DemystData, a New York-based software company, is dedicated to demystifying data for its clients, particularly financial institutions. Despite the increasing use of data in the financial sector, it is still heavily underutilized, leading to business decisions being made based on suboptimal or incomplete data. DemystData aims to close this gap by providing clients with access to new and more data. However, as datasets grow larger and data sources become more varied, the complexity increases, leading to more time-consuming work for the limited pool of data science resources at the company. The challenge was to manage this increasing complexity and workload without compromising the quality of data analysis and insights.

Lenovo, a multinational technology company, was facing a challenge in balancing supply and demand for its products among Brazilian retailers. The company aimed to predict the sell-out volume, the number of units of a product that retailers sell to customers, but was constrained by resources. The team had started developing R code to predict sell-out volume, with a goal to have it updated weekly for their top ten retail customers. However, with only 2 people writing 1,500 lines of R code for one customer each week, reaching their target of predictions for ten customers each week was impossible. The team needed to either invest in more data scientists or find a tool that could automate all the modeling and forecasting steps.

The Philadelphia 76ers, a professional basketball team in the NBA, is part of a new wave of sports franchises that are leveraging data analytics to optimize both their on-court performance and business operations. The organization has a strong focus on using data to inform decision-making processes across all levels. One of the key challenges faced by the 76ers' Analytics Team was improving the efficiency of their season ticket renewal process. The team had been using data science and simple modeling techniques, but lacked a dynamic machine learning tool that could adapt and learn as more data was collected. This meant that the team had to do a lot of work in the offseason to produce a static model. The goal was to transform the renewal process from a once-a-year event into a year-round retention process.

Harris Farm Markets, a grocery retailer in New South Wales, Australia, faced significant challenges in managing its perishable inventory due to unpredictable supply caused by wildfires and sudden spikes in demand due to COVID. With over two dozen stores and an expanding geographic footprint, the chain needed a way to consistently meet consumers’ demand for variety and freshness. The task of predicting demand for their 20,000 SKUs, including a subset of concurrent fresh produce running at 1200, was too vast for a manual approach. The company sought a solution that could provide accurate predictions with minimal labor on the part of the IT team.

Flexiti, a rapidly growing company in Canada, is recognized as the country's leading provider of point-of-sale financing with buy-now, pay-later solutions. Despite its success, the company faced a significant challenge. It sought to empower its talented risk and analytics team to gain greater visibility into data more quickly. The need for faster and more efficient data insights was crucial to maintain its competitive edge and continue its growth trajectory. The challenge was not only to speed up the data analysis process but also to ensure the accuracy and reliability of the insights derived from the data.

NTUC Income, a top composite insurer in Singapore, was facing rising claims costs across the insurance industry. As the cost of doing business increased, the company needed to understand the factors driving up claims costs, who was affected, and what actions to take. Furthermore, with insurance increasingly becoming a commodity, accurate price setting became more critical than ever. However, pricing analysis in insurance can be complex, repetitive, and time-consuming. The traditional method of using Generalized Linear Models (GLMs) for pricing analysis was not ideal due to several limitations. These included assumptions of a straight-line relationship between a rating factor and claim costs, time-consuming processes, and inability to analyze text in claim descriptions. The company needed a solution that could address their pricing analysis challenges and scale with their team.

DemystData, a New York-based software company, aims to 'demystify' data by providing a platform that helps clients discover, explore, and access the vast world of data. However, as datasets get larger and data sources more varied, the complexity increases, leading to more time-consuming work for the company's limited pool of data science resources. The company's clients, particularly financial institutions, are underutilizing data, leading to business decisions being made based on suboptimal or incomplete information. DemystData aims to close this gap by increasing their clients' access to new and more data.

