Operationalizing Your Warehouse, Streaming Analytics, and Cereal (W/ Arjun Narayan of Materialize and Nathan Bean of General Mills)
Oct 6, 2023
auto_awesome
Arjun Narayan, CEO of Materialize, and Nathan Bean, a data leader at General Mills, discuss operationalizing warehouses, streaming analytics, and the challenges of manufacturing cereal. They cover the maturation of streaming technology, data management challenges, real-time operational decision-making, managing variation in manufacturing, digital twins for manufacturing line automation, operationalizing warehouses, trade-offs between batch and real-time analytics, the evolution of streaming analytics, and query languages in data analytics.
Operationalizing warehouses and leveraging streaming analytics are crucial for enhancing business operations in the manufacturing industry.
Real-time data streams from machines on the plant floor provide crucial insights for optimizing manufacturing processes and ensuring product quality and throughput.
Deep dives
The Importance of Operationalizing Warehouses and Streaming Analytics
In this podcast episode, the guests discuss the significance of operationalizing warehouses and leveraging streaming analytics. They focus on Materialize, a company building and operational warehouse, and General Mills, which utilizes manufacturing analytics and insights. The conversation explores the founding story of Materialize, the maturation of streaming technology, and how companies are utilizing their warehouses to enhance their business operations. The guests emphasize the role of data in ensuring reliable manufacturing and the challenges of managing real-time information in complex manufacturing processes.
The Challenges of Real-Time Decision-Making in Manufacturing
The discussion highlights the complexities of real-time decision-making in manufacturing. Nathan Bean from General Mills explains the challenges of managing various ingredients and materials with specific characteristics, such as different origin regions and attributes. He describes how real-time data streams from machines on the plant floor can provide crucial insights for optimizing manufacturing processes and ensuring product quality and throughput. The guests emphasize the need for agile and contextualized data representations to enable rapid response and operational efficiency in manufacturing.
The Evolution of Data Analytics at General Mills
Nathan Bean shares his experience with data analytics at General Mills over the past two decades. He discusses how the company has adapted to the increasing demand for real-time data and insights, particularly in responding to operational issues and opportunities. The focus has shifted from batch reporting and reconciling historical data to leveraging real-time analytics for rapid decision-making. Nathan emphasizes the importance of reducing implementation complexity and empowering domain experts to leverage SQL and digital representations for solving manufacturing challenges.
The Promise of Materialized: Real-Time SQL for Operational Analytics
Arjun Narayan, CEO of Materialize, explains the vision behind Materialize and its potential to revolutionize operational analytics. He highlights the advantages of using SQL as a modern data stack for real-time analytics and the performance and cost-effectiveness benefits it provides. Arjun discusses how Materialize's cloud-native architecture enables low-latency SQL queries, eliminating the need for separate streaming infrastructure. The aim is to make streaming an implementation detail, allowing more users to leverage the power of real-time data without the complexity of traditional streaming systems.
It turns out data plays a big role in getting cereal manufactured and delivered so you can enjoy your Cheerios reliably for breakfast. We talk with Arjun Narayan, CEO of Materialize, a company building an operational warehouse, and Nathan Bean, a data leader at General Mills responsible for all of the company's manufacturing analytics and insights.
We discuss Materialize’s founding story, how streaming technology has matured, and how exactly companies are leveraging their warehouse to operationalize their business—in this case, at one of the largest consumer product companies in the United States.
For full show notes and to read 6+ years of back issues of the podcast's companion newsletter, head to https://roundup.getdbt.com.
The Analytics Engineering Podcast is sponsored by dbt Labs.
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode