Big data integration techniques for QSRs
Tech stacks behind event data integration for QSRs
Leading fast casual chains integrate data from third-party sources to optimize data quality, analytics, and machine learning strategies. Third party data providers include event APIs, the government, academic sources such as weather and public demographic data, and more.
Data integration is essential to operational efficiency, as data-driven decisions facilitate collaboration with suppliers and business partners such as delivery driver networks.
No one understands this better than industry leading restaurants – such as country-wide franchises that stand to lose millions due to over or under forecasting demand. In order to ingest event data and make forecasting real-world aware, you’ll need to understand how to incorporate external data into your existing tech stack, or what additional tools you might need to add to your tech stack.
How to build a tech stack that unlocks actionable demand insights
A tech stack typically consists of tools used to find data and make it useful, such as a database, programming languages and frameworks, front and back-end tools, and applications connected via APIs. Quick serve restaurants leverage these tools to:
Connect to third party data
Combine third party data with their own
Safely and securely store the data
Build and deploy demand forecasting models
Create interactive reports with visual analytics
Use some form of geo-location
Get the details behind each of these essential steps to big data integration in the free guide, Big data integration for QSRs. The guide covers a variety of different tools and processes that leading QSRs and fast casual restaurants use to find reliable data, make it useful within their own ecosystems, and capture the full picture and context of demand for each of their restaurant locations.
