X

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.

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.

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.

Birmingham City University is required to make mandatory annual returns to the OfS (Office for Students, previously HEFCE) to verify its government funding allocation. The Planning and Performance Department must pull together information from several internal systems and external sources to complete the aggregated funding return of student activity. Amassing and preparing the data from multiple sources is a significant, sizeable and complex challenge; with regulatory timeframes for the HESES return generally imposing a 7 working day turnaround from the census point of 1 December. Improving automation and underpinning processes became increasingly important both for securing efficiencies and for maintaining high levels of data integrity. It is essential that legitimate changes compared to prior years are clearly and accurately demonstrated, as this has the potential to affect the university’s core funding grant.

Symphony Care Network, a healthcare provider with 34 care facilities, was facing a significant challenge in tracking the exposure and treatment of COVID-19 among its patients and employees. The existing systems did not provide a consistent way to track this critical information, leading to inefficiencies and potential gaps in care and safety measures.

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.

SKOPOS, a technology company based in Europe, was seeking a solution to prepare, enrich, and model data for visualization in Tableau. The goal was to help its team visualize survey data in a more efficient and effective manner. The company was dealing with a large volume of data, over 2 million survey responses, and needed a way to analyze this data in near-real-time. The challenge was to find a solution that could handle the complexity and volume of the data, and deliver the desired results.

Takenaka Corporation, a major general contractor founded in 1610, has a wealth of architectural data spanning over 300 years. However, turning this vast amount of data into usable information for modern architectural design was a significant challenge. The data was stored in various formats and was difficult to manage and use effectively. The company wanted to use this data to expand the possibilities of architectural design and to integrate it with the thoughts and feelings of people today. The company was using Tableau, a Business Intelligence tool, to visualize this data, but it was not sufficient for their needs.

7-Eleven was facing a challenge in managing vendor data and gaining insights into their promotions. The process of requesting a vendor report or initiative was time-consuming, taking up to 100 hours. The company needed a solution that could be used by a business user 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.

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.

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.

ADL Digital Lab’s platform, Augusta, needed a flexible and efficient analytics automation technology that could integrate with any data source that could help drive the platform’s efficiency and security. The goal was to find a solution that could enhance the platform's capabilities and deliver better results for both ADL and their clients.

The Consumer Insights group of a top 10 CPG company was seeking growth opportunities, but they struggled to get analytics in a timely manner. They relied upon their massive amounts of company data as well as syndicated data. Handling syndicated sources was laborious. To answer a single question, it could take up to 50 clicks and 30 minutes. However, their users usually needed to ask several questions to help them better understand customer behavior. With hundreds of employees performing this type of work on a daily basis, this wasn’t efficient or scalable. Even worse, they still weren’t able to get to the level of insights their teams needed.

Food Service Rewards was facing a challenge with their traditional data analytics platform. The platform was not user-friendly and could only be operated by specialists with technical training. This led to a bottleneck situation as requests for data queries piled up. Moreover, by the time the results were generated, the need for that particular insight had often passed. To run a successful loyalty program, the team needed faster insights that would help them better understand customer churn and purchasing behavior to create more effective promotions.

SnapAV, a global supplier of electronic equipment, was facing a challenge with their business intelligence methods. The process of studying an aspect of their business required the involvement of a technical analyst, which could take hours or even days. This was a significant hindrance to the company's culture of curiosity and continuous improvement. The delay in obtaining insights often discouraged team members from asking questions, as they were unsure if the payoff would be worth the wait.

SnapAV, a leading manufacturer and retailer of A/V, surveillance, control, networking, and remote management products, faced a challenge after a series of mergers. The company consolidated multiple data sources into Snowflake and used Tableau to build visualizations for analysis. However, Tableau had a high learning curve, and business users struggled to get answers on their own. As a result, business intelligence teams had to step in to help, slowing down the speed to insight. This process was inefficient and couldn't last.

National Beverage, one of the largest soft drink companies in the US, was facing a challenge in generating actionable insights from Nielsen and SPINS data. The business intelligence team wanted to have a better view into the data that could be easily understood by non-technical people to get a better grasp on business performance. They also needed advanced analytics capabilities to solve complex problems like SKU rationalization and growth contribution analysis.

American Licorice Company, a leader in manufacturing and marketing candy products, faced a challenge in harmonizing and integrating their various data sources. The data and insights team had been manually working for years, which was inefficient and time-consuming. The fast-paced market demanded a more efficient process to deliver timely insights to the sales and marketing teams. The company needed to answer critical questions such as how to gain additional distribution of their products, capitalize on trends quickly, and bring innovation to the market fast.

Kearney School District had a challenge in visualizing their progress towards their objectives. The objectives were created by a committee and were aligned with the district's improvement plan. However, there was no visual representation of the progress being made towards these objectives, making it difficult to track and measure success.

The company aimed to create a comprehensive reporting system that would allow parents and teachers to view a wide range of student performance metrics. The challenge was to consolidate reports that would track various factors such as test scores, student infractions, and attendance.

Beeby Clark+Meyler, a New York-based marketing agency, was in need of an effective data visualization solution to provide actionable reports to their clients. The solution needed to cover all marketing endeavors of the agency, from website management to SEO, social campaigns, paid search, etc. The data was primarily sourced from Google Analytics but also pulled from many other analytical tools. A major challenge was that the data didn't arrive formatted the same way every time; sometimes it was even hard to capture. The agency also needed dashboards to function as working internal reports to highlight KPIs that would help them monitor and optimize campaign parameters in real time.

Kreditech, an online consumer finance company, was facing several challenges in its operations. The company wanted to reduce the involvement of its IT department in daily analytical processes and decentralize the decision-making process. This was to reduce dependency on single business intelligence experts. The company also wanted to combine multiple internal and external data sources to provide a foundation for strategic decisions and management meetings. The requirements for the solution included fast implementation time, robust self-service access for business users, fast in-depth analysis of huge amounts of data, maximum level of data security and privacy, and a scalable, stable infrastructure.

Lieferando, an online food-ordering service, was facing challenges in combining marketing and sales data in real-time. They needed to optimize their marketing campaigns and have a comprehensive view of the entire customer lifecycle. The requirements included real-time, self-service access to information, enterprise-caliber BI capabilities for multidimensional analysis, and low implementation time and cost.

Customer Alliance, a company that provides a rating system for the hotel industry, faced several challenges. Their data was spread across multiple systems, including MySQL databases, SugarCRM, and CSV files, making it difficult to consolidate and analyze. They needed to track the performance of their sales team on an hourly basis to ensure efficiency and productivity. Additionally, they wanted to combine data from sales and customer support to gain a comprehensive view of their operations and customer interactions.

Metro-West, the largest independent appraisal firm in the nation, was facing several challenges that were affecting its efficiency and decision-making process. The company was heavily reliant on manual reporting, which was not only time-consuming but also a significant drain on resources. The lack of real-time data was another major issue, leading to slower decision-making. Furthermore, the company was having difficulty leveraging data from disparate sources, which further complicated the decision-making process.