Sam Smith of Stellar One Consulting invited me to talk about my background and why I started Industrial Insight. I appreciate his invitation. I talk about challenges in manufacturing, where the future is going if changes aren't made, etc. https://www.stellaroneconsulting.com/blog/podcast-manufacturing-intelligence-turning-data-into-dollars #OperationalIntelligence #ManufacturingIntelligence #IndustrialInternetofThings

The hype train would have you believe that ML and AI are a "silver bullet" to solve all (or most) of your manufacturing problems. I say "not so fast." Here is part 3 of why. I discuss how preparing the data in different ways can reveal answers to different questions. We also discuss SIMCA for multi-variate analysis. #MachineLearning #Analytics #IndustrialInternetofThings #ManufacturingIntelligence

The hype train would have you believe that ML and AI are a "silver bullet" to solve all (or most) of your manufacturing problems. I say "not so fast." Here is part 2 of why. Ben Still and I discuss data quality and how raw data may not tell you the whole story. #MachineLearning #Analytics #IndustrialInternetofThings #ManufacturingIntelligence

The hype train would have you believe that ML and AI are a "silver bullet" to solve all (or most) of your manufacturing problems. I say "not so fast." Here is part I of why. #MachineLearning #Analytics #IndustrialInternetofThings #ManufacturingIntelligence

This video shows my reflections on 3 years in business, and I actually tell the whole story. From almost not starting Industrial Insight at all, to getting down to an incredibly low dollar figure in my business bank account and almost quitting in 2017, to now where we stand as a healthy, growing company. I would never tell this story if I didn’t think we were past the “startup” phase, and I am hoping that this story inspires at least one person to either begin their dream or

In my last video, I showed how you can turn a static daily report into a longer term view of your operations (In case you missed it, you can see it here: https://www.youtube.com/watch?v=w9HJcuBqENI&t=1s). You might have thought from that video that I see no value in understanding where a plant is operationally on a daily basis. That isn’t true. It is important to “know where you are on the map” as well as how you got there. In this video, I show how you can combine the daily

I have actually seen recent posts on PI Square where people wanted a printout of PI Processbook screens so that everyone would be looking at the same data in the morning meetings at their plant. I was also recently given a static .PDF report from one of our customers to convert to a longer term, more insightful report that was actually the inspiration for this post. I also recently had a discussion with a potential customer where they wanted to do real-time or near real-time

**** This is originally posted on Martin Davis's Industry 4.0 blog. I suggest you give him a follow. The original post where you can see how to follow him is here. **** A few weeks ago, Martin Davis and I were invited to present on a webinar hosted by Fluke/Accelix. We talked about the importance of the IIoT and Industry 4.0 as it pertains to maintenance. Martin talked strategy and how all of the pieces fit together and what you should be thinking about. I then showed some ex

In this video, I show how time-series data from the PI System can be sent to a PI View and then analyzed in Microsoft's Power BI tool: #BusinessIntelligence #OperationalIntelligence #PISystem #BigData #Analytics #ManufacturingIntelligence #OSIsoft #EventFrames

Here is the video series that Curt Hertler did several years ago that I reference in the video above: https://www.youtube.com/playlist?list=PLMcG1Hs2JbctevpVLAmrqTW0PL7LGDTjV #BusinessIntelligence #OperationalIntelligence #PISystem #IIoT #AssetFramework #Analytics #ManufacturingIntelligence

I had a blast presenting at PI World 2019 with Rich Ermlick. Here is the presentation: #BusinessIntelligence #OperationalIntelligence #PISystem #IIoT #Analytics #ManufacturingIntelligence #IndustrialInternetofThings

In this video, we take sampled data from PI Datalink and slice it with Tableau and Power BI. We also discuss the concept of combining data sets for investigations with BI tools. We can create "event frame like" investigations using these tools with raw data. Once again, I reference Rick Smith's presentation from the 2018 PI World conference: https://www.osisoft.com/Presentations/Feeding-the-Machine-Learning-Monster/ I also mention some videos on PI Datalink, which you can see

We were asked what tools we use for extracting data out of the PI System. This is part 1 - the decision making process on the right data extraction tool. There will be several videos to follow showing data analysis techniques and the extraction methods used for each. I mentioned Rick Smith's presentation in the video - here is the link: https://www.osisoft.com/Presentations/Feeding-the-Machine-Learning-Monster/ #OperationalIntelligence #Analytics #IIoT #ManufacturingIntellige

Buck Bard is a former colleague of mine at OSIsoft. Buck focuses on leading companies through digital transformation workshops, and is well versed in methods like design thinking and others. We spoke for an hour, but this is about an 8 minute snippet on what we both believe is the single biggest reason for digital transformation, and that is knowledge transfer. Every industry is seeing massive changes in the workforce through retirement and younger workers coming into the fol

In these two videos, I show how PI AF and PI Event Frames can help with multi-variate data models and I also talk in more detail about what I think our future in control rooms in manufacturing will look like. I truly believe that technology like this, married with domain expertise, has the potential to drastically change manufacturing for the better. Analysis tools like SIMCA and SIMCA Online will help us get to value way faster than ever before. I believe that tools that OSI

In these videos, I talk about how PI's Asset Framework and Event Frames help with advanced analytics and machine learning efforts. I also talk about "in the meantime" solutions and how they can help in potential longer range data science (machine learning, multi-variate data analysis, and AI) projects down the road. Here is the 3 minute synopsis: If you want to watch on YouTube: https://youtu.be/VuyTugAe0PQ Here is the full 18 minute discussion: If you want to watch on YouTub

Sometimes, it is the often, simple analytics that can help us a ton. In this video, I show an example. You can see it here If you can get to YouTube and if you like the content, please subscribe and hit the bell, as you will be notified when we post a new video! If your company blocks YouTube, hopefully, it will play below. #Analytics #OperationalIntelligence #IIoT #ManufacturingIntelligence #IndustrialInternetofThings #AssetFramework #OSIsoft #PISystem

So, I saw this tweet below recently and read the article. I got pretty fired up over this article too, not because of what Michael Kanellos said, although I really don't agree with what he said in the tweet either. What I got fired up over was the fact that I believe that there is a LOT of great technology out there right now and the reason it isn't being used is in fact, people. Do I believe that the whole article was hogwash? Absolutely not, as there is some well-intentione

I am featured as the "Monthly PI Geek" on OSIsoft's user forum called "PI Square." For some reason, this strikes me as comical, as there are much smarter and "geekier" people than me on the PI System, although I can hold my own with a lot of it. I take it as an honor and thanks to whoever nominated me to Rick Davin. #Analytics #OSIsoft #PISystem #EventFrames #AssetFramework #ManufacturingIntelligence #OperationalIntelligence #IndustrialInternetofThings #IIoT

I recently saw a former colleague of mine, Tjeerd Zwijnenberg of OSIsoft post this Forbes article "Why Data Lakes are Evil" and there are definitely some points made in the article that I agree with and there are some things that I believe get swept under the rug a bit. Don't be a pack rat: The first point in the article is that you shouldn't just blindly store all data in a data lake. Here is Adam Wray of Basho's comment: "Why is this? Well, think about a pack rat. Sure they