Watts in Your Data, hosted by Denis Gontcharov, explores how enterprises in energy & utilities leverage Databricks to improve operations. Listeners can expect in-depth technical discussions and interview that break down complex topics automated data quality testing, and advanced analytics into understandable segments, actionable insights, and real-world applications.
More About Me: https://gontcharov.eu
Jim Gavigan defines Datatude, a framework for measuring an organization's readiness to leverage industrial data effectively, in this interview with Denis, emphasizing the importance of building a solid data foundation before implementing advanced analytics.
13 Aug 2025
AI Agents for IT Operations at Italgas with Serena
Denis interviews Serena, Lead Data Architect at Bluedigit, about Italgas' AI-driven IT operations, showcasing how AI agents automate tasks and improve data quality.
2 Jun 2025
Concrete AI Applications in Heavy Industry with John Walmsley
John Walmsley discusses AI applications in heavy industry, emphasizing multi-sensor analysis and robust data architecture to overcome challenges and scale solutions enterprise-wide in this interview with the Industrial Data Quality Podcast.
20 May 2025
Evolution of OT Data Integration with Lonnie Bowling
In this episode of the Industrial Data Podcast, I interview Lonnie Bowling about the evolution of operational technology (OT) data integration from the 1990s to present day (2025). Lonnie, who runs Diemus consulting, shares insights from his extensive career in industrial automation and data integration. The discussion traces how manufacturing and utility companies moved from focusing purely on automation to recognizing the value of operational data, the rise of historian systems like OSIsoft PI, challenges with proprietary formats and data quality, and the industry's current shift toward cloud computing and AI-powered analytics. Both of us acknowledge that while new technologies will transform the landscape, legacy systems will remain an essential part of industrial operations for decades to come.
Key Ideas:
Historians evolved from simple trend-viewing tools within SCADA systems to specialized products like OSIsoft PI that could connect multiple systems.
The industry is experiencing consolidation, with AVEVA (now part of Schneider) acquiring OSIsoft PI and other players to build integrated stacks.
Despite technological advances, legacy systems will remain for decades, especially in control functions.
Problems include proprietary formats, inconsistent naming conventions, connectivity issues with distributed systems, and poor data quality.
Companies are increasingly moving data to cloud platforms like Databricks for better analytics capabilities and computing power, though often without clear objectives.
Reach out to Lonnie Bowling:
Email: lonnie@diemus.com (mailto:lonnie@diemus.com)
Diemus website: https://diemus.com/
6 May 2025
Industrial Time-Series Data Quality and Reliability with Timeseer
In this episode Denis invites Thomas to speak on the topic of industrial time series data quality. we delve into the challenges and importance of ensuring data reliability and observability in industrial settings. Thomas shares his extensive background in industrial time series data and his current work with Timeseer, a platform focused on data quality and observability. The conversation covers various aspects, including the differences between data reliability and observability, the challenges of moving data from the shop floor to the cloud, and the need for a proactive approach to data quality management. Finally, we discuss real-world examples and the technical and organizational components required to address data quality issues.
Notable Quotes
Data quality is not just a technical issue — it's a people and process problem, deeply tied to governance and ownership.
Data management at many companies is still reactive — fixing issues only after models break or KPIs look suspicious. When companies scale their data-driven operations, they need to turn to proactive data management to avoid ending up in firefighting mode.
Data maturity varies by company and by industry — utilities and pharma often lead, some other industries may still view data as a byproduct.
Data should be treated like a product — with quality checks, documentation, and accountability — especially as you scale analytics. This is also true for OT data.
AI needs data quality — ML and AI depend on quality inputs and sensor drift or misconfigured tags can quietly corrupt your entire model output. Interestingly, AI is also a key enabler in scaling data quality.
Moving data to the cloud introduces new risks — missing context, inconsistent pipelines, and ownership confusion.
Reach out to Thomas
Thomas Dhollander LinkedIn (https://www.linkedin.com/in/thomas-dhollander-303b411/?originalSubdomain=be)
Timeseer website (https://www.timeseer.ai/)
E-Mail thomas@timeseer.ai (mailto:thomas@timeseer.ai)
18 Apr 2025
My Six Years of Working in Data
This podcast episode was originally recorded as a video on my YouTube channel. In this episode I give a chronological overview of the first six years of my career in data. I do not have a background in programming or computer science, so I hope my store may inspire other people to transition into this field
1 Apr 2025
Introduction
Welcome to my podcast! In this very first episode I introduce the topics of this podcast and explain my background in data.
Who has been a guest on Industrial Data Quality Podcast
Names identified in recent episode analyses. Showing up to five guests.
Jim Gavigan
Serena
John Walmsley
Reach and audience
Public platform figures. Ratings count people who left a rating, not total listeners.
Spotify
5.0 / 5
3 ratings
Podcast Authority Score: 10 / 100
A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.
Quality
7
Social presence
0
YouTube
0
Engagement
32
Host of Industrial Data Quality Podcast?
Claim your podcast to manage its listing and keep your show details accurate.
Pod Engine is an independent podcast discovery and analytics service and is not affiliated with or endorsed by this podcast. Artwork and show content belong to their owners. Full legal notice.
Explore this show Podcast research with Pod Engine
