One of the most exciting parts of the Data + AI Summit is hearing about all the ways our over 10,000 global customers are using Databricks to improve their businesses.
With the Data Intelligence Platform, they’re empowering all employees to transform raw data into insights that help them do their jobs better, and outcomes that make internal processes more efficient.
Organizations including GM, Block, McDonald’s, J.P. Morgan Chase, the Texas Rangers, Unilever and over 250 other customers shared how they are using data and AI, the results they have achieved, and the tools that helped them get there. We wanted to compile all these great stories into one location to help other businesses see the transformative effect of data intelligence.
Use Cases Showcased at Data + AI Summit
Texas Rangers uses the Data Intelligence Platform to capture data at hundreds of frames per second to analyze player mechanics to help optimize personnel decisions and prevent injuries, among other use cases. Watch: How Data Intelligence is Delivering Big Wins at Texas Rangers
Minecraft reduced processing time by 66% with the move to Databricks, and is now able to use data and AI to enhance the gaming experience.
Blue River Technology, a John Deere subsidiary, showcased how it uses data and AI to power the new fully autonomous tractor. The machine is equipped with 360-degree cameras and is supported by AI for rapid image analysis. 
Ahold Delhaize USA built a self-service data platform on Databricks to allow its engineers to build pipelines that support data science and AI/ML applications. With the DI Platform as a unified data and analytics foundation, the company is able to analyze promotions and sales performance at scale, across different customer segments, in real-time to make more informed decisions. ADUSA also uses the DI Platform to support customer personalization, loyalty programs, food waste reduction, environmental initiatives, logistics, forecasting, and inventory management. Read: Workflow Helps Data Teams Scale and Reduce Cost
Block standardized its data infrastructure using the Databricks Data Intelligence Platform to pave the way for GenAI innovations. By leveraging Databricks’ GenAI capabilities, new businesses can now onboard even faster to the Square platform using AI-powered set-up and data import automation. They can also leverage GenAI to instantly generate content for marketing emails, team announcements, item descriptions, website copy, and more. eCommerce sellers can choose from more than 50 style prompts and add hyperreal, AI-generated backgrounds to product photos, elevating their websites and attracting more customers. With Databricks, Block achieved a 12x reduction in computing costs, continuing to redefine financial services in the 21st century. Watch: Building and Deploying GenAI Apps at Block with Jackie Brosamer, Head of AI, Data & Analytics. Read: Block Redefines Financial Services.
Doordash and Databricks came together to accelerate the pace of Databricks adoption for ML and streaming use cases to help accelerate the adoption of Databricks for workloads that perform more optimally with Delta and Spark compute. Watch: Accelerating Adoption of Delta Lake for Streaming ML Use Cases
Northwestern Mutual implemented a Retrieval Augmented Generation (RAG) system to enhance customer service efficiency. The insurance provider gave an overview of its RAG architecture and how it used Databricks to build a robust data pipeline for indexing content and collecting user feedback. Watch: Accelerating Operational Excellence with GenAI
AccuWeather utilizes its rich and quality-assured weather data in a proprietary algorithm that interprets forecasted weather into an impact value tailored to users’ data. By leveraging the most comprehensive weather datasets on the Databricks Marketplace, AccuWeather enables businesses to uncover powerful relationships between weather and operational metrics. Watch: Turning Risky Weather Obstacles into Business Opportunities
Shell shared their experiences, including initial hurdles in data strategy and governance, and how they used Unity Catalog and a business-owned data product approach to overcome them. They delved into the concept of Data Mesh, discussed the roles of the product team and the customer, and provided real-life examples. They also shared insights on using analytics, PowerBI, ML models, and AI for Data Governance. Watch: AI and the Lakehouse: Shell’s Journey Toward Effective Data Governance. Read Delivering Innovative Energy Solutions for a Cleaner World
Albertsons provides a model serving for an internal pricing analytics application that triggers thousands of models in a single click and expects to receive a response in near real-time. The grocery store chain provided an in-depth overview of how it achieved this complex requirement through its in-house developed model serving framework and Databricks serverless computing. Watch: Near Real-time With Databricks Serverless
AT&T, uses Databricks to streamline and accelerate new data products, everything from automated pipelining with Delta Live Tables to serverless Databricks SQL warehouses and AI/ML use cases. However, it can be challenging to meet complex security and connectivity requirements when workloads are not deployed on your own network. AT&T described how it met stringent security and regulatory requirements while adopting the Databricks serverless platform, starting with serverless SQL warehouses. Watch: AT&T’s Migration of Billions of Events Processing From Hadoop and AT&T’s Journey Towards a Serverless Data Intelligence Platform
The U.S. State Department’s existing manual review process for Freedom of Information Act requests was quickly becoming untenable. To get ahead of this challenge and ensure timely document release, the agency trained an open source supervised classification model to recognize human declassification decisions from 2020-2021 that performed to an accuracy greater than 98%, resulting in over 63% of the manual workload preserved.
