Overcoming fragmented data for faster insights
Before adopting the Databricks Data + AI Platform, PAR’s data was spread across disparate systems and formats, making it difficult to analyze consistently at scale. Getting a straight answer to a business question often meant routing it through an analyst and waiting on a backlog.
The challenges went beyond wait times. Historically, PAR faced hurdles across the AI and ML lifecycle, particularly around evaluation, monitoring and operationalizing models. Each of those functions lived in a different tool, which meant stitching together separate systems just to get a model into <a href="https://bitcomme.com/satellite-analytics-for-oil-and-gas-production-and-large-scale-pipeline-monitoring/” title=”Satellite analytics for oil and gas production and large-scale pipeline monitoring”>production and keep it running reliably.
Collaboration suffered as a result. Data science, engineering and analytics teams had to hand off work across environments, which slowed delivery and introduced friction whenever a project moved from one team to the next.
“Before Databricks, data was fragmented across systems and formats, making it difficult to analyze consistently at scale,” said Amine Errazi, Product Manager, AI at PAR Technology. “By centralizing data in Databricks, we were able to unify different data types in one place and unlock new analytics and AI use cases on top of a consistent foundation.”
