https://ift.tt/C9rtHgS The annual Databricks Data Team Awards recognize data teams who are harnessing the power of data and AI to deliver ...
The annual Databricks Data Team Awards recognize data teams who are harnessing the power of data and AI to deliver solutions for some of the world’s toughest problems.
Nearly 250 teams were nominated across six categories from all industries, regions, and companies – all with impressive stories about the work they are doing with data and AI. As we lead up to Data and AI Summit, we will be showcasing the finalists in each of the categories over the coming days.
The Data Team Transformation Award honors the data teams who are taking their business to the next level with data-driven transformation, accelerating operations that lead to clear, impactful results.
Meet the five finalists for the Data Team Transformation Award category:
Compass
Compass provides an end-to-end platform for real estate agents to manage and grow their business, from customer relationship management, to marketing and brokerage services. To fuel these innovations, they integrated data and AI into a single cloud platform with Databricks Lakehouse. The Compass data team has securely onboarded 100+ data sources, migrated all workspaces to Unity Catalog, and created a gold layer for key stakeholders to tap into, to gain insights into areas like customer, product, usage, revenue and more — creating a single source of truth, with complete access controls across the company. They now have over 300 monthly active users across the business, with insights being delivered via a wide variety of dashboards: Retention and renewal reports that enable executives to identify trends and best practices; Transaction dashboards for sales managers to review listings, closed deals and financial performance, helping to identify opportunities recognize individuals or provide extra coaching. In addition, the ML team has productionized a “likely-to-sell model” that predicts high-value sellers, leading to millions of dollars in incremental revenue for Compass.
H&R Block
H&R Block is leveraging big data and artificial intelligence to drive tax preparation and financial services and achieve their Block Horizons 2025 long-term growth and transformation strategy. Core to their data-transformation strategy is their Enterprise Data Platform (EDP), built upon the Databricks Lakehouse Platform and utilizing Delta Live Tables. The EDP helped H&R Block evolve from disparate, on-premises, legacy data-warehousing sources to an aggregated, cloud-based, and scalable data-lakehouse solution. With this technology foundation in place, H&R Block is dedicated to embedding data and analytics into every aspect of their business, allowing the company to better know and interact with its customers, streamline the customer experience, introduce data-driven products, innovate with operational models, and automate processes.
Providence
Providence’s vision is health for a better world, and predictive data-driven solutions are a part of achieving that vision. Leveraging an architecture built on top of Azure and Databricks Lakehouse, Providence can now unify a diversity of clinical and operational source data in a single cloud environment for both analytics and machine learning (ML). By parsing and scoring streaming messages from electronic medical records (EMR) and other on-premises systems, they can deliver insights that help hospital workers better manage operations and clinical care. For example, Providence is applying ML at scale to forecast patient census, acuity, and length of stay to optimize staffing levels across its 50+ hospitals. Looking ahead, Providence is building on its streaming capabilities to deploy a Mission Control Center designed to monitor and manage facility activity in real-time to further improve patient care and operations across all of its facilities.
Samsung Electronics
Samsung Electronics strives to delight its customers and provide the best connected experience across platforms. Personalization is a key focus area, where Samsung Electronics leverages data to provide viewers with the most relevant content attuned to their interests. The data team at Samsung Electronics implemented the Databricks Lakehouse Platform to build and transform their modeling environment. Since moving to Databricks to transform their approach in an intelligent and cost-effective way, the results have been highly impactful and improved viewer satisfaction and user experience. They are among the most innovative in the industry, leading the way with new and creative ways to provide a better user experience.
Toyota
Toyota’s mission is to “continuously strive to transform the very nature of movement.” For the company to deliver on that promise, they are moving forward with an aggressive commitment to sustainability–not only by shifting their focus to electrified vehicles, but also changing how those vehicles are manufactured to achieve carbon neutrality by 2035. The data team at Toyota is using Databricks Lakehouse to help power this change, moving away from their legacy on premise data warehouse, to a cloud-based unified platform for data and AI. With the Lakehouse ingesting and standardizing petabytes of data, we are able to use machine learning and advanced analytics to analyze trillions of batch and real-time records to optimize manufacturing processes, predict energy demand, improve the utilization of renewable energy sources, and identify opportunities to further reduce our carbon footprint. With the Lakehouse, Toyota is tackling the challenge of decarbonization head-on, while also solving a range of other problems– from supply chain and revenue forecasting, to quality assurance– to make sure that the company never stops moving while surpassing customers’ expectations.
Check out the award finalists in the other categories and come raise a glass and celebrate these amazing data teams during an award ceremony at the Data and AI Summit on June 29.
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The post Accelerating Business With Data & AI: Announcing the Finalists for the 2022 Databricks Data Team Transformation Award appeared first on Databricks.
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