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Brandon Medina
Chris P Bacon

Astronomer product values:

  • DataOps productivity — eliminating data silos through data centralization and automation.
  • Business impact — helping the client to deliver the right data, to the right team, at the right time.
  • Day-2 operations — adding scalability, diagnostics, automation, and observability.
  • Continuous innovation — allowing the client to stay on top of Apache Airflow advancements.

Herman Miller is a global company that places great importance on design, the environment, community service, and the health and well-being of its customers and employees. Herman Miller puts furniture in a bigger context of social well-being and sustainable living. Its mission is to "leverage the power of design to improve people's lives". 

Data & Analytics is a new organization at Herman Miller, responsible for managing the data needed to drive analytics and insights in the company. In March of 2020 they kicked off planning sessions focused on developing a vision and roadmap for how they want to manage data in the organization. The development of a new modern Data Platform was key to their success. Using tools like Apache Airflow, Astronomer and Snowflake as part of the data platform they intended to provide value to the business and its employees. They have been continuing to build the data environment prioritizing areas of the business where they can have the largest impact.

“My favorite thing about data and analytics is that it allows you to have a direct impact on the business. I love looking at data and making informed decisions that can be tied to any action and once implemented leads to change. It also allows us to help people in the organization and improve and streamline the way they work— from very manual and tedious to automated and scalable,” noted Mark Gergess, VP of Data & Analytics at Herman Miller. 


Herman Miller was facing two main issues:

  1. Disparate data sources — with core information about their customer, products and sales spread across multiple systems, bringing the data together and normalizing it to perform analysis was a very manual and difficult task for team members. Employees would have to run multiple reports, download them to Excel, and then figure out the business logic across the data sets to deliver a global report. Because of the differences in product, time zones, standards across the globe, figuring out the common language for that data involved many meetings with international teams and took a lot of time. 
  2. Data Quality, Alerting & Monitoring — the client’s existing data environments didn’t allow them to be proactive in communicating and resolving data issues. Most of the time the business side of Herman Miller would notify the Data & Analytics team of missing or bad data,  or when they failed to receive an automated report. Investigating, finding and fixing these issues would take hours.




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