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How Does an Information Digital Twin Democratize Industrial Data?

Sean Gregerson, VP of asset management at Aveva, explains Information Digital Twins, data democratization and what it means for industrial use of operations data; and why the gap between collected data and analyzed data remains so large at most companies.

   

Read the full transcript below 


David Greenfield: Welcome to the Automation World Gets Your Questions Answered podcast, where we connect with industry experts to get the answers you need about industrial automation technologies. And you can find even more answers by subscribing to automation world at Subscribe AutomationWorld.com

I'm David Greenfield, Director of Content for Automation World. And the question we'll be answering in this episode is how does an information digital twin democratize industrial data? Now joining me to answer this question is Shawn Gregerson, Vice President of asset management at Aveva? So thanks for joining me today, Sean.

Sean Gregerson: Hey, thanks, David. Really excited to be here with you today.

David Greenfield: You know, to get started, you know, we've all heard about the digital twin by now. But this idea of the information digital twin seems to be you know, a new concept. Can you explain what an information digital twin is, you know, how it's created and what it's used for?

Sean Gregerson: Yeah, absolutely. And today, we still have in our businesses this disconnected environment where we have these siloed functional islands in our business of data connectivity silos and communication silos, and divisional and stakeholder silos across Engineering and Operations and Maintenance and in all the different domains of our businesses and systems are still not always connected. And people still really struggle to find the information they need to make timely informed and inaccurate decisions and assets are still fail and customer commitments are not always met. In we have loss of profits and safety incidents. And this information digital twin is really a way to solve a lot of these problems. In the way you construct the information digital twin is to take all the information that you have about your industrial assets today, the design information, the operations information, the commissioning information, the asset management and financial information, and fuse this together into this information data model. And then link that information data model back to the physical asset itself in 3d and in the context of its connectivity within the plants are more informed, more timely, more accurate decisions can be made. 

David Greenfield: Well, thanks for explaining that, Shawn. That's, that's definitely helpful to understand this new term that I'm sure we're all going to be starting to hear more about as industry continues with the digital transformation process. And speaking of you know, the digital transformation you know, where would you say that you see industry being today overall in its digital transformation process and how are you seeing it benefit the customers that you work with?

Sean Gregerson: Yeah, it's really interesting that we've gone through these different industrial revolutions from industry one Dotto which was the invention of steam power to mechanized production in in use for transportation of goods and then progressed the industry to Dotto which is the invention of electricity and use of it to really create mass production and then industry three Dotto which is all about electronics in it to automate production and then we move to industry, for Dotto, which is all about digitalization and it's really a fusion of technologies that's blurring the lines between the physical and digital in biological spheres. And what I think is really unique about this industry for Dotto is the sheer pace of innovation that has occurred and how quickly technology is being innovated and how quickly that technology is being adopted in in as we now progress to the metaverse it's too early to tell if we're perhaps on the cusp of industry, five Dotto or if this is simply an extension of industry for Dotto and the benefits that companies are getting from digitalization are clear with improved productivity, improved asset reliability and performance, improved operational efficiencies, and resulting in improved profitability of of our customers businesses and improve sustainability of their businesses as well. But with the technologies that we have available today, such as AI in the cloud and augmented reality and virtual reality and extended reality and so on. I really think that we can do so much more as an industry

David Greenfield: As you refer to there with industry. 5.0…I hope we don't get to that and it goes, like you say, as more of an extension of industry 4.0. Seems like we're still trying to get everybody on board with industry 4.0. I'd like to see that happen more ubiquitously, then before we go to industry five point, but I'm not the person who's gonna be making that decision. That's for sure. So, but yeah, speaking of terms, you know, another popular term that, you know, we're seeing used more often is data democratization, you know, what, from your perspective, what does that mean, exactly? And is this a term that you see varying from one technology supplier to another? Or is this meeting largely agreed upon across industry?

Sean Gregerson: Yeah, data is, is really the new currency of our industrial world, and its collection and archive and transformation are the table stakes to play the game. And in there is no shortage of the amount of data that we have available to us today. And in fact, the study has been done, where they determined 50% Of all the industrial data that we have available to us today, has been created in the last two years only. And that's just amazing to think about the amount of information that we have available to us, that 50% of this industrial data has been created in the last two years. And it's expected if we look forward two years from now, that this will still hold true. And there's been 96, it's been estimated 96 zettabytes worth of data has been captured and copied and consumed in the last 12 months alone. And that's zettabytes with 16. Zero, so a lot a lot of data. And data democratization is all about transforming this raw data into actionable and contextualized information information that really enables accurate, optimized and timely decisions, decision making across the business. And it's it's a term that's universally gaining popularity and its use. And we similarly, SIM in a similar way hear about democratization of AI. And, and in a similar way, that's all about taking artificial intelligence and democratizing it in a way that it can be used not by data scientists, but by what you would call the resident data scientist within the organization, which is anybody and everybody through these no code technologies that are being developed so that anyone, and everyone can leverage this advanced technology to drive decision making across the business. And when you look at AI specifically in this in this in this way, it's just amazing when you can deploy these applications without needing data scientists today that anyone in the business can build the data models, they can test and validate the data models, they can scale the technology across the business, and do it in a way that's translating the results to something that's meaningful for them and meaningful for the business. 

