In this episode, Denis sits down with Jim Gavigan, founder of Industrial Insight, to discuss Datatude, a framework for measuring your organization's readiness to leverage industrial data effectively.

About the Guest:

Jim Gavigan brings 30 years of experience in industrial manufacturing, from vibration analysis and control systems to working at Rockwell Automation and OSIsoft. He founded Industrial Insight in 2016 to help companies maximize the value of their time series data.

Key Topics:

• What is Datatude and why it matters
• The five dimensions: Data, Technology, People, Priorities, and Culture
• Why companies struggle to build sophisticated analytics on poor foundations
• The importance of starting small with concrete, achievable projects
• Common pitfalls: prioritizing technology over people and process
• How to scale data initiatives across multiple plants
• Building the right team and culture for data success

Key Takeaway: Stop trying to implement advanced AI and analytics on crappy data. Focus on getting the basics right first — clean data, proper documentation, the right people, and a culture that supports data-driven decisions.

Connect with Jim:

• LinkedIn: https://www.linkedin.com/in/jimgavigan/
• Website: https://www.industrialinsightinc.com/

In this episode of the 'Watts In Your Data' podcast, Denis discusses advancements in AI agent technology in the energy and utilities industry with Serena, the lead data architect at Bluedigit, the IT subsidiary of Italgas, Europe’s first gas distributor. Serena details their initiatives at Italgas particularly focusing on their AI-driven IT operations.

The conversation delves into their journey since 2017, leveraging AI to ease workload, reduce ticket resolution times, and improve data quality. Key points include the integration of Databricks for centralizing data, the creation of an AI Factory combining IT and HR departments, and the deployment of multiple AI agents to automate IT operations, manage data, and resolve support tickets.

Serena emphasizes the importance of human feedback in improving AI agents, observability for effective resource management, and future plans for extending automation in cyber-security and cloud infrastructure. The discussion concludes with a call for empathy towards users adapting to AI and the potential for future innovations.

In this episode of the Industrial Data Quality Podcast, I talk with with John Walmsley of Aluminate Technologies, about what AI actually does in heavy industry today, cutting through the hype to explore real applications and challenges.

John brings experience from semiconductors to medical devices to AI in heavy industry. The conversation covers three levels of industrial AI: continuous monitoring, multi-sensor analysis, and autonomous optimization. Using aluminum industry examples, we explore why AI projects get stuck in pilot phase and what it takes to scale solutions enterprise-wide.

Notable Quotes

Key Learnings

1. Multi-sensor approach works: Single-sensor solutions stay stuck in pilots; combining multiple data streams creates valuable insights worth scaling.
2. Infrastructure over algorithms: Enterprise deployment needs robust, maintainable data architecture, not just clever code.
3. Products beat projects: Successful AI needs ongoing support and evolution, not one-time engineering solutions.
4. New pressures create opportunities: CO2 regulations and grid stabilization markets are driving fresh AI adoption in heavy industry.
5. Start with problems, not technology: Identify significant operational challenges first, then find appropriate AI solutions.

Reach out to John Walmsley on LinkedIn.