Databricks, the Data and AI company and pioneer of the data lakehouse architecture, announced the Databricks Lakehouse for Retail, the company’s first industry-specific data lakehouse for retailers and consumer goods (CG) customers. With Databricks’ Lakehouse for Retail, data teams are enabled with a centralized data and AI platform that is tailored to help solve the most critical data challenges that retailers, partners, and their suppliers are facing. Early adopters of Databricks’ Lakehouse for Retail include industry-leading customers and partners like Walgreens, Columbia, H&M Group, Reckitt, Restaurant Brands International, 84.51°(a subsidiary of Kroger Co.), Co-Op Food, Gousto, Acosta and more.
“As the retail and healthcare industries continue to undergo transformative change, Walgreens has embraced a modern, collaborative data platform that provides a competitive edge to the business and, most importantly, equips our pharmacists and technicians with timely, accurate patient insights for better healthcare outcomes,” said Luigi Guadagno, Vice President, Pharmacy and HealthCare Platform Technology at Walgreens. “With hundreds of millions of prescriptions processed by Walgreens each year, Databricks’ Lakehouse for Retail allows us to unify all of this data and store it in one place for a full range of analytics and ML workloads. By eliminating complex and costly legacy data silos, we’ve enabled cross-domain collaboration with an intelligent, unified data platform that gives us the flexibility to adapt, scale and better serve our customers and patients.”
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“Databricks has always innovated on behalf of our customers and the vision of lakehouse helps solve many of the challenges retail organizations have told us they’re facing,” said Ali Ghodsi, CEO and Co-Founder at Databricks. “This is an important milestone on our journey to help organizations operate in real-time, deliver more accurate analysis, and leverage all of their customer data to uncover valuable insights. Lakehouse for Retail will empower data-driven collaboration and sharing across businesses and partners in the retail industry.”
Databricks’ Lakehouse for Retail delivers an open, flexible data platform, data collaboration and sharing, and a collection of powerful tools and partners for the retail and consumer goods industries. Designed to jumpstart the analytics process, new Lakehouse for Retail Solution Accelerators offer a blueprint of data analytics and machine learning use cases and best practices to save weeks or months of development time for an organization’s data engineers and data scientists. Popular solution accelerators for Databricks’ Lakehouse for Retail customers include:
- Real-time Streaming Data Ingestion: Power real-time decisions critical to winning in omnichannel retail with point-of-sale, mobile application, inventory and fulfillment data.
- Demand forecasting and time-series forecasting: Generate more accurate forecasts in less time with fine-grained demand forecasting to better predict demand for all items and stores.
- ML-powered recommendation engines: Specific recommendations models for every stage of the buyer journey – including neural network, collaborative filtering, content-based recommendations and more – enable retailers to create a more personalized customer experience.
- Customer Lifetime Value: Examine customer attrition, better predict behaviors of churn, and segment consumers by lifetime and value with a collection of customer analytics accelerators to help improve decisions on product development and personalized promotions.
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