This year six teams from Benelux joined the global competition. These teams took on exciting challenges: from unlocking privacy-sensitive healthcare data with synthetic data to optimizing cheese production and much more! With access to the latest SAS software and SAS mentors, they were building innovative solutions to real-world problems.
Want to learn more about some of these cases? Read the article and check the videos to see how teams tackled challenges and learn their takeaways from SAS Hackathon!
Syntho: Unlocking analytics opportunities in health care with synthetic data
Privacy regulations lock up about 50% of valuable data, resulting in missed opportunities and revenue loss. The healthcare sector is particularly affected due to the sensitive nature of patient data. To address this, Syntho presented a groundbreaking solution at the SAS Hackathon: synthetic data. Synthetic data unlocks analytics potential by reproducing real data patterns while minimizing risks and helping make decisions faster.
During SAS Hackathon, the Syntho team collaborated with a leading hospital on a critical health care case involving cancer research. Their objective was to predict deterioration and mortality rates. With the help of AI, Syntho used synthetic data to mimic the characteristics of real patient data. This innovative approach unlocks previously inaccessible health care data within the hospital with less risk, more data and faster data access.
With the combined efforts of Syntho and SAS, it is estimated that $4 trillion worth of unlocked potential awaits across various industries.
PW Consulting: Predicting staffing costs with advanced analytics
With staffing costs accounting for nearly 70% of all hospital costs, accurately predicting and managing these costs are crucial for financial stability. However, the complexity of factors makes forecasting staffing needs challenging. Internal factors such as retirements, departures, hirings, and maternity leaves, coupled with external variables like changing demographics and the COVID-19 pandemic, further complicate the process.
The PW consulting team used historical data to build predictive models at the SAS Hackathon. They took an anonymized data set from CuraMare, cleaned it, prepared and transformed it to produce three scenarios: prognosis, machine learning and combined. The team then assessed the most accurate way to predict staffing costs and made recommendations based on that.
Notilyze: Optimizing cheese production with advanced analytics
Cheese production is a data-intensive process involving various quality parameters and machine metrics. Collaborating with a prominent cheese factory in the Netherlands, the Notilzye team aimed to optimize cheese production using advanced analytics. Combining and analyzing the available data, they wanted to enhance cheese technology, improve milk quality, minimize waste, mitigate environmental risks, and ultimately increase cheese yield without additional milk.
With the support of SAS, Notilyze helped the cheese factory enhance its production dashboard, helping them gain a deeper understanding of the key components influencing cheese production. Building upon this valuable insight, the team devised strategic recommendations to optimize cheese production techniques and maximize yield.
