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 has displayed remarkable innovation in their data and AI initiatives, and we want to help tell those stories. As we approach the Data and AI Summit, we will highlight each category's finalists in the coming days.
The Unified Lakehouse Award celebrates the data teams that have implemented a unified enterprise data platform architecture, moving from data warehouse to lakehouse to optimize costs and lower complexity by bringing together all analytics and AI workloads.
Below are the nominees for 2023’s Unified Lakehouse Award:
Condé Nast is a global media company producing award-winning journalism, content and entertainment for every platform today and operates in 32 markets worldwide. Recognizing the immense value of data in today's digital landscape, the data team embarked on an ambitious journey to centralize all data in Databricks Lakehouse, eliminating the need for other data warehouses, including cloud options like Redshift and Snowflake. With a unified view into over 3PBs of data, they can handle any analytics or ML workload on one platform while also saving them $6M in infrastructure costs and $2.6M in data engineering productivity. But it’s not just about IT impact; they are also delivering value to the business. 2000 ML models are serving predictive analytics to drive solutions in areas like segmentation, personalization, and subscriber churn, and over 200 business users are accessing reports via Databricks SQL, with all data access securely managed and tracked with Unity Catalog. The data team at Condé Nast isn’t stopping there — they are looking ahead to realize more cost optimizations with Serverless deployments of Lakehouse components and accelerate more value to the business by leveraging Lakehouse data.
Dedicated to aligning technology with customers' needs, Epsilon strategically utilizes data and artificial intelligence (AI) to drive customer engagement. Whether it’s tailoring marketing communications to individuals for better campaign results or analyzing customer data to forecast the probability of purchasing particular products, Epsilon is aimed at optimizing customer interactions to increase revenue for clients, all while respecting and protecting the privacy of all parties. With the adoption of Databricks Lakehouse, Epsilon has achieved a unified data architecture that combines customer and transactional data with machine-learning capabilities at an unprecedented scale. Epsilon has improved its data and AI initiatives by leveraging the Lakehouse Platform. They have effectively utilized Delta Lake and Photon, leading to improvements in their computational capabilities. As a result, they have experienced nearly an 80% reduction in the total time it takes to complete jobs, enabling faster innovation. Epsilon is looking forward to what future accomplishments are to come as a result of unifying on the Lakehouse platform.
As you might expect with a global brand, Mercedes-Benz has a massive, ever-evolving complex ecosystem that requires more and more access to data to ensure peak performance. Specifically, reporting on this data becomes critical to success. The old concept of database storage only adds more cost as the data volume increases, which also impacts the overall performance of the systems. In the past year, the IT data team at Mercedes-Benz Singapore was one of the pilot teams to implement the revolutionary Databricks Lakehouse architecture for their Region Overseas markets. Data was migrated from databases to Delta Lake storage and tables allowing for the eventual deletion of the older databases. Now all the data sits on Azure Data Lake Gen2 and is accessed easily via Databricks and Power BI. All data loads happen via Databricks – the Reporting teams easily access the data, and the Data Science team easily builds their workloads on Databricks.
Siam Commercial Bank
Siam Commercial Bank’s (SCB) has been actively developing data-driven services to meet the growing demand for digital financial management. Using data and AI, SCB aims to provide a holistic view of customers, improve operational efficiency, and enhance customer lifetime value. One key use case is using data to modernize the loan process, deliver better recommendations, and create an optimal digital experience for millions of customers across Thailand. SCB migrated its on-premises data warehouse to gain significant value from Databricks Lakehouse, utilizing nearly every component of the platform. Using the integrated data lake and data warehouse as the central data platform, unified governance is established, enabling efficient management of data and AI assets with Unity Catalog. Analysts leverage Databricks SQL to run queries and create impactful visualizations in Power BI, leveraging comprehensive customer insights. Databricks Workflows have also improved production workflow management, reducing bottlenecks. Loan application processing time has been reduced by 62%, and ML is used for personalized investment recommendations and new banking product offerings. The lakehouse commitment has trimmed operational costs by THB30M annually while doubling new digital loan acquisitions through personalized experiences. Siam Commercial Bank can now provide truly frictionless banking to its customers. As SCB reimagined what the customer journey could look like, they realized the benefits of the lakehouse could deliver on their goals to provide customers with data-driven, real-time products that elevate their financial well-being.
In the fiercely competitive world of sports, organizations leave no stone unturned in their quest for victory. In Major League Baseball (MLB), data has emerged as the ultimate game-changer, helping uncover insights that can boost player performance and fan experience. Behind the scenes, the Texas Rangers data team has emerged as MVPs (most valuable players), by utilizing the Databricks Lakehouse as their enterprise data platform to help gain a competitive advantage. By moving from a multi-cloud data warehouse to Databricks, they have achieved greater flexibility, agility, and scalability across the organization. Data engineering has seen 7X more velocity in producing new data pipelines, access to data has increased with 3X more self-serve analysts generating reports via Databricks SQL, and streaming data is now used for ML-driven predictive insights that help players up their game. And all data is managed securely through Unity Catalog, with role-based access and data lineage. And the results aren’t only on the field as they’ve seen a 4X cost-effectiveness basis. The same TCO has resulted in over four times as much data consumed and made available, including complex sources such as biomechanics and ball tracking. Coupled with the 10X velocity in communication of KPIs to players and stakeholders, the Texas Rangers have really hit a home run with Databricks.
We will announce each category's award winners 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.