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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

MUFG Bank's Global Financial Crimes Division (GFCD) was facing challenges in handling massive data processing and diversified IT systems. The bank, being a megabank, had a large number of transaction data. In Anti-Money Laundering (AML) operations, the staff had to judge each case that the AML system detected with their own eyes, one by one, if it was truly suspicious. Also, detecting new high-risk transactions was a challenge since it required a significant workload. Another challenge was the insufficient human resources of data scientists who can perform exploratory analysis. They needed exploratory data analysis skills to spot global financial crimes and take action quickly. However, there were not enough data scientists currently in the Japanese labor market.

Vodafone New Zealand, a telecom service provider, was grappling with the increasing complexity of managing customer demands in a rapidly evolving technological environment. The industry's shift towards digital-first solutions and simplicity for customers was putting immense pressure on the services Vodafone needed to provide. This included new service locations, increased traffic demand, and faster services, all of which required accurate modelling, efficient monitoring of complex networks, and smart capital expenditure on network builds. The company was also dealing with a vast amount of data from various sources, which was being handled differently by different teams. This approach was manageable when demand was low and network complexity was under control, but as customer needs grew, the company needed to simplify its processes and be proactively prepared for change.

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.

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.

Rent-A-Center, a leading retail company in North America, was facing challenges in creating maps for their 3,000 stores. The process was manual, involving a lot of clicking, joining, queries, and sub-queries. This was time-consuming and inefficient, taking up to 12-1/2 weeks to complete. The company needed a solution that could streamline this process and make it more efficient.

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.

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.

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.

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.

Spar International, a global retail giant, faced a significant challenge in building a comprehensive report of its 13,000 stores spread across 48 countries. The complexity arose from the unique product naming conventions, supplier and brand relationships, and currency differences in each country. Additionally, Spar wanted to leverage Nielsen data for competitive analysis, which had its own product categorization that needed to be standardized to fit Spar's reporting. The task of finding a common denominator in all this diverse data, standardizing it, and joining it for reporting was a daunting task.

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.

The Toronto Star, a leading Canadian newspaper, was facing challenges in managing and analyzing its vast data resources. The company was using a variety of tools including MS Access, MapInfo Professional, and Tableau, but was struggling with data fear due to the potential for errors and the resulting legal implications. The company was also looking to improve its advertising targeting and circulation planning capabilities, and to support data-driven journalism. The existing tools were not providing the flexibility, speed, and ease of use that the company needed.

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.

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.

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.

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.

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.

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.

Vector Logistics, a Sales, Merchandising, and Supply Chain partner in Southern Africa, was facing a significant challenge in managing multiple data sets within the customer analytics area. The company was spending a substantial amount of time manually extracting files from SAP and consolidating them with files received in email attachments using Microsoft Excel and the Power Query functions. This process was repeated approximately 4 days per month and upon ad-hoc requests, such as employee changes including new hires, resignations, and role changes. The challenge was to create an automated workflow that would significantly reduce manual efforts in extracting, mapping, and cleansing data that was previously done using Power Query. The company also needed to map unstructured IRI/Shoprite/PNP Portal data to SAP BW data, thereby reducing efforts involved with cleansing data previously completed using 'text to columns', 'vlookup' and other manual interventions.