Euskaltel Group, a leading telecommunications company in Spain, was planning a nationwide expansion. The company needed a scalable way to use AI and machine learning to attract and retain customers, reduce the incidence of default, and identify cross-selling opportunities. Their business intelligence team had experimented with AI on a limited basis but still spent considerable time writing code. The challenge was to find a more efficient and effective way to use AI and machine learning in their workflow.

Matmut, a major player in the French insurance market, relies heavily on data to elevate nearly every area of the company. However, the company was facing challenges in deriving insights within the limits of stringent privacy regulations. Matmut’s data lab was building predictive models with a single Jupyter notebook, a process that was manual and required considerable coding. This approach was not efficient and did not foster collaboration between data scientists and the business. The company was in need of a single solution that could reduce the effort and enable collaboration.

Embrace Home Loans, a prominent mortgage lender licensed in all 50 states and the District of Columbia, sought to optimize its marketing spend across its digital and direct mail channels. The company wanted to maximize marketing spend and increase revenue across all marketing channels. The challenge was to do so across the scale of Embrace’s operations, which was a significant task. The company needed a solution that could manage hundreds of Jupyter notebooks and run SQL queries on millions of rows of data. The solution also needed to ensure the security of Embrace’s customer data, which included risk-based and standards-based security protocols to protect all data.

AUTOproff, a European leader in digital dealer-to-dealer trading, was facing a challenge in scaling its operations. The company, which had more than 100,000 cars on auction in 2021, was struggling to produce car value estimates within the 20 minutes promised to customers. This task was entirely dependent on a team of skilled vehicle professionals. As the company grew, the need for scaling became increasingly important. The challenge was to automate the process of producing car value estimates to expedite the turnaround time for customers and free up the data scientists and estimators to focus on more rewarding parts of their jobs.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The fintech company provides consumer financing to merchants and consumers at point-of-sale through more adaptable alternatives to traditional lending programs. They built models to support the company’s projects in various departments including underwriting, accounting, and collections. However, they faced a challenge in the collections department. With tens of thousands of delinquent loans at any given time, there are a lot of calls for the Collections team to make. The more successful calls they have — measured by an industry metric called Right Party Contact (RPC) — the more likely they are to be able to successfully collect on these delinquent loans, and thus bring in revenue for the company. However, with such a great volume of target calls to make and generally low connection rates in terms of reaching the right person or party, any type of optimization or efficiency can make a big difference.

FOXO Technologies is a biotechnology company that aims to make longevity accessible to all using epigenetic science. They use machine learning to examine thousands of models to find patterns of DNA methylation that classify human health, wellness, disease, and aging. Their mission is to help people live longer, healthier lives. However, the data science team at FOXO found it challenging to scale as they looked to build thousands of predictive models based on 860,000 DNA probes. They needed a solution that could help them build, fine-tune, deploy, and manage models in production at scale.

Turo, the world’s largest car-sharing marketplace, sought to optimize its operations by leveraging data insights. The company connects guests and vehicle owners for mutual benefit across the US, Canada, and the UK. With over 1.3 million active guests and over 85,000 active hosts powering more than 160,000 active vehicles across 1,300 unique makes and models, Turo needed a way to efficiently manage its vast operations. The company aimed to optimize pricing, risk, and marketing strategies using data insights. However, the sheer scale of its operations presented a significant challenge in terms of data management and analysis.

The U.S. Army was facing a challenge of identifying funds that were potentially going to be lost due to expiring contracts. They needed an innovative AI solution that could help contracting officers accurately predict the contracts most likely to underspend their funding so they could quickly deobligate and reallocate these funds to other high priority projects. The Unliquidated Obligation (ULO) project was born out of the Army’s HQ Analytics Lab (HAL) and Deep Green OBT initiatives.

MinterEllison, a multinational top-tier law and professional services firm, was looking to grow profitably and sustainably as part of its 2025 strategy. The firm, which operates in five countries, needed a more sophisticated, predictive lens to understand what might happen, especially in the wake of the COVID-19 pandemic. The firm's existing data analytics platform was not sufficient for this task. The firm's Head of Data and Analytics, Shaheen Saud, emphasized the need for a good understanding of performance and opportunities, which prompted MinterEllison to take an innovative look at its IT and digital services infrastructure.

Sanlam, Africa’s largest non-banking financial institution, exists with the purpose of empowering generations to be financially secure, prosperous, and confident. However, the company was facing challenges with its data science operations. The open-source AI options they were using felt cumbersome to navigate and lacked critical explainability for business stakeholders and compliance. This was hindering their ability to drive critical business value levers such as sales and client retention. The company needed a more streamlined and transparent AI solution that could help them improve their operations and deliver better results.

Cegid, a French tech company offering cloud services and management software solutions, is facing the challenge of creating more models in less time while minimizing the technical skills and resources required. The company serves 350,000 customers across 150 countries and generates €632 in revenue. The predictive analytics team at Cegid is under pressure to meet the ever-expanding demand fueled by frequent acquisitions. The team is tasked with tackling a growing list of business challenges, including predicting the likelihood of getting paid on invoices and the propensity of customers to add services.

Decode Health, a healthcare AI company, has always relied on predictive analytics to unlock discoveries using data. However, in the early days, modeling was a slow, manual task. Analyzing a single dataset could take two to three weeks, with two to three data team members working around the clock. This exhaustive manual effort included considerable time preparing data, waiting on models, recalibrating, and waiting again. The company needed a solution that could streamline this process and deliver accurate results more quickly and cost-effectively.

Phoenix Children’s is one of the nation’s largest pediatric health systems. It provides world-class inpatient, outpatient, trauma, emergency, and urgent care to children and families for more than 38 years. The organization is continuously at the forefront of innovation and is recognized among the nation’s top-ranked children’s hospitals. Phoenix Children’s wanted to use analytics to improve both clinical and operational decisions. However, manually building a single model took the better part of a year. The healthcare system knew that a certain percentage of children who present with other health concerns may actually have undiagnosed malnutrition. If they could identify cases of malnutrition, they could intervene and influence outcomes.