Valley Bank, a regional bank with approximately $50 billion in assets, was facing a challenge in its Anti-Money Laundering (AML) department. The bank was dealing with an overwhelming volume of false positives in its effort to uncover money laundering activities across millions of transactions. The bank's AML team was seeking to reduce the manual work involved in predictive modeling. The process of creating models manually was time-consuming, taking weeks to complete. The bank was looking for a solution that could automate its fraud detection process and manage the volume of false positives in a realistic way.

Citi Ventures, the innovation arm of Citibank, is constantly on the lookout for emerging trends in technology and financial services that can help solve challenges faced by Citi and its clients. Since its inception in 2010, Citi Ventures has invested in over 100 different companies to enhance Citi’s products and services. However, the organization was seeking innovations that could solve challenges for Citi and its customers more efficiently. They were particularly interested in the field of AI and machine learning, which they saw as game-changing for the financial industry. They were looking for a solution that could empower both data scientists and business users, automating much of the modeling process and freeing up their time to focus on solving complex business problems.

MAPFRE, a Spanish insurance company, operates in over 100 countries, generating €27.3 billion annually. The company's analytics team is responsible for providing advanced analytics to help make decisions on pricing, sales, retention, underwriting, and more. However, given the demand for data insights, the team found it challenging to keep pace with the many incoming requests and deliver value quickly. The team needed to expedite its time to market in tackling new business challenges.

Anacostia Riverkeeper is a nonprofit organization dedicated to protecting and restoring the Anacostia River, which runs through Washington, DC and parts of Maryland. The river is heavily polluted, and swimming has been illegal since the 1970s due to health concerns about pollution. The current methods for testing water quality take days to return results, creating a delay between when the water is tested and when the results are shared with the public. Moreover, water quality can rapidly change with weather conditions, such as rain, making test results outdated before they’re even returned. Anacostia Riverkeeper needed a more efficient and timely way to monitor and predict water quality in the Anacostia River.

US Foods, one of America's largest food companies, was facing significant challenges with its legacy, on-premises data warehouse. The system required constant maintenance, experienced frequent resource contention, and could not affordably store more than two years’ worth of data. Business analysts took weeks to prepare a single report due to the system’s counterintuitive user interface, inability to load large data sets, and limited BI features. Reporting delays led some business users to seek insights from siloed Microsoft Access databases and Excel spreadsheets. Data science modeling to predict customer loyalty and churn rate was simply impossible. US Foods evaluated several cloud data management solutions, but none offered the right mix of performance and affordability.

Steward Health Care, the largest for-profit private hospital operator in the United States, was faced with the challenge of how to use predictive analytics, artificial intelligence (AI) and machine learning to derive value from the vast amount of data they are required to collect and maintain. The primary task was to improve operational efficiency across Steward’s network of 38 hospitals, with a focus on reducing costs. The company decided to address one of the most pressing challenges facing hospital operations — staffing volume. The typical hospital staffing model is set to average census and volume, leading to inefficiencies during peaks and valleys in patient volume. This results in high expenses for on-call staff and overtime pay. Steward Health Care’s CEO, Dr. Ralph de la Torre, challenged his team to find a more proactive approach.

Avant, an online lending platform, has been using data and machine learning to make smart loan decisions. However, as the company wanted to scale its business, it faced the challenge of maintaining the quality and sophistication of its analytics. The company needed a solution that would allow its analysts and business users to access data science tools that could be leveraged by the business teams. Avant was looking for a solution that was easy to use, statistically sound, supported by a reliable company, and simple to integrate with production systems.

Smith School of Business at Queen’s University in Canada is known for its innovative approach to business education, including creating ground-breaking programs and courses in emerging areas including artificial intelligence, fintech, analytics, cultural diversity, team dynamics, social impact and more. Anton Ovchinnikov, Distinguished Professor of Management Analytics at the Smith School of Business, teaches courses in predictive modeling, data science and machine learning. His students are typically working professionals who are consumers of analytics, not producers. Many of them are, or will soon be, managers of analytical projects and teams. As part of Anton’s courses, he wants his students to familiarize themselves with the raw coding, at least at a basic level, in order to fully understand what’s behind the curtain of what they’re trying to predict. However, the manual coding process can be time-consuming and complex, leading to a need for a more efficient solution.

