What is Real Time Retail?
Real-time data processing enabling retailers to respond instantly to customer behaviors, inventory changes, and market conditions via streaming analytics
- Real time retail is the shift from batch based reporting to always available data so retailers can make accurate, timely decisions across inventory, pricing, personalization and fulfillment.
- By processing streaming data from stores, e-commerce sites and supply chains, retailers can track demand, prevent stockouts, react to disruptions and avoid costly mistakes caused by stale information.
- Databricks Lakehouse for Retail supports real time retail by combining Delta, DLT, Autoloader and Photon to ingest large event streams and power low latency analytics for decisions across the retail value chain.
What is real-time data for retail?
- Real-time retail is real-time access to data. Moving from batch-oriented access, analysis and compute will allow data to be “always on,” therefore driving accurate, timely decisions and business intelligence. Real-time use cases, such as demand forecasting, personalization, on-shelf availability, arrival time prediction and order picking and consolidation, provide value to the organization through improved supply chain agility, reduced cost to serve, optimized product availability, and stocking replenishment.
Why is real-time data important for retail?
- We witnessed the move to e-commerce and omni-channel commerce happen over the past 20 years, only to see consumer behaviors fundamentally shift once the COVID-19 pandemic occurred. In just 10 weeks, we witnessed a rate of change that took the previous 10 years to accomplish. As physical stores faced lockdown orders, consumers shifted purchasing to digital channels. Restaurants saw internal dining evaporate, while drive-thru and delivery skyrocketed. With the shift in dollars came other changes: increased fraud, shifting customer expectations, a higher volume of returns, and increased costs to serve customers curb-side service and delivery.
- Compounding consumer-led changes has been the recent volatility in supply chains. The greatest risk to retail and consumer goods for the next several years is volatility.
- Legacy business strategies were instantly outdated - demand forecasting predicted the wrong demand, customer preferences changed, which precipitated stock outages, hence retailer’s margins suffered. As consumers purchased in real-time, businesses had to shift their outdated data warehouse architectures to those that could run and respond in real-time - hence Lakehouse for Retail.
What are the benefits of real-time access to data?
- Rapid data ingestion at scale makes advanced insights available across the value chain in real-time, reducing costs and minimizing errors. Retailers make mistakes when they make decisions without information. These mistakes can manifest in many ways, including some of the following:
- Underestimating demand leads to expedited shipping costs for rush delivery
- Incorrectly predicting how much of an item to produce leads to excess carrying costs, missed sales, and higher waste
- Reacting to breakdowns leads to unplanned outages that disrupt full production cycles
- Order fulfillment with incomplete or inaccurate data leads to additional shipping costs or higher rates of return
- Missing an opportunity to engage a consumer based on current data leads to missed sales opportunities
- Processing data in real-time enables all parts of the value chain to see the status of operations without delay and make better-informed decisions that help avoid these problems.
The agentic AI playbook for the enterprise
What makes Databricks' real-time data capabilities different?
- Databricks’ Lakehouse uses technologies that include Delta, DLT, Autoloader and Photon to enable customers to make data available for real-time decisions.
- Lakehouse for Retail supports the largest data jobs at near real-time intervals. For example, customers are bringing nearly 400 million events per day from transactional log systems at 15-second intervals. Because of the disruption to reporting and analysis that occurs during data processing, most retail customers load data to their data warehouse during a nightly batch. Some companies are even loading data weekly or monthly.
- A Lakehouse event-driven architecture provides a simpler method of ingesting and processing batch and streaming data than legacy approaches, such as lambda architectures. This architecture handles the change data capture and provides ACID compliance to transactions.
- DLT simplifies the creation of data pipelines and automatically builds in lineage to assist with ongoing management.
- The Lakehouse allows for real-time stream ingestion of data and analytics on streaming data. Data warehouses require the extraction, transformation, loading, and additional extraction from the data warehouse to perform any analytics.
- Photon provides record-setting query performance, enabling users to query even the largest of data sets to power real-time decisions in BI tools.
Learn more about Lakehouse for Retail solutions.
Additional resources on real-time retail
- Collaborating Across the Retail Value Chain with Data and AI
- Real-time Point-of-Sale Analytics With a Data Lakehouse
- Improving On-Shelf Availability for Items With AI Out of Stock Modeling
Frequently asked questions
What retail use cases benefit most from real-time data?
Real-time data delivers the greatest value in use cases such as demand forecasting, personalization, on-shelf availability, arrival time prediction, and order picking and consolidation. These use cases improve supply chain agility, reduce cost to serve, optimize product availability, and support stocking replenishment.
How did the COVID-19 pandemic reshape retail's need for real-time data?
The pandemic compressed roughly a decade of change in e-commerce and omni-channel commerce into about 10 weeks, pushing retailers toward real-time data. As physical stores faced lockdowns, purchasing shifted to digital channels, in-restaurant dining evaporated in favor of drive-thru and delivery, and retailers saw increased fraud, shifting customer expectations, higher return volumes, and higher costs to serve curbside and delivery customers.
What mistakes can retailers avoid by processing data in real time?
Processing data in real time helps retailers avoid costly decisions made without current information. Examples include underestimating demand, which leads to expedited shipping costs; incorrectly predicting production needs, which leads to excess carrying costs and waste; reacting to breakdowns only after they disrupt production; fulfilling orders with incomplete or inaccurate data, which raises shipping costs and returns; and missing opportunities to engage consumers based on current data.
Which Databricks technologies power real-time retail data processing?
Databricks' lakehouse relies on Delta, DLT, Auto Loader and Photon to make retail data available for real-time decisions. An event-driven lakehouse architecture handles change data capture and provides ACID-compliant transactions, DLT simplifies pipeline creation with automatic lineage, and Photon delivers the query performance needed to power real-time decisions in BI tools.
How does Databricks' real-time processing scale compare to traditional batch loading?
Lakehouse for Retail supports very large data jobs at near real-time intervals, with some customers bringing in nearly 400 million events per day from transactional log systems at 15-second intervals. This contrasts with legacy approaches, where most retail customers load data into their data warehouse only during a nightly batch, and some load data just weekly or monthly.
Get the latest posts in your inbox
Subscribe to our blog and get the latest posts delivered to your inbox.