Workday collaborated with Databricks to create an LLM capable of transforming inputs like job titles, company names, and required skills into new job descriptions. By training this custom LLM on a vast repository of Workday’s job requisition data, the team developed a model that met expectations while sidestepping the substantial costs and security risks associated with external vendor models. Databricks provided a platform for developing the innovative ETL, training, inference, and evaluation pipelines crucial to the project’s success. Watch: Time Series Forecasting For Infrastructure Resources
Bayer built ALYCE, the Advanced Analytics Platform for the Clinical Data Environment, which uses offensive and defensive data strategies, robust governance, and a tailored lakehouse paradigm design. The ALYCE Data Intelligence Platform enables the analysis of large, complex clinical data while adhering to regulations. It employs business intelligence, AI, and machine learning to expedite clinical trial data review, enhancing patient-centric study designs. Watch: Advancing Drug Development with Data Intelligence
Unilever: The global consumer goods giant uses Databricks in several ways, including:
- Unilever’s metadata framework, Blueprint, is a huge leap forward in Unilever’s lakehouse management with key capabilities. The framework brought Unilever’s engineering team together and increased development speed tenfold. Serving over 3,000 users with its downstream capabilities, Blueprint significantly advanced Unilever’s data engineering, setting new efficiency and scalability standards in its lakehouse architecture. Watch: How Unilever Uses Metadata to Power Its Lakehouse
- Unilever migrated to Unity Catalog in order to democratize its vast data sources in a secure, governed way. When Unilever made its Unity Catalog tooling production-ready, it opened up excellent new possibilities, specifically with Lakehouse Federation and Delta Sharing. Watch: Unity Catalog at Unilever
- For Unilever, forecasting has always been a hot topic. The company showed how it improved the existing forecasting processes by employing ML predictive models to predict various key business metrics at different levels of granularity. The engine behind this is a reusable predictive framework Unilever created and developed on Databricks that can be reapplied to different time series forecasting scenarios to gain development efficiencies and easier maintenance. Watch: Predictive ML: How Unilever is Improving Forecasting
The Centers for Disease Control and Prevention (CDC) needs cost-effective, responsive, and timely big data visualization for public health purposes. The agency applied an enhanced big data processing methodology with Databricks to produce visualizations to inform public health situational awareness and action. Watch: Big Data Visualization in Public Health
Sleep Number uses Databricks to quickly analyze large and complex time-series data from the sensors underneath each leg of its signature Smartbeds to generate personalized sleeper insights with the weight in the bed. Watch: Rapid PySpark Processing on Time Series Big Data in Databricks
Navy Federal Credit Union is a trailblazer in member personalization; a large part of this hinges on real-time capabilities. The company uses Delta Live Tables to power real-time analytics. Watch: Real-time Omnichannel Banking with DLT, DBSQL, and PowerBI
BP acknowledges the pivotal role of a robust data foundation in driving its AI and governance strategy forward. The company relies on the Databricks Data Intelligence Platform to power BP’s cutting-edge Unified Data Experience (UDX) solution. With UDX, BP strategically unified data engineering and management, resulting in a remarkable 25% boost in productivity, decentralized data ownership, streamlined metadata collection, ensured unwavering data quality consistency, optimized costs through flexible compute services, vigilantly monitored data reliability, and introduced a singular portal for lightning-fast data exploration. Watch: BP’s Journey with Unified Data Experience
