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 Disruptor Award celebrates the data teams that are shaking up the status quo and leading the market by implementing cutting-edge AI use cases that others will follow, including early successes with applied Large Language Models.
Below are the nominees for 2023’s Disruptor Award:
Trust and disruption may seem to be unlikely companions, especially in the financial services industry. However, Bread Financial is defying this notion by displaying how it is possible to use AI to prevent customers from becoming victims of fraud. Rather than waiting for losses to occur, Bread Financial’s data science and fraud operations teams are using Databricks Lakehouse to proactively identify deposit accounts with a higher propensity for fraud, and then actively reviewing them for potential malicious activity. Through the rapid development of predictive fraud prevention models, the teams are accelerating machine-learning lifecycles to operationalize models in days, rather than weeks or months. In doing so Bread Financial is disrupting bad actors adept at social and technical engineering in near real-time, even as fraud tactics continue to evolve.
By enhancing its existing fraud solutions at a critical time, the company is not only preventing millions of dollars in losses, but offering protection and security to its customers.
Over the past decade, well-established luxury brands have been challenging their long histories to redefine themselves as fashion disruptors, and Burberry is no exception. But they haven’t stopped there. With the aim to always find new ways to engage customers, they’ve invested in building AI solutions that leverage computer vision to predict customer preferences with remarkable precision, ensuring they create the most effective content for their marketing campaigns and channels. With Databricks Lakehouse and LabelBox, Burberry’s team has developed a ground-breaking technology, that deconstructs marketing imagery into features and then learns which design components drive engagement. Via an easy-to-use self-service app designed for creative teams (not data scientists or developers) they can upload marketing images, retrieve a ranking of the content according to its predicted performance, and explore the insights in more detail. Brand teams are able to predict engagement with up to 87% accuracy at channel and market levels (e.g, what sort of images work better on Instagram versus ecommerce and emails). Burberry is the first in luxury fashion to blend creativity and science in this way. So while data provides the insights, the human remains in the loop, allowing space for human expertise and creativity.
By leveraging the power of LLMs and Databricks Lakehouse, JetBlue is deploying AI & ML products with the goal of optimizing operations, growing new/existing sources of revenue, reduced delays, and enhancing efficiency. They began by moving their data and AI platform off a highly-constrained multicloud data warehouse (MCDW) to the highly flexible Databricks Lakehouse, which can mesh data from various sources and then rapidly experiment and iterate on ML models and features including LLMs. With their groundbreaking BlueSky operational digital twin and embedded LLM chatbot launching in phases, JetBlue is empowering decision-makers with invaluable insights, enabling them to provide market-leading Customer experiences while achieving significant cost savings and operational efficiencies — from real-time weather to parts availability, to crew scheduling and more. The positive impact goes beyond traditional applications of AI & ML. Using Databricks, JetBlue is seeing enhanced ROI across various AI & ML products and a lower total cost of ownership savings. These financial benefits continue to provide ample TCO runway for additional industry-leading products further solidifying their position as an industry disruptor. JetBlue’s trailblazing approach is setting a new standard for the industry, shaping the future of air travel for the better.
Recruiting the right employee can sometimes be like finding a needle in a haystack, but what happens when you put generative AI on the job? Kelly Services is redefining productivity and efficiency in talent sourcing and development. Built using Dolly and other LLMs from Hugging Face in the Lakehouse, they’ve developed a GPT model, GRACE (GPT Recruiter AI Content Engine), that serves as a chatbot specifically designed for recruitment and HR tasks, such as writing text content for resumes, job postings, and emails. This industry-first solution has the potential to save thousands of recruiters an average of 200 hours per year, enabling them to work smarter and achieve better results. By prioritizing immediate business value and leveraging the agility of Databricks Lakehouse, Kelly Services has rapidly developed GRACE in just a few weeks, setting a new standard for innovation and time-to-market. With this strong foundation, they continue to explore and pioneer even bolder applications of LLMs, further disrupting the industry.
US Department of Veternas Affairs
One wouldn’t necessarily think of a social services agency as being known for tech innovation, but that’s exactly what the US Department of Veteran Affairs is doing by embracing the new world of LLMs to reimagine its supply chain operations. The Data Analytics Service within the VA’s Financial Services Center is using generative AI to implement automated scanning and analysis of millions of vendor listings to find the most cost-effective equivalent healthcare products. Using Databricks Lakehouse, the journey started with a hackathon to brainstorm new ideas from a broad spectrum of industry collaborators, followed by a rapid prototype that leveraged LLMs – a first of its kind at the VA. Large Language Models help the team to analyze vast amounts of unstructured data, including product descriptions and specifications, to identify functionally equivalent items and reduce off-contract spending. This transformative project has already achieved significant success, and the team has identified automated means to improve spending from 40% to 80%. Through the power of agile data science, the VA is ensuring superior care to America’s veterans by procuring the best available products and services, so that budgets can be optimized to spend less on logistics, and more on veteran care.
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.