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.

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.

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.

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.

Ngozi Dozie and his brother Chijioke identified a significant gap in the Nigerian financial landscape, particularly in the areas of consumer lending and credit infrastructure. Out of 100 million adults in Nigeria, over 40 million of them did not have bank accounts, and there were only about 200,000 distributed credit cards in the entire country. Commercial banks were hesitant to offer consumer loans due to the high risk associated with lending to consumers without credit. Building a credit score in a market like Nigeria is a huge challenge, with little documented financial history or asset ownership. This presented an opportunity for Carbon, the fintech company started by Ngozi and his brother, to help serve the underbanked population of Nigeria.

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.

Catholic Education Diocese of Parramatta (CEDP) is an educational institution with 80 schools and 44,500 students across New South Wales. The institution holds a wealth of data on its students, from performance to attendance to demographics. However, CEDP lacked the internal resources to mine this data to improve student performance and advance operational goals. They sought a solution that could help them leverage this data to enhance student success and operations.

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.

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.

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.

Zidisha, a non-profit online microlending community, aims to transform the lives of people in some of the poorest countries by offering microloans to create businesses, attend school, or improve their living conditions. However, every loan carries the risk of default. Traditional lenders have found ways to identify, quantify, and price default risk, with higher risk loans attracting higher interest rates. The work of assessing risk commonly falls to a loan officer and the costs are passed on to the borrower. In developed economies where loans of thousands or hundreds of thousands of dollars are common, these costs can be comfortably absorbed without undermining the case for taking a loan, but this is not the case in developing countries. Employing a loan officer to assess default risk for a microloan results in interest rates as high as 40%, undermining promotion of economic development. Zidisha's challenge was to improve levels of repayments by identifying applicants most likely to be high-risk borrowers.

DonorBureau, a small company that provides modeling and segmentation services to nonprofits, was facing the challenge of providing more effective and accurate predictive models to differentiate itself in a competitive market. The company was dealing with over 900 million mail transactions, 140 million donations, and over 40 million individuals, and the predictive modeling demands were mounting. Ideally, they would like to have a large team of data scientists on staff, but those are coveted positions that come at a premium. Building and deploying predictive analytics is time-consuming, budget-breaking, and for the layperson, challenging to implement and maintain.

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.

Evariant, a rapidly growing SaaS company in the healthcare provider market, delivers a suite of innovative CRM solutions that help healthcare systems identify and execute on the most important strategic growth initiatives. However, the company faced a challenge in building and deploying predictive analytics, which can be costly and time-consuming. The complexity of their healthcare data demanded a high level of hands-on data preparation, making their existing solution adequate, but not optimal. They needed high-quality predictive analytics that could be generated both automated and semi-automated — and with an extremely high degree of reliability and validity.

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.

Predictive analytics is reshaping business and society, raising serious questions about how colleges and universities should prepare graduates. One answer may be to teach predictive analytics to all business school students. What would it take to implement this important vision and why is it not currently being done? As a business analytics professor, Kai Larsen’s goal is to teach a mixed range of students: those who immediately understand how predictive analytics has reshaped their future jobs (Information Management and Marketing), those for whom different flavors of business analytics have long since infused into the core of their fields (Operations Management and Finance), and those for whom predictive analytics currently is reshaping “only” a small part of their discipline (Accounting). It is becoming clear that all of these students must, at a minimum, understand predictive analytics conceptually to make decisions that will affect the future of their companies as machine learning tools continue to provide business insights and drive change within and outside the enterprise.

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.

Domestic & General (D&G), a specialist in providing warranties for household appliances, was facing a challenge in personalizing and delivering relevant offers to its customers. With 9 million customers in the UK and 16 million globally, the company was resource-constrained for the scale of personalized customer service and offerings they were trying to reach. The company's pricing team had to build a lot of models for each customer, which was a laborious and time-consuming process. D&G wanted to predict the likelihood of churn when customers are up for renewal and determine the price point at which customers are most likely to be happy with the warranty coverage they receive and renew their policies. However, delivering this level of personalization to individual customers required building a lot of pricing models, which was not scalable with their existing resources.

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.

Beacon Street Services, the services division of Stansberry Holdings, provides subscription-based publications of financial information and software to millions of investors globally. The company had a vision to have one single source of truth for all of its data, housed within Snowflake, to ensure consistency and accuracy across all applications of that data. Having migrated from AWS Redshift to Snowflake several years ago, the company had collected and stored great volumes of data within Snowflake. However, the company realized there was value to applying a data science approach to this data, especially for its marketing and sales teams. There was an opportunity to improve on previous tactics and processes of selling subscriptions, with a clearer feedback loop and signal for marketers to optimize their campaigns.

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.

The National Association of REALTORS® (NAR) is America’s largest trade association, representing over 1.4 million members around the country. Their members include brokers, salespeople, property managers, counselors, and others engaged in all aspects of the real estate industry. With so many members from unique backgrounds with varying professional interests, each looking for something different out of their membership, delivering value to them requires NAR to truly know their members well. To do that, NAR turned to the data. However, the association was trying to become more data-driven, and so was focused on higher-level objectives like understanding its members better and solving business problems that impacted its members. But because of the nature of how the two data scientists operated — without a centralized team or the appropriate resources - communication and feedback loops around data science projects were inefficient, and negatively impacted the ability of the data scientists to deliver value.

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.

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.

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.

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.

The University of California, San Francisco's Transforming Research and Clinical Knowledge in Spinal Cord Injury (TRACK-SCI) team is dedicated to improving patient care for individuals with traumatic spinal cord injuries. Each year, there are 17,000 cases of spinal cord injury (SCI) in the United States, often resulting in permanent challenges such as paralysis and sensory dysfunction. The estimated lifetime costs for each individual patient can range from just over $1 million to nearly $5 million. Acute clinical decisions made throughout SCI patient care, such as during surgery and ICU management, are critical for setting a patient up for recovery. However, clinicians lack guidance developed through data-driven research. One area of particular interest to the TRACK-SCI team is how blood pressure management during operating procedures affects a patient’s likelihood to recover.

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.