JetBlue’s integration of Databricks Unity Catalog and Theom’s access governance platform marks a significant advancement in aviation technology, specifically in deploying generative AI. This approach addresses the industry’s data management challenges and regulatory requirements. Key to this initiative is BlueBot, an AI-driven chatbot that enhances customer service. The use of a hybrid Retrieval-Augmented Generation (RAG) model furthers the accuracy and relevance of AI responses. Crucially, the combination of Unity Catalog and Theom fortifies data security, ensuring compliance and data integrity, making JetBlue a model for AI application in aviation. Watch: Building a Secure and Scalable LLM Framework at JetBlue Related: Accelerating Innovation at JetBlue
CVS Health built the world’s largest RAG system for knowledge management. The head of machine learning at CVS shared how they created a unified and scalable knowledge platform to support finding information at CVS. Now employees can use semantic search and search through multiple systems and knowledge sources at one time. Watch: Building the World’s Largest RAG for Knowlege Management at CVS Health
Mastercard uses the Databricks Data Intelligence Platform as part of its multi-step journey to deepen its commitment to recognizing AI’s potential as a foundational element for commerce. The company is using the breadth of fully automated capabilities, ranging from data lineage, sensitive data identification, model development, and model governance, it can seamlessly marry data discovery to product development while ensuring compliance and transparency of data use. Mastercard also uses Databricks Clean Rooms to facilitate collaboration across multiple parties to solve modern data problems. Watch: Collaboration with Databricks Clean Rooms
AXA France shared how it harmonized data intelligence with innovative data management concepts to build a resilient, adaptable and intelligent data platform for the future. Watch: Harmonizing Data Intelligence and Data-as-Code Platforms
Chevron Phillips Chemical Company partnered with Databricks and Seeq, a specialized time-series analytics tool, to scale up its industrial IoT analytics and machine learning capabilities. Watch: Amplifying the Value of Timeseries by Combining Seeq and Databricks
Myntra’s migration from a cloud data warehouse and Hive to Delta Lake focused on optimizing clickstream data analysis of petabytes of data. The re-architecture aims at enhancing scalability, cost-efficiency, and performance. Delta Lake’s integration provides superior data handling capabilities, which is crucial for the retail sector’s dynamic needs. This transition has ensured significant cost savings and improved scalability to manage the vast data volumes efficiently. Watch: Shaping the Future of Data in India’s Premier Fashion E-commerce Platform
Skyscanner enriches 30+ billion analytical events per day, helping to optimize the business and traveler experience. The company simplified its analytical data infrastructure using the lakehouse architecture and Unity Catalog to deliver a practical approach to data governance. This has been critical to implementing impactful business-critical use cases, including machine learning and AI. Watch: Enabling Practical Data and AI Governance
Experian shared their plan to embed GenAI across all products and solutions, leveraging the Databrick Data Intelligence Platform. With hundreds of use cases documented for future delivery, Experian views GenAI as a critical tool that enables its key mission of helping customers improve their financial lives. Watch the Financial Services Industry Forum at Data + AI Summit featuring the VP of Engineering & Dark Web Intel at Experian
Bloomberg and Databricks have a strategic collaboration to allow mutual customers to seamlessly access Bloomberg’s extensive data offerings via Data License and cloud-based data management solution Data License Plus (DL+). These solutions have been designed to facilitate seamless data integration, setting the stage for data analysis acceleration, insights generation and unified governance for structured and unstructured data, as well as artificial intelligence (AI) and machine learning (ML) on any cloud or platform. Watch: Creating Optimized Thematic Portfolios at Bloomberg
Rivian drives on data + AI
Rivian uses Databricks in a number of ways, including:
- Rivian built on Databricks a secure, scalable, and cost-effective cybersecurity lakehouse on open standards to enable real-time, scheduled, cross-team, and predictive detections for security events. Databricks ease of use allowed Rivian’s Cybersecurity team to migrate its previous solution to the new platform in under 3 months while enabling Rivian to deliver new capabilities to end users.
