Scaling Business Metrics Observability with AI: A Freshly Case Study
Technology Category
- Analytics & Modeling - Real Time Analytics
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Apparel
- Cement
Applicable Functions
- Warehouse & Inventory Management
Use Cases
- Real-Time Location System (RTLS)
- Track & Trace of Assets
About The Customer
Freshly is a prepared meal delivery service that was acquired by Nestle in 2020. The company has seen significant success, with an estimated revenue per employee of $140,408, a remarkable achievement considering the average small business brings in an average of $100,000 in revenue per employee. David Ashirov, VP of Data at Freshly, has been leading the data team for the past three years. He is a senior executive with two decades of experience in data engineering, business intelligence, and marketing, and has developed data-driven products and strategies that enable fast growth, greater efficiency, and the creation of new revenue streams.
The Challenge
When David Ashirov joined Freshly, a prepared meal delivery service, the company lacked systems to measure and evaluate data. The business was largely reliant on human intuition to gauge its performance. This approach was sufficient for a startup, but as the company grew, it became clear that human intuition could not scale. Ashirov's primary challenge was to build a data fabric, a system that would connect data across the company, allowing for easy querying of every bit of data without unnecessary complications. The goal was to create a single source of information for any business question, fostering trust in the data among the company's employees.
The Solution
Ashirov and his data team began by evaluating existing technologies and determining what they needed to build a standardized platform for data collection, warehousing, analysis, and alerting. They used various SaaS products to build this platform, including Anodot Autonomous Business Monitoring for continuous real-time data monitoring. Once the data fabric was in place, the team spent a month building every kind of report that anyone in the company could want. They then interviewed internal stakeholders about their business processes and data needs, mapping the business processes and establishing sensors at key points in the processes to collect information. These sensors served as business metrics, indicating the health of the business or any issues. Finally, they used Anodot to monitor these metrics for abrupt and significant changes, grouping similar metrics together for collective examination.
Operational Impact
Quantitative Benefit
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