Anodot Case Studies Scaling Business Metrics Observability with AI: A Freshly Case Study

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	Scaling Business Metrics Observability with AI: A Freshly Case Study Anodot

Scaling Business Metrics Observability with AI: A Freshly Case Study

Anodot

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The implementation of a robust data infrastructure and automated data monitoring has significantly improved Freshly's business operations. The company now has a single source of information for any business question, fostering trust in the data among employees. The data team can now easily build any kind of report that anyone in the company could want. The company can also monitor millions of metrics in real time for abrupt and significant changes, allowing for quick response to incidents and minimization of impact to the business. This has helped prevent incidents that could potentially drain millions in revenue. The company can now also better understand its business processes and data needs, enabling it to make more informed decisions and drive growth and efficiency.

Impact #1

The implementation of a robust data infrastructure and automated data monitoring has significantly improved Freshly's business operations. The company now has a single source of information for any business question, fostering trust in the data among employees. The data team can now easily build any kind of report that anyone in the company could want. The company can also monitor millions of metrics in real time for abrupt and significant changes, allowing for quick response to incidents and minimization of impact to the business. This has helped prevent incidents that could potentially drain millions in revenue. The company can now also better understand its business processes and data needs, enabling it to make more informed decisions and drive growth and efficiency.

Benefit #1	The company's estimated revenue per employee increased to $140,408, significantly higher than the average small business revenue per employee of $100,000.
Benefit #2	The data team was able to build every kind of report that anyone in the company could want within a month of establishing the data fabric.
Benefit #3	The company can now monitor millions of metrics in real time for abrupt and significant changes.

Benefit #1

The company's estimated revenue per employee increased to $140,408, significantly higher than the average small business revenue per employee of $100,000.

Benefit #2

The data team was able to build every kind of report that anyone in the company could want within a month of establishing the data fabric.

Benefit #3

The company can now monitor millions of metrics in real time for abrupt and significant changes.

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Overview

Scaling Business Metrics Observability with AI: A Freshly Case Study

Operational Impact

Quantitative Benefit