Alteryx
Overview
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HQ Location
United States
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Year Founded
2011
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Company Type
Public
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Revenue
$100m-1b
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Employees
1,001 - 10,000
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Website
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Stock Ticker
NYSE:AYX
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Company Description
Alteryx, Inc. was formed in 2011 and is a leader in self-service Data Science and analytics. Alteryx provides analysts with the unique ability to easily prep, blend and analyze all of their data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks.
Analysts love the Alteryx Analytics platform because they can connect to and cleanse data from data warehouses, cloud applications, spreadsheets and other sources, easily join this data together, then perform analytics – predictive, statistical and spatial – using the same intuitive user interface, without writing any code.
IoT Snapshot
Alteryx is a provider of Industrial IoT platform as a service (paas), and analytics and modeling technologies, and also active in the consumer goods, equipment and machinery, finance and insurance, healthcare and hospitals, and oil and gas industries.
Technologies
Use Cases
Functional Areas
Industries
Technology Stack
Alteryx’s Technology Stack maps Alteryx’s participation in the platform as a service (paas), and analytics and modeling IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
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Case Studies.
Case Study
Pepsico's Transformation to Smarter Sales Forecasting with Designer Cloud
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.
Case Study
IQVIA Accelerates Clinical Trial Data Processing for Rapid Healthcare Innovations
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.
Case Study
Stock Price Forecasting Using Monte Carlo Simulation in Alteryx
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.
Case Study
RCI Bank and Services' Transformation in Data Management with Alteryx
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.
Case Study
Ansaldo Energia Enhances Data Quality and Monitors Production KPIs with Automated Analytics
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.
Case Study
Bank of America's Transformation: From Reactive to Real-Time Regulatory Testing
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.
Case Study
Automating Regulatory Compliance: A Case Study on BT's Transformation with Alteryx and PwC
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.
Case Study
Predicting Passenger Flows at Dubai International Airport: An IoT Case Study
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.
Case Study
JPMorgan Chase & Co.'s Successful API Integration for Enhanced Project Management
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.
Case Study
KPMG's Digital Transformation with Alteryx: Enhancing Client Outcomes
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.
Case Study
Streamlining Transaction Matching in Large Datasets
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.
Case Study
McLaren Racing Leverages IoT for Enhanced Performance and Efficiency
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.
Case Study
Monoprix's Digital Transformation: Harnessing Data for Optimized Promotional Campaigns
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.
Case Study
NTUC Income’s Digital Transformation Journey with Modern Analytics
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.
Case Study
Siemens Energy's Digital Transformation with Alteryx
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.
Case Study
Siemens' Efficient Data Management: A Case Study on Alteryx and Tableau Integration
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.
Case Study
Automating Aviation Chart Creation: A Case Study
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.
Case Study
West Marine's Transformation: Elevating Customer Data Insights with PK and Alteryx
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.
Case Study
ABN AMRO's Transformation with Designer Cloud
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.
Case Study
Adidas Automates PowerPoint Presentations to Save Time and Cut Errors
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.
Case Study
Health Insurance Company Boosts Email Campaigns with Alteryx
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.
Case Study
Amway's Rapid Adaptation to Product Hierarchy Changes with IoT
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.
Case Study
Anglo American's Advanced Analytics Transformation with Alteryx
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.
Case Study
Anthony Nolan's Data Transformation Journey with Alteryx for Life-Saving Outcomes
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.
Case Study
Armor Express: Enhancing Supply Chain Efficiency with Predictive Analytics
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.
Case Study
Castor and The Information Lab: Leveraging IoT to Analyze COVID-19 Medical Research
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.
Case Study
Transforming Drug and Alcohol Support Services with IoT: A Case Study on Change Grow Live
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.
Case Study
Prescriptive Analytics: Unleash the Optimization Tool
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.
Case Study
Automation in Healthcare: CPP's Journey to Reduce NHS Procurement Costs with Alteryx
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.
Case Study
Corsicana Mattress: Optimizing Shipping Routes for Efficiency and Sustainability
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.
Case Study
Revamping Sales Compensation Model with IoT: A Case Study on CUNA MUTUAL Group
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.
Case Study
Deutsche Börse Group's Transformation with IoT: A Data Science Lab Case Study
Deutsche Börse Group, a global financial services company, saw an opportunity to transform the large volumes of stock data, previously considered as 'exhaust' of their trading business, into a significant revenue contributor. The company decided to invest in data science to sell not only raw data but also more advanced content. Despite having invested in on-premise architecture in the past, Deutsche Börse Group realized the need to build its new data science center in the cloud to leverage the cloud's flexibility and scalability. However, the company faced a challenge. Business users required specific transformations to be made to the data before it could be migrated to the cloud, but they did not want to overload the already busy IT team with requests. Furthermore, Deutsche Börse Group wanted to prevent their highly-trained data scientists from spending most of their time on data cleansing and preparation tasks, even after the data migration.
