Announcing the 2026 Databricks Customer Awards Industry winners

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Summary

Announcing the 2026 Databricks Customer Awards Industry winners: ten organizations recognized across sectors, including financial services, health and life sciences, manufacturing, retail, energy, enterprise technology, public sector and more.

Each winner represents the most compelling data and AI story from their industry — organizations that have used Databricks to tackle complex challenges and deliver measurable results.

Congratulations to all ten winners, whose work demonstrates the transformative power of data intelligence across every corner of the global economy.

Every industry has its own unique challenges. The Databricks Customer Awards Industry winners are the organizations that didn't just face theirs — they used data and AI to solve them. The Industry Awards recognize one standout organization from each sector: the company whose work best demonstrates how data intelligence can drive breakthrough results, reshape operations and create new possibilities within their field. From a global bank unifying risk and finance data across regions to a hospital transforming how clinical data is ingested and reused, this year's winners show just how broad and deep that impact can be. This year's program recognizes 10 winners spanning financial services, communications, health and life sciences, manufacturing, retail and CPG, energy and utilities, enterprise technology, public sector, digital-native businesses and excellence in cybersecurity. Together, they represent some of the most inventive and impactful uses of Databricks we've seen — and we're thrilled to celebrate each of them. Without further ado, meet the 2026 Databricks Customer Awards Industry winners. Financial Services Industry Award: Sumitomo Mitsui Banking Corporation SMBC Group is a top-tier global financial group. Headquartered in Tokyo and with a 400-year history, SMBC Group offers a diverse range of financial services, including banking, leasing, securities, credit cards and consumer finance. The bank is using data and AI to integrate risk, treasury and finance; harden cybersecurity; and modernize the digital experience for clients. At the center of that transformation is a single Databricks lakehouse that brings SMBC's data together across regions — on one governed platform. Unity Catalog provides the bank with cross-region data sharing, lineage and access controls. Lakehouse runs analytics and AI at the scale a global bank requires, while holding the line on cost and complexity. On that foundation, SMBC is using Genie and Agent Bricks to put trustworthy, data-driven intelligence directly in the hands of the business. The platform powers a chatbot for the Cash Management System, generates Early Warning Indicators for portfolio risk and is being extended into front-office credit memo workflows — turning analyst hours into minutes. The results are measurable: Faster analytics and AI. Critical workloads — from reporting to risk signal generation — run on a consolidated, performance-tuned lakehouse instead of fragmented regional stacks. Lower run cost. Standardized data processing, centralized governance and active cost monitoring across dozens of production-serving endpoints keep spend predictable as usage grows. New revenue and risk levers. Credit memo automation, cash management self-service and Early Warning Indicators surface insight earlier.

These outcomes show how SMBC treats data as a strategic asset — operationalized every day across global teams to drive better, faster and more defensible decisions. Communications, Media and Entertainment Industry Award: Lumen Technologies Lumen Technologies is a digital networking services company that delivers the secure, high‑performance, intelligent connectivity enterprises require to achieve their AI ambitions. The company uses data and AI to give finance and operations teams faster, more consistent access to information. In finance, Lumen brings together data from domains such as revenue, billing and network expense so teams can work from a shared view of the numbers. The company consolidates this data on the Databricks Platform. It introduces natural‑language agents that let employees ask questions in their daily tools and get governed, explainable answers without specialist skills. On this foundation, Lumen: Improves visibility into revenue, billing and network expense. Extends agents to Enterprise Operations to inform contract discussions and vendor negotiations.

In Service Assurance, Lumen is making it easier to investigate and resolve network incidents that can affect critical customer workloads. A Lakehouse architecture, foundation models and multi-agent workflows provide a single conversational interface. In which teams can review service status and operational context, analyze tickets, review case history, run diagnostics and explore time to resolution in natural language. This approach is helping scale automation across service operations, with internal metrics showing 3 million+ AI-powered diagnostics, 35% ticket deflection and more than 100,000 AI-generated actions. Health and Life Sciences Industry Award: Hospital for Special Surgery Hospital for Special Surgery (HSS) is the world’s leading academic medical center focused on musculoskeletal health. For 16 consecutive years, it has been ranked #1 in orthopedics in the U.S. by U.S. News & World Report — a distinction that reflects sustained excellence across clinical care, research and innovation. Building on this foundation of leadership and impact, in June 2025, HSS launched an ambitious enterprise data transformation journey: to design and implement a modern data lakehouse on Databricks within an 18-month timeframe. The objective was not incremental improvement, but structural change, unifying a highly fragmented data ecosystem, strengthening governance at scale and enabling faster, more reliable decision-making across the enterprise. Rather than following a traditional use case-driven data ingestion model, HSS adopted a fundamentally different strategy — full system ingestion. In practice, this required ingesting source systems in their entirety, rather than selectively extracting subsets of data tied to individual use cases. ERP, HRIS, EHR, PACS and other source systems were brought in as complete datasets, preserving fidelity, context and enabling reuse…