- The company developed a solution on the DI Platform to remotely monitor vehicle performance and a broad set of vehicle diagnostics data, and use AI to predict maintenance needs, as well as energy and charge profiles. These capabilities not only slashed operational expenses for the company, but also facilitated a more agile, data-driven enterprise – and, ultimately, a better, more convenient experience for customers.
Read more about how Rivian is Driving into the Future of Electronic Transportation
Michelin used Databricks to help move its ERP data to a data lake and embrace a Data Mesh architecture, to unleash the power of business users to perform analysis. Read: Using Data to Streamline Business Operations at Michelin Watch: Using Data at Michelin
The International Finance Corporation (IFC) used the DI Platform to scale MALENA, its AI-powered development platform, to help address the challenges of poverty and climate change. Watch: Delivering Domain-Specific LLMs with GPU Serving
Mahindra & Mahindra Limited developed an industry-first enterprise-level Gen AI solution, Mahindra AI, to help drive growth, enhance customer experiences, and optimize operational efficiency. Examples of successful deployments powered by Mahindra AI include a GenAI bot for financial analysts, which led to a 70% reduction in time spent on routine tasks and enabled teams to focus on higher-value strategic initiatives. Looking ahead, Mahindra is also using the DI Platform to support multiple use cases and leveraging the Databricks DBRX open source LLM to build a Voice of the Customer chatbot using both internal data via Delta Lake and external data from websites and social media.
T-Mobile integrated its lakehouse into a Data Mesh using Unity Catalog and external Delta Sharing, to enable teams across the enterprise to use the data while still maintaining a rational and easily understood security model. Watch: Delta Sharing and Unity Catalog Lessons Learned at T-Mobile
Nasdaq uses Delta Sharing to help facilitate the sharing of data and AI assets across different cloud environments and platforms. Watch: Delta Sharing Unlocks the Value of Your Data
M Science leverages the Data Intelligence Platform to democratize data and analytics on petabytes of data. Unity Catalog also provides robust security and data management features. Watch: Democratizing Data At M Science
Nextdoor uses Delta Live Tables (DLT) to enable its analysts, data scientists and engineers to query events promptly for analysis, monitoring, and real-time aggregations while reducing its computing cost. Delve into Nextdoor’s transformation journey from hourly batch event ingestion to a near-real-time streaming solution with DLT. Watch: Event Ingestion Using DLT: Insights and Lessons
Comcast’s deployment of Unity Catalog has been pivotal in addressing the challenge of harmonizing data from diverse sources into a single reliable, secure system while enabling fine-grained access control and ensuring data lineage. Watch: Empowering Centralized Data Governance at Comcast
GM uses Databricks and Amperity to support its Customer 360 (C360) to drive meaningful business impact for its business and customers. Read: Elevating Customer Loyalty at GM. Watch: Building an Insights Factory at General Motors
Fox Corporation uses Databricks fine-tuning API to train several custom LLMs with distinctive style and tone, unlocking many Gen AI applications. Watch: Leveraging LLMs for Personalized Content Engagement
Conde Nast uses Delta Lake’s Change Data Feed (CDF) for efficient data modification tracking, ensuring GDPR compliance in real-time processes, and addressing regulations like ‘right to be forgotten’. Watch: Ensuring GDPR Compliance for Data Pipelines at Conde Nast.
Bridgestone Americas is using Databricks and its multicloud ecosystem to generate models in various areas, including supply chain, marketing, labor optimization, genomics, AI assistants/chatbots, and vision models. Watch: Growing AI / ML Maturity at Bridgestone.