Case Study
Revolutionizing Container Supply Chain Processes: A Case Study on GHD and Alteryx
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.
Case Study
GC's Digital Transformation: Optimizing Data with Alteryx Analytics Automation
GC, the largest petrochemical company in Thailand and third largest in Asia, was facing challenges in its operations due to a lack of data-driven decision making. The company, which is involved in the manufacturing and distribution of a wide range of petrochemical products, had 70% of its business focused on engineers and chemical plants. However, the engineers were relying on piecing together data from different suppliers, which carried complex variables. This was not only inefficient but also hindered the company's ability to innovate and optimize its operations. Furthermore, the company was following traditional practices such as changing chemical catalysts at known intervals, without any data-driven reasoning. This was leading to unnecessary costs and resource wastage.
Case Study
Revamping Employee Entitlement Payments with Alteryx: A Case Study on Grant Thornton
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.
Case Study
Health Care Program Advisors Leverages IoT Tools for Enhanced Data Analysis and Revenue Monitoring
Health Care Program Advisors (HCPA), a boutique healthcare consulting firm based in Atlanta, Georgia, specializes in revenue cycle management, information systems, business intelligence, and clinical and operations performance excellence. They partner with prestigious hospitals and health systems across the nation. One of the significant challenges they face is assisting health systems in mitigating adverse financial consequences during electronic health record (EHR) implementations. EHRs are designed to maintain patient health records, streamline administrative tasks like scheduling and billing, and maintain accuracy and patient safety. However, implementing an EHR is a vast undertaking and can often leave health systems with significant financial issues if not managed properly. HCPA uses Alteryx for revenue monitoring before and after implementation, assisting health systems in understanding the effects of changes on revenue and offering insights necessary for accurate revenue capture. However, factors like revenue volatility, annual pricing adjustments, natural disasters, and the volume of historical data make establishing a performance baseline extremely difficult without advanced tools.
Case Study
Integratis Enhances Decision-Making with Alteryx Analytic Process Automation Platform
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.
Case Study
Jones Lang LaSalle's Digital Transformation Journey with Alteryx Adventure
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.
Case Study
Alteryx Streamlining Sales Prospecting
Joe Simpson, a sales professional at Alteryx, spends most of his day cold calling and messaging data professionals at large companies that are not yet using Alteryx. His challenge was to identify key targets for sales prospecting, which involved mining titles, roles, responsibilities, and locations. He also needed to identify prospects within driving distance for field events, those with titles most relevant to working with data, and to combine Salesforce Leads and Contacts for proper outreach. The process was complicated by the fact that many companies had different billing and physical addresses, and sometimes the primary address was a PO Box, which was not suitable for spatial analysis. Furthermore, the list of potential contacts could run into thousands, with titles ranging from CEO to Tax Intern, making it difficult to focus on those most likely to convert.
Case Study
Alteryx for Healthcare: Streamlining Supply Chain Forecasting
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.
Case Study
LATAM Private Bank Streamlines Operations and Saves Time with Alteryx
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.
Case Study
Automated Reconciliation Tool for End User Billing: A Case Study
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.
Case Study
Revolutionizing Natural Disaster Response with IoT
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.
Case Study
Optimizing Reporting in Nuclear Energy with Alteryx: A Case Study
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.
Case Study
Lilly's Transformation: Collaborative Engineering with Designer Cloud
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.
Case Study
LGAQ's Energy Saving Initiative through Alteryx
The Local Government Association of Queensland (LGAQ) was facing a significant challenge in managing and understanding their energy utilization, particularly in remote indigenous islands off the coast of New Guinea. The team had no clear understanding of their asset management or energy utilization for this area. They were using manual processes in Excel, Java, and MySQL for anomaly detection, which was time-consuming and inefficient. The team of four was doing the work of what twenty analysts should be doing, and they needed to increase their productivity drastically. The lack of a proper blueprint for energy saving and the use of multiple disparate points of data ingestion further complicated the situation.
Case Study
Digital Transformation and Automation of Internal Auditing at Merlin Properties with Alteryx
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.
Case Study
NBN’s Transformation: From Data Prep to Advanced Analytics
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.
Case Study
Nielsen's Business Intelligence Revamp with Alteryx and AWS
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.
Case Study
NOVUS NEXT Leverages Alteryx for Rapid Media Planning and Data Analysis
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.
Case Study
PASHA Holding Streamlines Reporting and Data Analysis with Alteryx
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.
