SAS Hacker's Hub

For the past two years, I’ve had the privilege of mentoring several SAS Hackathon teams, and the experience has been incredibly rewarding - both personally and professionally. I was able to connect with talented individuals from across the U.S.  and Europe, gain insight into innovative ways to improve public service and witness the excitement of teams discovering new software capabilities.   Judging by the last two years, SAS Hackathon 2025 is shaping up to be another incredible event. While the official kickoff isn’t until late summer, I want to highlight why public sector organizations should start thinking about it now—planning and being ready to sign up once registration opens.   Why Participate?   Hands-On Experience That Goes Beyond Demos There’s a well-known saying: seeing is worth a thousand words. When it comes to technology, I’d take it a step further - trying it firsthand is worth a thousand words and countless technical demos. SAS Hackathon provides a unique opportunity to bring your own data (or leverage synthetic data we can help generate) and experience the full analytics lifecycle with SAS technology.  Not only will you gain a deeper understanding of what’s possible, but you’ll also uncover any limitations—critical insights for making informed technology decisions.   Direct Access to SAS Resources Free trials are useful, but the overall customer experience often varies. When users encounter questions during a trial, they typically have to search for answers on their own—sometimes ending up with inefficient solutions, especially when working with new capabilities. SAS Hackathon offers a different experience. Each SAS Hackathon team is assigned a dedicated SAS mentor who provides onboarding support, technical guidance, and connections to the right subject matter experts at SAS.   For example, one of the teams I mentored last year initially planned to build a predictive model with structured data.  Midway through the Hackathon, they decided to pivot and incorporate unstructured data by leveraging SAS Natural Language Processing (NLP)—a capability they had little prior experience with. To support them, I quickly connected the team with SAS NLP experts, arranged a knowledge transfer workshop, and within a short time, the team successfully implemented NLP into their use case. This kind of direct mentorship and hands-on learning is what makes SAS Hackathons such a valuable experience.   Supporting Technology Modernization Government agencies are naturally cautious when adopting new technologies. They must ensure security and data protection requirements are met, verify seamless integration with existing infrastructure, and, of course, evaluate the software’s functional capabilities. The SAS Hackathon provides a unique opportunity for agencies to test-drive new technology. Participants can assess how well it meets their needs, determine the required skill sets, estimate the time to value for a successful use case, and explore the underlying architecture. This experience helps teams build a strong business case for leadership, accelerating the approval process when their organization is ready to modernize and invest in new technologies.   From my experience as a mentor, I saw this firsthand. One team I worked with discovered advanced visualization capabilities in SAS that they hadn’t been aware of before. With guidance, they quickly integrated these features into their analysis, enhancing their project’s impact and strengthening their case for adoption.   If you’re passionate about innovation and problem-solving and want to drive meaningful change at your organization, I encourage you to participate in SAS Hackathon 2025. Not only you get to work on your use case with one of the best data analytics software, but you will also be a part of the vibrant Hackathon community which fosters collaboration, creativity, and a deeper appreciation for the potential of technology to drive efficiency and productivity in the public sector. Reach out if you want to learn more about #SAS Hackathon! ... View more

