Data and AI company and pioneer of the data lakehouse architecture, Databricks, is introducing the Databricks Lakehouse for Retail, its first industry-specific data lakehouse for retailers and consumer goods (CG) organisations.
With Databricks’ Lakehouse for Retail, data teams are enabled with a centralised data and AI platform tailored to help solve critical data challenges.
Early adopters of Databricks’ Lakehouse for Retail include Walgreens, Columbia, H&M Group, Reckitt, Restaurant Brands International, 84.51° (a subsidiary of Kroger Co.), Co-Op Food, Gousto, Acosta and more. In Australia, early adopters include retailers Coles and retail analytics start-ups Hivery and Lexer.
“Databricks has always innovated on behalf of our customers and the vision of lakehouse helps solve many of the challenges retail organisations have told us they’re facing,” Databricks CEO and co-founder, Ali Ghodsi said.
“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 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. 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.
- Machine learning-powered recommendation engines: Specific recommendations models for every stage of the buyer journey including neural network, collaborative filtering, content-based recommendations and more, to create a more personalised customer experience.
- Customer Lifetime Value: Examine customer attrition, better predict behaviour of churn, and segment consumers by lifetime and value to help improve decisions on product development and personalised promotions.