Kiva is a financial services nonprofit that uses crowdfunding to underwrite loans for people who are underserved by traditional channels. The World Bank estimates that approximately 1.7 billion people are unbanked, meaning they do not have access to financial services offered by retail banks. This leaves many people without access to the financial instruments that much of the world takes for granted, such as credit cards and loans. Alternative banking methods tend to have high fees that can put them out of reach for the people that need them. This lack of capital hinders economic growth, opportunity, and equality in the places that need it the most. The key to Kiva’s mission is to ensure that those who apply for loans are successfully funded.

Florida International University (FIU), one of the largest universities in Florida, was facing a challenge in identifying and assisting at-risk students. Many of their students come from low-income areas, are the first in their family to go to college, or are the first of their family to enter the country. These factors often present obstacles that make it difficult for these students to progress. The university's analysis was more reactive than proactive, identifying students who had already faced academic or financial obstacles. The university wanted to be more proactive with data to better serve their students.

The Adecco Group, UK & Ireland, a part of the Global 500 ranked company, The Adecco Group, was facing an efficiency problem in their recruitment process. The traditional recruitment process involved multiple manual interventions, which were prone to mistakes and human interpretation. Recruiters had to sift through high volumes of CVs, making it difficult to match the right candidates to the right job. With recruiters working full throttle, it was easy for data-driven insights to remain hidden. The company was looking for a solution to reduce time and speed to fill open positions and improve their hiring attraction pipeline for client talent pools.

OYAK Cement, a leading Turkish cement maker, was facing a significant challenge. The company operates 18 plants in six countries with a production capacity of 33 million tons of cement each year. It was estimated that up to eight percent of CO2 emissions stem from manufacturing cement, the raw material needed for concrete. This was a major concern for OYAK Cement as it was contributing to the environmental problem and also risking costly penalties from exceeding government emissions limits. The company recognized that increasing operational efficiency by five percent would result in four to five percent cost-savings, along with reducing CO2 output by two percent — preventing the release of nearly 200,000 tons of CO2 emissions and eliminating $10M+ worth of CO2-related social impact costs per year.

Carbon Digital Bank, a financial institution serving the underserved African market, needed a way to quickly determine credit risk for individuals without prior credit. The bank also wanted to empower its data science team to take on additional business challenges. The bank had committed to a data-first strategy and looked to AI as an integral part of its decision-making. However, assessing customers' credit worthiness was a major challenge. The bank needed to expedite decisions on hundreds of thousands of loan applications every month.

84.51°, a retail data science, insights, and media company, was faced with the challenge of creating more personalized and valuable experiences for shoppers across the path to purchase. This included the entire customer journey, from initial awareness to activation, retention, and beyond. The company's goal was to leverage 1st-party retail data from over 62 million U.S. households to fuel a more customer-centric journey. However, the sheer volume of data and the complexity of creating personalized experiences for such a large customer base presented a significant challenge. The company needed a solution that would enable better production, deployment, and interpretation of data science models to meet its objectives.

Trupanion, a leading provider of medical insurance for cats and dogs, was dealing with a lot of data from different aspects of their business; pricing, sales, claims projection, customer retention, and more. They did a good job of reporting metrics, but they did not yet have the technical capability to analyze that data on a deeper level for optimal decision-making. This required more sophisticated technology and a lot of time. Trupanion was looking for fast and accurate predictive modeling software that is robust enough to support all their different data and information from different functions of their business.

Florida International University (FIU) is a top-50 public university that serves a diverse student body of more than 58,000 and 260,000 Panther alumni. Many of these students come from low-income areas or may be the first generation to attend college. The university has a proactive approach to keep students in school, which depends on spotting signs of trouble. However, the previous modeling tools used by FIU produced inaccurate results and required exhaustive manual input. The out-of-the-box solutions weren’t tailored to the nuances of their institution, they would flag students that weren’t actually at-risk.