Case Study
PDPAOLA's eCommerce Success with Google Cloud Dataprep
PDPAOLA, an online jewelry company, was faced with the challenge of differentiating itself in a crowded market. The company's Shopify eCommerce platform provided high-level profit margin analytics, but PDPAOLA wanted to delve deeper into the data to uncover more granular insights such as net margins or contribution margins. As the company began to build out data pipelines using SQL on Google Cloud, it quickly realized that it would reach a scalability limit. Hiring additional SQL developers and training them on the company’s unique processes would require significant time and resources. PDPAOLA needed a platform that would increase automation, allowing it to scale without added expenditure.
Case Study
Super-Local Solutions for Personal Deliveries: A Case Study on Pickup's Real-Time Service
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.
Case Study
PlusUp Boosts Efficiency and Reduces Errors with Google Cloud Dataprep
PlusUp, a leading company in advertising consulting services on social networks, was facing a significant challenge in analyzing paid social media. The company was routing all social media data through Excel spreadsheets where it was standardized and stitched together for analysis. This manual, time-consuming process left PlusUp constantly playing catch up and spending more time on prep work than on the actual analysis. Furthermore, manual data preparation and cleaning is prone to error, which meant that PlusUp had to triple check its work. As PlusUp grew its business, it couldn’t see any obvious ways to scale this process except by hiring more analysts.
Case Study
Historical Snapshot of Project Estimate-at-Complete: A Quantum Spatial Case Study
Quantum Spatial, Inc. (QSI) was facing a significant challenge with their Enterprise Resource Planning (ERP) system. While the ERP was effective for many tasks, it was not capable of tracking historical data, specifically project Estimates-at-Complete (EAC). The inability to track EAC on a week-to-week basis made it difficult for the Finance team to understand variances in EAC and identify potential issues if a project EAC varied greatly from its baseline. Furthermore, the company's ERP and Customer Relationship Management (CRM) systems were not integrated, creating gaps in the project lifecycle data collection. This situation was further complicated by the fact that QSI had no formal Business Intelligence (BI) system in place, and the Enterprise Systems team was unfamiliar with Alteryx, a tool that could potentially solve their historical data problems.
Case Study
Digital Transformation of Analytic Processes at the US Census Bureau
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.
Case Study
Room & Board Enhances Marketing Analytics with Alteryx & Adobe
Room & Board, a privately held, American retailer of modern home furnishings, faced challenges with data in marketing. The primary issue was the volume of data coming from different systems, which sometimes required real-time data streaming to feed decision management systems. Additionally, working across different departments and teams to get the necessary data and answers was a significant challenge. The shift towards digital analytics in the marketing department, due to the lack of in-person activities, made personalized email campaigns more important and relevant than ever. The company needed a solution that could blend data from different sources for a more personalized and effective approach, and speed up analytics to optimize marketing across channels and products.
Case Study
Digital Transformation and Efficiency Enhancement at Roquette through Alteryx
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.
Case Study
SaskTel's Transformation: From Manual Inventory Forecasting to Automated Analytics
SaskTel, a telecommunications company with over 100 years of experience, faced significant challenges in inventory forecasting. The company had been manually forecasting inventory, a process that was both time-consuming and inefficient. Byron Waugh, the Demand Planning and Forecasting Manager, had to manually extract data from SAP for each material number, analyze the history, and input the information into a spreadsheet. This process was not only tedious but also unsustainable given the 3,300+ active material numbers that SaskTel used. The company also faced supply chain issues due to the pandemic, with material lead times extending to 2.5-3 years. Previously, SaskTel would only order materials a few months in advance, leading to frequent rush orders and additional costs.
Case Study
SearchKings Doubles Customer Base with Google Cloud Dataprep
SearchKings, a digital advertising agency, faced three main challenges that they needed to address. The first challenge was the consolidation of data from different platforms, specifically Microsoft and Google, into a single view. This was crucial for them to have a comprehensive understanding of their data. The second challenge was leveraging data insights to better advise their customers regarding their investments. This was important for them to provide valuable advice to their customers and help them make informed decisions. The third challenge was enhancing their value proposition when working with larger organizations that required a flexible and scalable data feed as part of their solution. This was necessary for them to cater to the needs of larger organizations and provide them with a solution that could scale according to their needs.
Case Study
Spar's Global Retail Analysis Revolution with Designer Cloud
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.
Case Study
Leveraging IoT for Efficient Employee Commuting Post-Covid-19: A Case Study of Acme Corp
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.
Case Study
Transforming the Department of Defense with Data Analytics: A Case Study
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.
Case Study
Predicting and Trading on the Cryptocurrency Markets using Alteryx
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.
Case Study
Vector Logistics Enhances Data Automation for Improved Efficiency
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.
Case Study
Verato's Accelerated Time-to-Value with Alteryx in Healthcare
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.
Case Study
Veritone's Game-Changing Advertising Insights through Alteryx & AI
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.
Case Study
Vodafone NZ's Digital Transformation Journey Through Simplified Processes
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.
Case Study
Washington State Department of Health Leverages IoT for Efficient Data Analysis
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.
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