Agile Predictive Monitoring - Part Three - With Burak Polat

Welcome to the Trend Detection podcast, powered by Senseye, an industry leader in using AI to drive scalable and sustainable asset performance and reliability. This is a new publication designed to help you go away with ideas on how to achieve maintenance efficiencies.


For this 3-part series, I’m joined by Burak Polat, CEO at Skysens, a company that is focused on creating user centric IoT infrastructures for every enterprise.

In the third and final episode of this series, we discuss how to get the C-Level excited about agile predictive monitoring and the key trends in industrial IoT. You can listen to episode one here and two here.

Please subscribe via your favorite podcast provider (Apple, Google and Spotify) if you'd like to be notified about future episodes and let us know your feedback by leaving us a review. 


Key topics covered (click to jump to the section)

  1. Getting the C-Suite excited about agile predictive monitoring
  2. The latest industrial IoT trends
  3. Smart Industry as a concept
  4. Why data is closed
  5. Listen to the Trend Detection podcast

Niall Sullivan, Senseye: No, that's really clear. And I think we see the similar at Senseye in terms of your point about influencing the C level, yeah, and the people on the floor as well. All those stakeholders actually replicate what we focus on too. I guess my next question would be around the C level, and it's very difficult thing to do, but how do you influence the C level towards... How do you get them thinking and excited about a project around agile predictive monitoring?


Burak Polat, Skysens: Of course, I mean, as a C-Level, they want to see clear results, right? And they want to see a very clear target, very clear outcome of the initiative, but they have multiple things. I mean, they're juggling around with multiple things at the same time, right? They are struggling about increasing their work, because they had some problems maybe a month ago or something, and they want to create more quality product in order to sell it better. They want to decrease their cost, reduce their cost in order to increase their profits.

So they have multiple things on board, but everything comes to the financials, right? I mean, we always think C levels only think with the finances, right? So we are trying to embed ourselves into their financial outcome. We always talk about numbers. If we're talking about predictive maintenance, you need to talk about how much money they are losing.

If some machinery broke down in the middle of the production cycle, right? Or how much money they are currently losing. Every second they are losing because they are not using their energy efficiently. How much money they can be lost or how much negative publicity their company can have if some worker got sick because of bad air quality or because of some work external or something like that.

So we are talking about those engine results when we talk with C level. I mean, you need to be as clear as possible, as simple as possible for C level to get their attention and to have their valuable time for the application to move forward.

Niall Sullivan, Senseye: Yeah. Yeah, it's about having a different mindset depending on the stakeholder you're talking to and really focusing on their day to day concerns, I guess, is simple when you put it like that.

Burak Polat, Skysens: It also differentiate if the boss is around or not. For example, if the company is small, they only have one boss, right? Sometimes. And that guy is the budget holder, right? He or she say yes or no to implement some project. But if the company is really big, you usually don't see who is holding the budget, right? There are multiple owners, different partners. It's a multinational company sometimes.

So in that sense, if you are talking about the money or the outcome, you need to be very specific for those people because they're also selling this idea to a other person, they are not deciding by themselves. So it also can change according to ownership of the company or the scale of the company that you are facing with.

Niall Sullivan, Senseye: Absolutely. Absolutely. As we get to the final part of our conversation, I just want to shift the conversation to more of a broader industry topic, let's say, and just get your view on what the current trends you're seeing in industry IOT. Industry IOT. Industrial IOT. That's the one.


Burak Polat, Skysens: Yeah. I mean, what we see is big players who already started earlier with the bigger budgets with better consultant support, they already come to a point that they have the data, they have the connection, they have the platform, but they are struggling to add smartness on their platform.

So for example, if we go to a very major digital company, a large enterprise, they always ask us, "Okay, we have the data..." I mean, maybe they don't have data by the way, for the specific application, but in general, they can bring some quality data. They already passed those steps.

So they say we have the data, but we are not creating some meaningful results out of it because they cannot reach good analytics engineer, good data science engineers who can understand the process. But at the same time who can understand the data and the data handling and the other software related issues.

So they are at that level. They have the data and they are trying to make it meaningful. Other smaller players, I mean, five, six years ago, they didn't understand what you mean when you say IOT or something, we were at that point.

But right now they do understand, they have the knowledge base, and they can feel that they have to do something because of the competition, because they don't want to lose their competitive edge, they want to produce more quality products, et cetera.

So the awareness is there, but they are also struggling to start small because almost... I mean, not all, but most of the platforms today, they're being designed for the big players. They require a lot of investment to start, they require a lot of expertise in order to implement a good solution. So they are struggling where to start in order to make an efficient industrial digital application.

And also, this global systemic shifts about manufacturing is going away from China and they are try to bring at least the high tech products into Europe or into US again. There is a improving excitement about manufacturing, even though with the current global financial situation with the higher interest rates, with the higher inflation everywhere, I think still the venture money, the venture capital money is targeting more this type of companies, like industrial IOT or industry 4 applications because of this reason, because the manufacturing is becoming more interesting again like 40, 50 years ago. So this is also creating a lot of excitement for the lot of midsize player mostly.

Niall Sullivan, Senseye: And you also mentioned about smart technology and you have a smart industry concept. So I was wondering maybe you could talk a bit more about that and explain exactly what that is.


Burak Polat, Skysens: Yeah. I mean, if you think about smart industry concepts, I mean, a place where everything is defined, I can say. I mean, no chaos involved in daily operations, everything is defined and everything is smart and adaptive to daily operations. I mean, there's no unused technology already implemented inside the system because we always see that somebody initiated some project and they purchased some product, hardware of software. And they're using it, but nobody knows it, right?

