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The annual Data Team Awards showcase how different enterprise data teams are delivering solutions to some of the world’s toughest problems. 

Nearly 300 nominations were submitted by companies from diverse industries and regions across six categories. Each of these organizations have displayed remarkable innovation in their use of data and AI initiatives and we want to help tell those stories. As we approach the Data and AI Summit, we will highlight the finalists in each category in the days to come.

The Data for Good Award salutes the data teams who are making a positive impact in the world, delivering solutions for global challenges — from healthcare to sustainability.

Below are the finalists for 2023’s Data for Good Award:


Australian Red Cross Lifeblood

Australian Red Cross Lifeblood knows that saving lives begins with the ease of access to biological products, so life-giving donations can enable life-changing outcomes for Australia’s most vulnerable. Their Performance and Analytics team is using data and AI to strengthen engagement and support communities by recruiting new donors, recognizing existing donors and optimizing supply chains. Utilizing Databricks Lakehouse, Lifeblood can efficiently run granular and accurate forecasts to understand local dynamics. For example, by processing real-time data within their donor centers, they can predict wait times and cancellations, alerting donors of potential appointment delays. They look for opportunities to understand what drives local donations, as well as what donor gifts may increase donation frequency through complex segmentation and marketing attribution models. With Databricks Lakehouse’s unified architecture, Lifeblood has made it easier to attract more generous donors and strengthen bonds within their community, saving countless lives along the way.



In the United States, there are approximately six to ten million households that rely on lead service lines for their drinking water. However, the precise number and locations of these homes remain unknown. Fortunately, The Databricks Lakehouse has emerged as a powerful tool for BlueConduit, providing them with the means to swiftly and efficiently identify lead service lines and lay the necessary groundwork for their replacement. Databricks Lakehouse provides BlueConduit with a unifying architecture — so they can merge disparate sources, such as historical records, zoning and property information, water sampling data, infrastructure information, and demographics and use ML to predict the water lines that have the highest probabilities of lead exposure. Additionally, platform features around Data Engineering, MLOps, and job orchestration that easily integrate with infrastructure-as-code patterns enable BlueConduit to bring high-quality LSL decision intelligence to local governments in a scalable manner. The impact of BlueConduit's predictive modeling technology is evident in Detroit, where the city estimates saving millions of dollars in its search for lead pipes. To date, BlueConduit has inventoried more than two million service lines that serve more than five million people. The integration of Databricks Lakehouse has proven to be a game-changer for BlueConduit, amplifying their impact and enabling substantial progress in tackling the challenge of lead service lines.



It’s well documented that marginalized communities in the US continue to have subpar health outcomes in many areas, and in particular when it comes to childbirth. The data team at CareSource is using the Databricks Lakehouse Platform to level the playing field, eliminating these disparities by using data and AI to detect risk factors before they turn into complications. While high-risk pregnancies are a multifaceted problem, influenced not only by health history but also other socioeconomic determinants, CareSource wrestled with the challenge of not being able to use the entirety of their data for training machine learning (ML) models. Along with the inability to track ML experiments and trigger model refreshes, CareSource was blocked from sending time-sensitive risk predictions. By combining services like Feature Store, MLflow, and Stacks (currently in private preview), they built a repeatable end-to-end ML framework in 6 weeks to deliver models that predict time-sensitive obstetrics risk so mothers and their care providers receive results when it has the most impact. This high-risk obstetrics model is only the beginning for CareSource. They plan on using this same repeatable dev-to-prod ML workflow for other use cases that previously required specialized production solutions. 


Inari Agriculture

Inari is dedicated to building a more sustainable global food system by unlocking the full potential of seed. Through its SEEDesign™ technology platform, the company combines AI-powered predictive design and an expansive multiplex gene editing toolbox to advance critical solutions that benefit the population, the planet and the people who grow our food. Crucial to Inari’s ability to meet its objectives is the ability to understand exactly which gene edits in which locations will have the biggest impact. With Databricks Lakehouse and ML Flow as their underlying infrastructure, Inari’s data team is working to unify all their genetic and environmental data, running data pipelines that are feeding ML models in less than 20 minutes. Machine learning is used to extract insights that help identify rare variants, gene function, and gene-to-phenotype relationships, moving Inari forward on its mission to develop seeds that yield more while requiring significantly fewer resources. With the power of data and AI, Inari is uniquely positioned to help lead the way in addressing the unprecedented environmental food challenges as we work to save our planet and care for a growing global population.



Ontada is an oncology data, research and technology business dedicated to improving the lives of patients and transforming the fight against cancer. The Databricks Lakehouse Platform enhances Ontada’s deep learning natural language process (NLP) models and algorithms, improving the efficiency of the computation as well as improving the efficacy and accuracy of the algorithm. With the Databricks Lakehouse Platform, Ontada is able to quickly ingest and process millions of unstructured medical documents. One such example is the ability to accelerate the extraction of biomarker data from unstructured notes. This is important because access to and accuracy of biomarker information is critical for physicians to provide targeted therapies for patients, harness the promise of precision medicine, and ultimately, improve cancer care outcomes for patients. Combining our in-house data science and research expertise with Databrick’s data platform architecture allows us to scale and accelerate the speed of clinical insights while ensuring high-quality results, thereby acting as a cornerstone for Ontada's differentiation and data value offerings.


We will be announcing the award winners for each category at the Data and AI Summit on June 27th at 6:00 p.m. in the Expo Theater. We look forward to celebrating these amazing data teams with you there.


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