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PepsiCo, a global consumer packaged goods company, faced a significant challenge in calibrating sales forecasting to supply the right product quantities to its retailers. The sales forecast incorporated a variety of data, including warehouse data, store stock data, and promotional forecast data, all of which were provided by retailers in different file formats and delivered using various methods. The primary challenge was the speed of preparing a sales forecast. With the existing Microsoft Access and Excel-based processes, the time required to prepare this data was so extensive that analysts could only leverage it once a month or not at all. This inefficiency risked under or oversupplying retailers, potentially impacting PepsiCo's business operations and customer relationships.

IQVIA, a global leader in healthcare data and analytics, was grappling with the challenge of managing patient data for up to 70 different clinical studies run by various entities including government agencies, pharmaceutical companies, and academic institutions. The data, originating from 250 unique vendor warehouses, was being copied into a single system using legacy tools and processes like SAS, a process that took several days. Standardizing this data into FDA-compliant formats was another hurdle, requiring 1 to 2 months. As the rate of incoming data from clinical trials increased, and the data became increasingly non-identified and unstructured, IQVIA faced the risk of significant delays. These delays threatened to stall the progress of their clients' clinical studies.

The case study revolves around the use of Monte Carlo simulation for forecasting stock prices. The challenge was to create a sample Alteryx workflow that sources stock price data, performs analysis of the historical prices, uses these metrics to perform Monte Carlo simulations, and then analyzes the output of these simulations to drive business decision making. The aim was to provide an Alteryx template for Monte Carlo simulation-based forecasting that could be used and further enriched by the Alteryx community. The challenge also involved sourcing stock prices from Yahoo Finance, calculating daily percentage change in the stock price, preparing metrics for the simulation, and running the simulation multiple times.

RCI Bank and Services, the financial services brand of the Renault Group, was facing challenges in data management and digitization. The company was using Microsoft Excel for pricing, risk calculation, sales forecasts, results forecasting, and debt recovery. This limited their capacity for innovation and the implementation of new projects. They also had multiple solutions in place, including an SAS-trained credit scoring tool, an Oracle data warehouse, an ad hoc ERP, Business Objects for operational BI reporting, and SalesForce as a CRM tool, plus SAP for accounting. The company also had some in-house applications for ETL and web analytics. However, the data from all these tools was dispersed, preventing the RCI team from working consistently.

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.

Bank of America, a global financial services provider, faced a significant challenge in its regulatory testing process. The Enterprise Testing Team at the bank was responsible for ensuring that all regulators were notified of any applicable transactions. However, given the volume of transactions, which ran into millions every minute, manually preparing and cleansing this data for quality assurance (QA) was a slow and laborious process. The team could only confirm that the appropriate regulators had been notified up to two months after the transaction had occurred. This delay not only made the process inefficient but also left the bank vulnerable to costly regulatory fines in case of any system failure that required correction.

BT Group, the UK’s leading telecommunications and network provider, had to comply with strict reporting regulations due to its public service nature. The company was using over 140 legacy Excel models to run its regulatory processes, a process they named the ‘Cascade’. The Cascade required a large team and took up to four weeks for a single run, and had to be run multiple times before the reports were ready to be published each year. This process was not only time-consuming but also resource-intensive. Additionally, BT faced challenges with cumbersome change control due to complex logic buried deep in cell formulas, making it difficult to audit changes in Excel. The risk of errors was high with 140 complex Excel models, and even very small errors needed to be reported to regulators. Knowledge management was also a challenge as Excel isn’t the best tool for annotation and knowledge transfers, especially with 1,500 unique data inputs requiring specific methodologies.