The HEINEKEN Company uses Delta Live Tables to help data teams simplify streaming and batch ETL cost-effectively, automating task orchestration, cluster management, monitoring, data quality and error handling. Watch: Getting Started with DLT Pipelines.
Honeywell Intelligrated uses Unity Catalog and Delta Live Table as the backbone for effective data governance and efficient streaming data processing.
Migrating traditional BI workloads to the lakehouse allowed the company to standardize ingestion methods and open up new capabilities, like query federation and Delta Sharing – all while laying the foundation for AI customized on enterprise and IoT data. Watch: Honeywell Intelligrated’s IOT Streaming Lakehouse
Capital One partnered with Databricks to help build its own enterprise data platform that includes a real-time publishing and streaming platform, a cloud-based lake, and a consumption layer that’s enabled by open table formats like Delta Lake and Iceberg. All of the information is governed by a common set of metadata contracts that allow producers to make data available via self-service. Watch: How Captial One Enabled Innovation Using ‘You Build, Your Data’
Hinge Health uses Delta Live Tables to simplify change data capture, improve data reliability, meet SLAs and reduce TCO. Watch: Hinge Health’s journey to an optimized CDC architecture.
McDonald’s leverages Databricks Marketplace and ML to support decision-making around new site selection. Watch: How McDonald’s Uses ML to Optimize Restaurant Site Selection
KPMG uses the DI Platform to consolidate business intelligence and AI workloads, reduce the risks associated with data sprawl and simplify data governance. KPMG improved data accessibility and management across its different teams by enabling flexible, safe, and secure environments for project and engagement teams to collaborate with third-party market data in accordance with contractual terms and conditions by leveraging Unity Catalog. Read: How KPMG modernized their data estate with Azure Databricks.
Diageo centralizes their data on the Databricks Data Intelligence Platform. With Unity Catalog and the lakehouse architecture, the company was able to easily unlock other parts of its data roadmap. Watch: How Migrating to Unity Catalog Unlocked the Diageo Data Roadmap
DraftKings uses Databricks, including Workflows and Delta Lake, to ensure that the ratings it generates, which provide estimates of player and team skill in various sporting scenarios, are up to date. Watch: Simulating the Superbowl: Real-time ML to Predict the NFL
Moody’s Analytics relies on Databricks to handle both its data engineering and science workloads, helping the company achieve its strategic goals of unifying its analytical data plane. Watch: How to Efficiently Scale Your Data Analytics Team
North Dakota University System (NDUS) has leveraged the Databricks Data Intelligence Platform to develop a Retrieval Augmented Generation (RAG) architecture to better support administrative needs for processing unstructured data with large language models and created an AI portal for all NDUS staff, faculty, and students to access their approved AI apps. Watch: Unleashing the Potential of Unstructured Data with LLMs and Databricks
Asana turned to Databricks to help power data science solutions and empower its data scientists to more easily build models. Watch: Building Enterprise-Grade ML at Asana
Providence Health is partnering with Databricks to create a centralized “Model Marketplace” on the Databricks Data Intelligence Platform. The Model Marketplace initiative was designed to democratize access to a diverse range of ML models, allowing more than 120,000 caregivers to easily and efficiently access tools supporting their daily decision-making. By aggregating models from more than 65 different Databricks workspaces into a single, accessible location, the marketplace will simplify deployment, reduce complexity, and enhance model visibility and usability across the organization. Read about How Providence Built a Model Marketplace Watch: ML Ops and AI Governance in Healthcare
Related: Hear from Providence data architects about Healthcare Data Intelligence with Unity Catalog
If you need more inspiration for how to use data and AI to power innovation, productivity, and data intelligence at your organization, watch these and 200+ more customer sessions from the 2024 Data + AI Summit on demand.
See the latest customer stories to learn how data and AI teams are using Databricks solutions to build innovative data and AI solutions.
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