact of inaccurate or outdated information on Ansaldo Energia’s manufacturing processes.

Armor Express, a leading designer, manufacturer, and supplier of defensive armor systems, faced significant challenges in managing its supply chain. The company's products, which include body armor and other protective equipment, are composed of up to 15 different items sourced from various suppliers worldwide. Without the right data, matching raw material orders with customer fulfillment was a significant challenge, risking over- or under-purchasing and unnecessarily long lead times. The company's supply chain was previously managed through spreadsheets and the knowledge of its employees, leading to frequent inaccuracies in raw material purchases. These issues not only affected the company's efficiency but also had potential implications for the safety of the end-users of their products.

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.

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.

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.

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.

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.

Anthony Nolan, a British cancer charity, was faced with the challenge of accelerating the donor registration process and using data insights to strengthen their work. The organization was dealing with disparate data elements from their data lake to onboard potential lifesaving donors onto the stem cell register. This process was time-consuming and relied heavily on Excel, taking hours each month. The organization was also dealing with growing datasets and ensuring regulatory compliance. The original team of four business analysts at Anthony Nolan were working in an environment lacking in data quality management and governance, which made it almost impossible to derive actionable insights. They were divorced from the data sources and lacked analytic tools such as stats packages and visualization tools. They could produce very little reporting and no true insight.

The medical research industry is heavily reliant on the convergence of science and technology to enable progress. However, the vast amount of medical data and information required to support these studies can potentially hinder the rate of progress. This was a challenge that Derk Arts, Founder and CEO at Castor EDC, a Netherlands-based data capture platform, was acutely aware of. Trained as a doctor, Arts was frustrated with outdated systems, processes, and lack of forward-thinking for patient-data. The aggressive spread of the coronavirus created an unprecedented urgency to process and investigate vast amounts of medical data captured within the Castor platform. The priority was enabling customers to take their data and quickly generate insights in a chaotic global environment. Arts was specifically looking for a vendor that could develop a web connector for Tableau to ensure they stayed true to the Castor software.