So I think this smart industry concept should involve a lot of collaboration between the workers, between the employees, between the stakeholders, a lot of collaboration, it needs to be done. And also, the real time data or historic data should be always readily available for people who wants to use it because once you make the data available, it is very easy to start to do at least some small analytics, right?

I mean, it still might be hard to deploy more in depth analysis, but most of the problems that we face today is just practical problems on the field.

For example, I would imagine you also are having struggled to find the right data for the enterprise itself in order to deploy the Senseye and go forward, right? We are always having the same problem. So I will imagine the smart industry should have a good and practical data hub, which includes every kind of data readily available for the people who wants to do some analytics out of it. And other part is they need to be fast and responsible. I mean, yes, manufacturing is shifting, it is coming home from overseas. I mean, it is probably not going to be as fast as it has before, but it will be still be a dynamic environment.

The orders will be changing, the product models will be changing, product definitions will be changing. So the smart concept needs to be adaptive to different changes. And with the more global fast pace financial instruments, the investing will be easier for the most of the industry and enterprises, so which means that improvements will happen on the ground, on a fast pace.

So systems needs to be adaptive and responsive to these different changes and also for the logistic improvements, for the logistic efficiency. And as a last point, I think the most important one is the healthy and happy environment for the workers. I mean, we always think that manufacturing spaces should be boring, should be dull, right?

For the workers, because it needs to be very well defined, very strict, but it shouldn't be the case because I mean, every day we hear that, we see that. Robots and the automation is taking place of the worker. But the worker itself, he or she should be getting to the point, should be act like a manager, like a decision maker for this automation system, for these robotics.

So their place should be at the controlled amount, controlling all those different robots, all those different operators, all those robots and automation systems. So the worker, the blue collar workers are becoming the operators. I mean, not just doing the physical work. So for them, it needs to be a healthy and happy environment. To create a happy environment, you don't have to provide some snacks or something like that, it doesn't work like that. You need to provide some context, some meaning into their job, right?

They should have the ability to improve it according to their understanding of the business, understanding of the operation. So you need to give them some tools, at least to test some ideas, to test some hypothesis about this process that they are using, they are managing. And let them to choose if they want to improve it or not, if they want to continue with the way things before or not without hindering the overall operation. Yeah. I mean, I think the most important part is to create an environment where people can work happily and productive at the same time.

Niall Sullivan, Senseye: No, absolutely. Absolutely. I think you've covered quite a lot of the final questions, which is nice. It was a nice summary, so that's really good. One thing I wanted to point out that you talked about data being closed. Is there a reason and the importance to make it more available? Is there a reason why it's so closed off at the minute?


Burak Polat, Skysens: I think we are just getting to a point for any industry enterprise to understand that data by itself is kind of a meaningless. It is meaningful if you make it a knowledge or if you create some insights from out of it, right? So when they first start to capture data in '90s or '80s for the large enterprises, they only think about controlling the device. That's why they get the data in order to remove the controlled operation or remove the controlled process. So that's why for them, data is the controlling of the asset. But today it's not. I mean, today it is for creating the insight, right? So that's why their brains, their thinking style evolve around data is by itself, it is very, very, very important. You shouldn't be connecting data everywhere, you shouldn't create some integrations or something like that, because if you do your bread and basket, your money making machine will hurdle.

I mean, we'll have a problem and you will have some catastrophic events. But if you act right, if you act with the prominent, with the clear security strategy, you can do everything with data without hindering your OT security, right? If you put your fire walls right, if you integrate your secure gateways, if you use some security products, only then embedded with your OT networks. I mean, you don't have to worry about anything and you can start working on the data. So I think we are having this transition about thinking about industrial data. And after this transition, it will be much easier for everybody to create some insights out of some industrial data.

And I believe there will be some kind of decentralized industrial data hub will be available because we are using the products which we buy from the markets remarks or something like that, but we don't know how it is being manufactured, right? And we don't have any idea about the inner operation of this manufacturing, which heavily affects environment, which heavily affects our lives. So I think world is going to some kind of integral data approach for every type of industry. But because of the reason that where they are coming from, they still have problems, they still have concerns about opening this data up for the analytics and other applications.

Niall Sullivan, Senseye: Absolutely. Absolutely. I see we're running out of time here. It's been a really good conversation. I just think it would be a great way to end would be if you could just summarize, say, a few key takeaways for the audience on how to enable agile predictive monitoring, and then also how they can find out more about

Burak Polat, Skysens: Of course, of course. I mean, thank you for your time, by the way, it's a wonderful exchange. It has been a wonderful exchange for me too. I mean, when you think about agile predictive monitoring, I think people should think about starting small and adapting all these digitalization efforts into their existing infrastructure, in terms of the physical or in terms of workflows, in terms of digital structures very easily.

Agility is the key word for us. And if they want to start small, if they want to improve their workforce capabilities, or if they want to improve their existing application for predictive maintenance, for energy efficiency or for production quality, they should consider agile predictive monitoring concept. And if they want to find us, they can just visit our website, and we can take from there.

Niall Sullivan, Senseye: Fantastic. Well again, thank you for a really interesting conversation, lots of great insights for our audience to take away. So yeah. So that's all I have to say. Thank you for participating and thank you everyone for listening.

Burak Polat, Skysens: Thank you. Thank you, Niall. Thank you very much.


Subscribe to our Trend Detection podcast

We post weekly episodes on a wide range of different subjects including predictive maintenance, industry 4.0, digital transformation and much more.

Here are all the places you can access/discover the latest episodes of the Trend Detection podcast: