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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.

The Covid-19 pandemic has led to a significant increase in remote working worldwide. As restrictions begin to lift, employers and employees face new challenges related to commuting safely and minimizing risk. This case study focuses on the imaginary example of Acme Corp, a company with around 1,000 employees across Greater London, operating from five different offices. The company needed to answer critical questions such as: What commute options are available for our employees if they wish to avoid using public transport? How can we efficiently allocate our employees between our multiple offices? How can we help employees avoid particularly busy stations or train lines? And how should we best consolidate the number of sites that we operate to minimize the impact on employees? These questions are not only relevant to Acme Corp but also to other organizations across various industries.

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.

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.

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.

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.

Veritone, Inc., an artificial intelligence technology company, was facing challenges in managing and analyzing large datasets for their advertising campaigns. The company provides tools and SaaS products to help TV and radio stations prove advertising attribution to their clients. However, they were using Excel spreadsheets for all their data blending, which was not only time-consuming but also limited in terms of flexibility to connect to different data sources. This process was subject to delays and slowed their ability to deliver enhanced analysis and reports to their clients. Additionally, Veritone wanted to develop a foundational Uplift Study to show the effectiveness of broadcast advertising compared to digital. This required analyzing and correlating data points from 250 radio and TV campaigns, a task that proved to be daunting due to the sheer volume of data.

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.

The Washington State Department of Health was faced with the challenge of managing and analyzing a massive influx of data from various sources such as hospitals, schools, and clinics due to the COVID-19 pandemic. Their traditional processes were overwhelmed and the use of virtual machines did not provide a solution. The department's data systems, which had been underfunded for the past 50 years, were built for single purposes, overly customized, and lacked interoperability. This resulted in a lengthy and complex process to clean, transform, standardize, and restructure data before it could be queried. The lack of tools to simplify or centralize this process led to a long time to insight and a great amount of duplicative work done by agency analysts.

Audi's Strategic Planning team was facing challenges with data analysis. The data was being accumulated faster, in greater quantities and in more diverse types than ever before in history. The current data analysis processes were inefficient and often ineffective. Data was scattered & fragmented, typically in many different places. Analysts were spending more time pulling data, transferring it into excel and then graphing it in neatly formatted powerpoint slides than exploring the data and actually analyzing what it means. There was a clear need for tools to empower data analysts, managers and executives - not IT - to explore and self-serve their own advanced data visualizations.

Clear Channel Outdoor, one of the largest outdoor advertising companies in the US, was facing several challenges. The company was dealing with unreliable business location data, which was previously addressed by licensing expensive, static datasets and geocoding addresses copied from websites. Additionally, the company's reports were constantly out of date and lacked visual appeal. These reports displayed all of the company's assets that affected each state and congressional district. The previous solution was an expensive, infrequently updated, and often incorrect report. Lastly, there was a resource bottleneck as the GIS Manager was the only person in the department with Alteryx, making the report unavailable if he was not present.

Delhaize America, a part of Delhaize Group with food supermarkets in 6 countries, was spending $1.5M/week on Sunday flyers. The company wanted to test the effectiveness of these flyers and determine whether they were providing a return on investment. The challenge was to conduct a test in a market representative of all Food Lion stores, where the test market would not receive a Sunday flyer. The company needed to understand the business reasons for the test, define criteria for success and failure, align on dates, stores, products that are involved, and identify blackout periods and other business initiatives that could potentially impact the test or control results.

Blend and Batch, a retail company, was facing challenges in generating store-level reports showing geographic sales trends, competitive impacts, and demographic summaries. They needed an accurate baseline for predicting new stores and remodels, which required block group-level sales, market share, and percent of store sales. They also needed to combine data sources to delineate a trade area and group stores to provide trends to measure performance. The company was using a combination of T-Log data from front end point-of-sale, MapInfo for geocoding, and MS Access for data calculations. However, this process was time-consuming and required manual steps.

Delhaize America, a grocery retailer with over 1300 stores across the Eastern USA, was facing challenges in various strategic areas including operations, supply chain, new site location, real estate, and marketing. The company was struggling with data discovery, report generation, handling ad hoc requests, mapping, spatial analysis, and querying databases. The process of trade area report creation was long, complex, and expensive. The company was also dealing with issues of replicability and continuity, with the output varying from run to run and difficulties in maintaining continuity with new staff.

GameStop, a $9B Specialty Retailer with operations in 14 countries, was facing several challenges. The demand for analysis was greater than the throughput, and the complexity of analysis requests or business questions was increasing. IT resources were constrained for reporting and data manipulation. The company needed to increase throughput, capability, and decrease dependency. They needed to reduce the need for complex coding (SQL), reduce time spent on repetitive tasks, and share analysis products and procedures across the team. They also wanted to focus on the problem, not manipulating data, provide insight versus analysis results, and increase analysis capability and skillset. Lastly, they wanted to remove IT from basic tasks such as reporting and data loading.

Hyatt Corporation, a global hospitality company, was facing challenges in increasing its corporate sales, which contributes to over 60% of revenue for its full-service hotels. The business environment was dynamic with high competition, long sales cycles, and complex customer relationships. The company was looking for ways to target and expand its share of the business. The challenges included limited customer insights, no global ranking, manual data extracts, and long durations for data refresh and ad-hoc analysis.

The national retailer of modern American-made home furnishings and accessories was facing challenges in understanding when to grow, how to grow, who to grow, and where to grow. They were struggling with identifying the right time in the buying cycle to introduce consumers to their brand. Their customer acquisition strategy was not data-driven, leading to an undifferentiated approach where all customers were treated the same. They were also unsure about their future consumer and how to reach them. Lastly, their retail expansion goals were not aligned with their customer acquisition and development strategy.

Ryder System, Inc., a leading provider of fleet leasing and maintenance, dedicated transportation, and supply chain management solutions, was facing several analytic challenges. They had an in-house analytics team of over five people, but there was no centralized data source. The team heavily relied on IT, resulting in long lead times for project completion. They were using various CRM systems that collected product line information on customer transactions, contacts, prospects, and pipeline opportunities for analysis. However, blending Salesforce data with other data sources was labor-intensive and sapped analyst productivity. Incorporating advanced analytics and visualizations complicated the process even further.

Sally Beauty, a global retailer with 3,673 stores worldwide, was facing challenges with its customer relationship management (CRM) and marketing analytics. The company's reporting was done in Access and Excel, and the transactional and customer data was not in a central location. The sales/transactional data was pulled from an application that did not support ad hoc reports and was no longer supported by IT. The company relied on IT to pull flat files for customer level analysis, which took days to weeks. Campaign data was not stored for performance trends or historical view, and month-end reporting took 2-3 weeks to pull all data and analyze.

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.

In early 2017, SCAN, a non-profit Medicare Advantage organization serving California, started receiving reports from members about receiving unwanted Lidocaine ointments by mail. This scheme had started affecting the industry in 2015, but had not yet impacted SCAN at a significant scale. The challenge was to identify the source of this scheme and stop it from further affecting the members and the organization financially. The Special Investigations Unit at SCAN was tasked with investigating these allegations that might have an adverse financial or compliance impact on members or the program. The team had to figure out the chances of one member being served by many pharmacies, dozens of members going to the same pharmacy outside California, every patient getting the same high quality, and several pharmacies sharing the same address.

Brookson faced several challenges in their operations. They had no visibility across departments, which led to a high amount of time being spent on creating spreadsheets. They also had a high volume of internal work orders and a siloed customer experience. These challenges were hindering their ability to deliver customer experience excellence. They needed to improve accuracy and timeliness, reduce investment in administration, ensure tasks were done right the first time, and become less reactive and more proactive.

The Colorado Hospital Association (CHA) was facing a significant challenge in the form of the opioid epidemic. Opioids, traditionally used to treat pain in the emergency department, were contributing to prescription abuse and serving as a gateway to illegal drugs. According to data from the Colorado Department of Health Care Policy and Finance, one Colorado resident died every 36 hours from overdose in 2015. From a data perspective, the situation was messy. The CHA needed to join emergency department data with claims base data to see what was happening across hospitals. Each question seemed to lead to another, creating a complex and challenging situation.

The team at Continuus Technologies identified a need for a search engine that could provide information about National Parks and Monuments based on a user's location, the features of the parks, and the expected weather conditions. Existing solutions, such as the National Park Service's 'Find a Park' feature, only provided state-level location information and did not offer accurate or updated weather forecasts. Google Maps' success varied depending on the quality of data in each zip code, which was inconsistent. The team sought to leverage their expertise as Alteryx Partners, data enthusiasts, and conservation advocates to create a solution that would empower others to preserve and admire the United States' natural resources.

Montgomery Transport, a transportation company based in North America, was facing challenges with their reporting process. The process was time-consuming, requiring three to four days of manual data entry. The company was looking for a solution that was easy to set up and effective at driving change quickly. They wanted a solution that could free up time for their team and publish to Tableau Server so that their executive team could get easy access to insights.

SEGA Games Co., Ltd. was facing a challenge with their promotional mailshots. The emails were sent to lists that had been collected over time, resulting in customers often receiving irrelevant content. This led to high unsubscribe rates and low hit rates. To address this, the team wanted to manage multiple datasets including gameplay data, customer histories, marketing and finance insights to improve analysis and reporting. They had implemented Amazon Web Services (AWS) to extract live data from game titles as they’re being played. However, they wanted to take this a step further by linking to other sales and gameplay data that was spread across numerous sources.

Brookson, a financial services company, was facing the challenge of automating the financial and tax advice being provided by their accountants. The goal was to provide real-time advice that is a capability beyond the reach of traditional accountants. The company recognized that they couldn’t teach their traditional developers accountancy, but knew that through Alteryx, they could get experienced accountants to execute advanced analytics.

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.

Honda was focused on commercializing services that create new value through the visualization and analysis of a huge volume of location data. They were developing BtoB data analysis services based on motion data captured from Honda’s own car navigation system, Internavi. However, they faced issues with processing time series location data with a time history. The huge volume of data stored in the cloud data was not being fully coped with, not only with Excel but even with BI tools.

The University of Nottingham, one of the UK's largest universities, needed to forecast its student population and the related income each year to allocate departmental budgets. The planning and performance team was responsible for collecting and analysing up to nine million records of data on thousands of students across the globe to model expected student numbers and activity accurately. However, with 56 departments requiring deep levels of analytical granularity and five-year forecasts, calculations were complex. By 2015, the team desperately needed a more dynamic student planning model that would allow increased granularity and subsequently improve decision making, whilst working alongside Tableau’s data visualisation. The task would be to handle all reporting for student planning and forecasting as well as verifying data for HESA (Higher Education Statistics Agency), the body that collects quantitative data about higher education in the UK.