Data in Depth explores the world of advanced analytics, business intelligence, and machine learning within the context of the manufacturing industry. In each episode, we talk with industry leaders and analytics experts to help manufacturers gain a 360-degree view of the shop floor, their business processes, and their customers. We dig into the concepts of descriptive, prescriptive, and predictive analytics to help solve modern manufacturing problems. From MRP to quality control, from field service to customer experience, our conversations are designed to spur innovative, data-driven thinking for those working to build the factories of the future.

Data in Depth
Claim This Podcastby Mountain Point
Podcast Overview
Data in Depth explores the world of advanced analytics, business intelligence, and machine learning within the context of the manufacturing industry. In each episode, we talk with industry leaders and analytics experts to help manufacturers gain a 360-degree view of the shop floor, their business processes, and their customers. We dig into the concepts of descriptive, prescriptive, and predictive analytics to help solve modern manufacturing problems. From MRP to quality control, from field service to customer experience, our conversations are designed to spur innovative, data-driven thinking for those working to build the factories of the future.
Language
🇺🇲
Publishing Since
6/12/2019
Reach the team behind Data in Depth
Verified contact details for this show aren't on file yet — sign up to get notified when they land.
Recent Episodes

November 6, 2024
AI Deep Dive - Driving Revenue Growth in Manufacturing Through Aftermarket Sales and CPQ Solutions
Industry experts examine how manufacturers leverage AI, CPQ tools, and customer portals to optimize aftermarket sales and boost revenue growth, focusing on maximizing customer lifetime value.

October 30, 2024
AI Deep Dive - Unlock Efficiency in Your Engineer-To-Order (ETO) Manufacturing Processes
Industry experts explore how AI addresses engineer-to-order manufacturing obstacles like complex workflows and data silos, improving efficiency and enabling intelligent automation.

June 29, 2020
Demystifying Serverless Machine Learning
Carl Osipov from CounterFactual.AI discusses real-world applications of serverless machine learning and provides strategies for maximizing machine learning investments in this interview.
26 total episodes available
Recent guests on Data in Depth
Guests from recent episodes — sign up to see every guest that has ever appeared on this show.
Carl Osipov
Guest
Ed Kuzemchak
Guest
Bastiane Huang
Guest
Alex Reneman
Guest
Mendy Ezagui
Guest
Lisa Arthur
Guest
Michael Cromheecke
Guest
Zach Boyd
Guest
Tyson Higginbotham
Guest
Nick Humpries
Guest
Clark Richey
Guest
Caroline Hilla
Guest
Deep-dive analytics for Data in Depth
Frequently asked questions
Have a different question and can't find the answer you're looking for? Reach out to our support team by sending us an email and we'll get back to you as soon as we can.
- What is Data in Depth?
- How often does this podcast release new episodes?
This podcast updates daily.
- Where can I listen to this podcast?
This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Yes, this podcast regularly features guests.
Legal Disclaimer
Pod Engine is not affiliated with, endorsed by, or officially connected with any of the podcasts displayed on this platform. We operate independently as a podcast discovery and analytics service.
All podcast artwork, thumbnails, and content displayed on this page are the property of their respective owners and are protected by applicable copyright laws. This includes, but is not limited to, podcast cover art, episode artwork, show descriptions, episode titles, transcripts, audio snippets, and any other content originating from the podcast creators or their licensors.
We display this content under fair use principles and/or implied license for the purpose of podcast discovery, information, and commentary. We make no claim of ownership over any podcast content, artwork, or related materials shown on this platform. All trademarks, service marks, and trade names are the property of their respective owners.
While we strive to ensure all content usage is properly authorized, if you are a rights holder and believe your content is being used inappropriately or without proper authorization, please contact us immediately at hey@podengine.ai for prompt review and appropriate action, which may include content removal or proper attribution.
By accessing and using this platform, you acknowledge and agree to respect all applicable copyright laws and intellectual property rights of content owners. Any unauthorized reproduction, distribution, or commercial use of the content displayed on this platform is strictly prohibited.