Dubai International Airport, known for its high volume of transfer traffic, faced a significant challenge in managing passenger flows. The airport experienced peaks in passenger volumes throughout the day, with immigration halls and transfer security checkpoints fluctuating between being completely empty and overcrowded within short periods. While the airport could plan for expected passenger load profiles, changes to flight arrival times could significantly impact the actual passenger load profile. The Airport Operations Control Center (AOCC) had access to real-time queue information from sensors and cameras, but there was a need for advanced passenger load predictions and resource requirements. This would enable operational teams to collaborate with other stakeholders and proactively open more security or immigration lanes, in anticipation of passenger arrivals. The challenge was to predict passenger load profiles and manage resources effectively to prevent overcrowding and long queues.

JPMorgan Chase & Co., one of the oldest and most respected financial institutions in the United States, was facing a significant challenge in managing its active projects. The organization was using JIRA for their project management needs, but the limitations of the software's canned dashboards made it difficult to extract data in a manner that presented hierarchical information of all tickets in the queue. Due to JIRA server limitations, only a limited number of records from a given JIRA project could be pulled at one time. This lack of visibility into everyday tasks and the complexity of pulling data at a large scale was a significant issue for the organization. The challenge was to find a solution that could provide improved oversight into the organization’s active projects and automate the ideation and project creation phases of more than 10,000 projects over the past two years.

KPMG, one of the 'Big Four' accounting firms, has a long-standing reputation for delivering results for clients across 146 countries and territories. However, the firm recognized the need to digitally transform its operations to maintain its competitive edge and continue to provide high-quality services. The challenge was to streamline processes, optimize compliance, and help organizations transform their functions for the future. This was part of KPMG’s Tax Reimagined initiative, which aimed to help tax leaders embrace disruption, seize new opportunities, and drive greater value. To achieve this, KPMG needed to change the way its people work and interact daily, necessitating an investment in data and analytics platforms. The firm also needed to eliminate human error and automate its processes of entering and accessing data and performing quantitative analysis. The existing system was slow and lacked transparency, with some calculations taking up to 30 minutes to complete in Excel.

The challenge was to match credit and debit transactions in a single dataset made up of 24 million records. The dataset did not include any identifier that might match debit transactions (sales), and credit transactions (returns). Each debit transaction with a corresponding credit transaction had to be removed from the data, and isolated into a separate stream of records without that single identifier already mentioned missing. The legacy process was time-consuming and prone to errors, involving filtering for high count values, and finding their exact credit value. This process was performed manually, copying and pasting the debit row with sales and the credit row into a separate excel spreadsheet, and removing the “matched” rows from the larger dataset.

McLaren Racing, a globally recognized sports entity, has a rich heritage of innovation and success in Formula 1 racing. However, with over 20 race weekends in the Formula 1 calendar, each generating 1.5 TB of data, the ability to collect, process, and act on that data is crucial. The challenge was to make data-driven decisions at tremendous speed to improve performance both on and off the track. The team needed to analyze data from 300 telemetry sensors on each race car, generating 100,000 data parameters. Furthermore, the introduction of a strict budget cap of $145M by the governing body for world motor sport, the FIA, added another layer of complexity. The team needed to control operating costs while driving performance enhancements.

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.

NTUC Income, a leading digital and multi-channel insurer in Singapore, was facing challenges in managing and analyzing large volumes of data. The actuarial team at Income deals with data extensively on a daily basis, covering all aspects of data extraction, data preparation, data visualization, and data modeling. They relied on tools such as Microsoft Excel and Access, as well as some programming languages such as SQL, VBA, or R. However, data came from many different sources and in various sizes and formats. They used multiple tools to converge the data, which often created many silos of data processes. This resulted in data reconciliation issues in their end analysis and reports. Another problem they faced was that some of the data processing tools they used were not effective in handling huge volumes of data and required significant time for their analysts to manually customize the data to serve insights to multiple stakeholders. They were lacking in audit trail and documentation logs, which made it difficult for a new analyst to trace data errors or make enhancement to the existing data processes.