The food industry has always run on very tight margins, including both the takeaway and eat-in food sectors. It is also an industry in which demand planning is very important. Hygiene regulations—and the nature of perishable foodstuffs—mean that food and ingredients must be stored correctly. However, they still cannot be kept indefinitely, even when refrigerated or frozen. Indeed, some foods can be kept for a few days at most, and many for less. Companies therefore cannot afford to over-order ingredients, especially perishable goods. If they do so, they will end up throwing out stock and losing money. Equally, however, if they do not have enough stock and ingredients available to enable them to meet demand, they will have unhappy customers. These customers may well move permanently to competitors, and leave negative reviews, both of which have implications for profits.   Forecasting demand and delivering supply Ultimately, food companies need systems in place to provide them with accurate forecasts of demand. These systems must be sensitive enough to predict busy times of day, and also highlight events that are associated with an increase in demand. They must also be able to predict variations in the type of demand, for example, for seasonal goods. This will enable the owners and managers of restaurants, takeaways and cafés to ensure that they can order the right ingredients, in the right quantity, and at the right time, to meet demand. A team from SAS partner DataCurate decided to bring this problem to the 2024 SAS Hackathon. The team wanted to develop a system that would enable food businesses to decide what ingredients to procure, when to procure them, and in what quantities, to meet demand. The plan was to support these companies to increase the efficiency of their operations, and particularly to minimise waste. The team used a two-stage approach. In the first phase, they aimed to forecast demand at store level by using historical sales and offers data. They also wanted to aggregate store-level forecasts to provide demand forecasts for central warehouses linked to larger companies. In the second phase, they wanted to move onto accurate procurement planning. This aimed to optimise procurement decisions by considering the future prices of ingredients, warehousing costs and product shelf-life. The focus overall was on timely procurement, minimising total costs and avoiding excess stock.   Using SAS to deliver results A pizza company supplied the DataCurate team with data from 30 separate pizza restaurants or stores, covering 13 different types of pizza with 28 different ingredients. The team analysed this data, looking at store-level sales, promotions at store and company level, and seasonal trends. The team drew on a range of SAS solutions to help them develop their procurement engine. They used SAS Data Explorer for data preparation, and SAS Visual Analytics to generate insights. They then added Model Studio for forecasting, and SAS Studio for customer optimisation algorithms. They created a model that optimised procurement decisions looking at demand forecasts, ingredient prices, shelf-life and storage costs. Finally, they used SAS Graph Builder to display their results. As it stands, the DataCurate model will enable the pizza company that supplied the data to improve its procurement decisions about both store and warehouse level. It should facilitate more informed buying decisions, including both timing and quantities. This will enable stores within the company to avoid running out of stock, and wasting ingredients, particularly those with a short shelf-life. More importantly, however, the model has much wider potential impact. With different data inputs, it could be used by other food companies and outlets, including both restaurants and takeaways. The combination of central warehouse and distributed stores is widely used in the industry, making this solution applicable around the world, especially for larger companies.   Creating an impact As Royson Almeida, the DataCurate team leader commented, “The company can ensure efficient operations and minimal waste.” Given the tight margins in the industry, this may be all that many food companies require to give them a competitive advantage. However, a competitive advantage is not the only aspect of this solution that is important. As the global population expands, we cannot afford to waste food. Better procurement decisions in the food industry help to ensure better use of global resources. This may only be one company at the moment—but the DataCurate solution has much wider potential impact. Accurate demand forecasting is an important first step in avoiding food waste. ... View more

Our team photo Team Name Esse quam videri Track Public Use Case Information transparency to promote equity in farming communities   SAS data maker and SAS Copilot Region NA (North carolina, USA) Team lead Harmandeep Sharma   @hsharma123 @Anuoluwapo @otsomefun @adelusi @ekwekutsu @OderinwaleOA @moakindoyin @DorcasAmoh  Social media handles https://www.linkedin.com/in/harmandeep-sharma-51912950/       Optional: Expand on your technology expertise     ... View more

  Team Name Code Blossoms Track Public Sector Use Case A smart wellness platform based on IoT smartwatches and AI data analysis Technology SAS Viya , Python, React, Nvidia AI workbench, Google Cloud SQL Region Australia (APAC) Team lead Xinyi Gao Team members @joangaooo @allenann @jiachengli  Social media handles Xinyi Gao: https://www.linkedin.com/in/xinyi-gao-cn/  Li Cui: https://www.linkedin.com/in/li-cui-73809027b/  Jiacheng Li: https://www.linkedin.com/in/jiacheng-li-b17b41242/  Is your team interested in participating in an interview? Y Optional: Expand on your technology expertise   Team photo   Project Overview: https://dr-ginseng-nvidia.vercel.app/ Github: https://github.com/Joan-gao/Dr.Ginseng    Pitch Video         Jury Video                                   ... View more

The Provost’s recent policy requiring students to complete course evaluations integrated into our Learning Management System (LMS) has resulted in a significant increase in response rates—from 32% to 80%. Students are alerted during evaluation periods, and during the last week of classes, evaluation completion is mandatory, with an opt-out mechanism provided once the process starts. Initial data shows no negative outcomes, with responses largely unchanged and stronger students participating more. While the Likert scale data has been analyzed, there is potential to gain deeper insights from the textual feedback provided in open-ended responses. We propose leveraging SAS’s powerful analytics, specifically Natural Language Processing (NLP) and Visual Text Analytics (VTA), to dig deeper into this qualitative feedback and uncover actionable insights that could help improve instructional methodologies. ... View more