Freddie Mac, a company chartered by Congress in 1970 to support the U.S. housing finance system, has been facing challenges in achieving meaningful predictions and key insights to inform business decisions. The company works with hundreds of thousands of customers and mines nearly four terabytes of data. However, they found that business intelligence and manual practices didn't scale effectively across this vast customer base and data volume. As market and economic conditions change, Freddie Mac must remain flexible and continuously deliver on its commitment to affordable, adequate housing. In a sea of unstructured and semistructured data, it’s challenging to achieve meaningful predictions and key insights to inform business decisions.

As datasets get bigger and data sources more varied, complexity increases and work processes become more time-consuming. Demyst clients need help identifying which external data attributes are predictive in marketing, risk, and portfolio management use cases across the vast ocean of external data.

Harmoney, a marketplace lending platform in Australasia, was facing the challenge of keeping pace with the constant innovation required to stay ahead of big banks. The company's small team of data scientists was tasked with the development and deployment of machine learning models to improve the efficiency of the personal loans market. However, the team was finding it difficult to dedicate sufficient time to predictive analytics due to their other responsibilities. Additionally, the traditional tools they were using for modeling were time-consuming and often led to distractions from the main goal of improving the business.

The fintech company, based in the US, was facing challenges in aligning their business process to regulatory compliance requirements. They were using machine learning models for decision-making, which increased the stakes due to the highly regulated nature of the industry. The company was already using DataRobot’s Enterprise AI platform to improve their model-building, but they needed to accelerate the alignment of their business process to model risk management regulation. They had several models built on DataRobot’s platform and deployed into production, including an internal credit score model, a fraud score model, and a dealer score model. However, they needed an independent model validation after partnering with a bank, which was a critical component of their partnership.

Ascendas-Singbridge Group (ASG), a leading sustainable urban and business space solutions provider in Asia, was facing a challenge with parking capacity at their properties. In densely populated cities like Singapore, parking capacity is a major issue. Despite having high-rise buildings with carparks or garages, parking capacity remained a challenge for both property managers and drivers. ASG wanted to forecast and predict parking lot capacity to optimize their parking services, improve the experience for visitors and drivers, and potentially increase revenue. They had previously used a different platform for model building, but it was costly and did not deliver the accurate predictions they needed.

Tommy Tan, CEO of TC Capital, a leading Pan-Asian boutique investment firm specializing in M&A and negotiated capital investments, was dissatisfied with the traditional methods of valuing firms used in investment banking. These methods, which include comparing past mergers and acquisitions, looking at stock market valuations of similar companies, and discounted cash flow models, were manually intensive and carried a high risk of human error. They could also lead to highly subjective valuations. Tommy and his team wanted to build their own valuation methodology, one that utilized cutting edge technology and took advantage of the amount of data available to bankers today.

Consensus Corporation, a subsidiary of Target, simplifies the complex process of selling connected devices. However, a major risk for retailers selling expensive devices and services is fraudulent customer activity. To address this risk, Consensus adopted fraud prevention as one of its core services. Through its automated machine learning-powered online engine, Consensus can alert its retailer clients to high-risk consumers before they purchase expensive devices. To identify potential fraud, Consensus built an advanced data model that leverages huge volumes of disparate data and undergoes routine updates. In order to be able to constantly refine its predictive models and alert their retailer clients faster to potential fraud, Consensus sought out technologies that would allow it to prepare this data faster for use in its machine learning models. The painstaking process of re-engineering SQL scripts took Consensus up to six weeks (on average) to update its fraud detection machine learning model. In addition, the data preparation process required sophisticated knowledge of data science techniques, leaving the company’s product and business intelligence teams unable to perform data preparation tasks on their own.