Siemens Energy, a global force in power generation and transmission, faced a significant data management challenge. The company's Transmission unit, with 36 geographically distributed factories, generated a vast amount of production, logistics management, and financial data. Each factory had its own database, adding to the complexity of data management. The company also had to pull information from various sources such as SAP, Salesforce, and Amazon Web Services. The organization was spending a significant amount of time consolidating data, checking its validity, and ensuring the proper functioning of formulas. The lack of data analytics and the complexity of data management prevented the organization from drawing full value from its data. The company needed a solution that would provide access and consolidation to gain full visibility into the company's data estate and automate the process to simplify it.

Siemens, a global company operating in 85 countries, faced a significant challenge in consolidating financial data from across the company. The process was complex, involving the integration of financial data with external market data, productivity data, and detailed data on customers or products. The finance department was tasked with calculating numerous KPIs, growth rates, and margins, which were then aggregated through a regional hierarchy and business segmentation. This entire process was conducted using spreadsheets, leading to a high risk of errors in the complex formulas used. Furthermore, any slight change in the analytical question required the controller to redo the entire analysis, a time-consuming and labor-intensive process. The company also faced difficulties in maintaining and updating a manual data preparation process that involved 3,000 lines of VBA code, which was prone to errors and hard to hand over to another person.

TerraVeta, a Geospatial information firm, was faced with the challenge of manually creating aviation charts. These charts, which provide essential information to pilots such as safety warnings, terrain contours, communication frequencies, geospatial data, and other instructions, had to be constructed with precision and attention to detail. The process was time-consuming and labor-intensive, with technicians having to extract information from a database piece-by-piece and individually plot each element. The challenge was to automate this process, reducing the time spent on menial tasks and allowing aviation experts to focus on larger conceptual issues.

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.

ABN AMRO, a leading bank in the Netherlands, was facing significant challenges with its data management and analytics. Data is central to ABN AMRO's strategies, including improving customer experience and optimizing compliance and regulatory processes. However, fulfilling data requirements for business users was a slow process, often taking up to two days. This delay was further exacerbated as the bank dealt with new and more complex data. The bank's data architecture was underpinned by an outsourced data center (IBM) and legacy tooling (SAS/Informatica), which were costly, inflexible, and not conducive to agile analytics. ABN AMRO recognized that its ambitious analytics goals were not aligned with its existing data technology.

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.

Amway, a multi-level-marketing company, manufactures over 450 different nutrition, beauty, personal care, and home products. Each of these products needs to be meticulously categorized and organized in its product hierarchy. The hierarchy requires daily updates as categories expand, business lines evolve, and new products develop. However, Amway’s original desktop-based model was complex and required numerous manual updates, proving itself slow and unsustainable. Amway attempted to smooth out some of these issues by switching to a virtualization solution, but the solution’s SQL-based transformations required too much involvement from the engineering team. Analysts and engineers had to communicate back and forth about data requirements until, days later, the outcome produced was as expected. Neither solution allowed for flexibility nor agile changes to the product hierarchy.

Anglo American, one of the world's largest mining companies, found itself grappling with a complex data estate and an outdated database solution. The company uses a variety of systems and tools, which made working with data on a day-to-day basis challenging. The company needed to perform more frequent and detailed internal audits, ethical business conduct reviews, risk management activities, investigations, and more. However, the wide range of data sources and audits to produce, different data types, levels of granularity, and output formats often made bringing datasets together a challenge. Anglo American needed a solution that would allow the team to cleanse and match data more effectively, with automation to help analysts cover more scope than with manual audits.

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.

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.

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.

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.

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.

The Collaborative Procurement Partnership (CPP), owned by four NHS trusts, manages three major contracts within the NHS Supply Chain Operating Model. The volume and variety of data CPP deals with daily are immense, working with 545 suppliers across five categories, translating to almost 370,000 different products. Each product could have more than 30 different pricing options, resulting in millions of data points to consider. In early 2019, CPP undertook a data and analytics review, revealing that they were not making the best use of all that data. Their existing system was heavily manual and resource-intensive, with most of their time and effort spent on data processing, not analysis.

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.