David Greenfield:  With all this data being generated by industrial equipment and systems, which is only increasing as manufacturers do a better job of collecting the data, which, as you mentioned, we're into the zettabytes now in a 12 month period. So given that, do you see industrial companies doing a better job of using that data to improve operations now? Or is there still this big disconnect between the act of gathering the data and then actually using it?

Sean Gregerson: Yeah, and I would say that we're getting better at it. But there's, there's still a long way to go from an industry perspective. And, unfortunately, more data alone does not translate to more informed decision making more timely decision making or improved profitability for the business. And there's been a study that Seagate recently did, and they determined that only 32% of all the industrial data that we have available to us today was actually being put to work. And that 68% Of all the data that we have available to us today is going unleveraged. And it's it's reasonable based on how quickly the amount of data that we have available to us is growing in industrial world 50% created in the last two years, that this amount of Unreal leverage data's going to continue to grow in that same way, unless we take some decisive steps to apply the advanced technologies that we have available to us today to make better, more informed more timely decisions.

David Greenfield: Yeah, I think that figure, 32%, really speaks to the gap of what's out there to the upside still to be achieved, but still a long way to go thing that gets back to I think we're not ready for industry 5.0 yet, get on, get more get that percentage higher on industry 4.0 First, but we'll see.

Sean Gregerson: That's right, there's definitely so much more we can do with the technology that we have available to us today. 

David Greenfield: So, you know, based on your experience, you know, working with industrial companies of all different types, you know, why do you think this gap is still so large considering the, you know, industry has been really focused on leveraging data for, you know, at least that, you know, really more than a decade, but you know, hardcore focusing on it, you know, from data collection to Analytics has been a huge issue now for at least a decade. So why do you think this gap is still so big at this point?

Sean Gregerson: Yeah, I think a really key point here is that it can't be technology for technology's sake. We see a lot of industrial operators building things like data lakes in the cloud, to store data, but not first defining how that data is going to be used, who is the user of the data? How can that data be transformed into something that's meaningful for the consumer the information, and I think that's really what's missing is escaping the fundamentals of how we're going to improve the business and jumping forward to a technology solution. And in the same way, we see some industrial operators deploying AI platforms in our business that are touted to solve the any and every AI problem that they may ever see. But they're doing this without first understanding what the problem is that they're trying to solve. And how that translates into the output of that translates into something that's meaningful for the reliability engineer, the performance engineer, whoever the consumer of, of that output of the AI is, so that they can make better and more informed decisions for the business. And I would just say that, you know, another challenge that we see in the industry is this, that we're not very good at sharing of our data. And in another study has been done by Gartner, where they determined that those industrial operators that were sharing their information with their ecosystem of partners and suppliers and OEMs, and customers that they work with, are getting three acts the economic benefit of those that do not and this is all about this connected industrial economy, where you're sharing that data with this, this ecosystem of partners and customers and suppliers that can get benefit from this data, and then translate that into some business benefit, and provide that back to you as well.

David Greenfield: Given your experience with working with these different industrial companies and different verticals, can you share an example of a company that's using the connected industrial economy as you described it, and how its benefited their operations?

Sean Gregerson: Yeah, sure, I think a good example would be Dominion Energy, where they're going through a transformation of their business where they're transforming their generation X and adding more and more renewables to that. And as part of that, they've really developed a new service for their customers, both their residential and industrial customers, were, they're leveraging this connected industrial economy, to be able to so that their customers can understand how their energy is being sourced, and they can understand better their energy consumption patterns, so that they can really make better decisions for their families and their businesses. And the way that, that they did this, is they did this using the Aveva PI System at the edge to collect all of this operational data from these renewable assets. And this PI system takes it and archives this information, high fidelity, second by second, compresses it, and then organizes it through this asset information model, this asset framework that allows all this information to be organized and contextualized in a way that it's consumable by all the applications and all the users of the technology. And it's it's very understandable because the the information has been contextualized at that source level, what this information is and how it can be used to make better decisions across the business. And the next layer of that is really the analytics layer their self service analytics so they can go and build any KPIs or any analytics that can better drive more have informed timely decisions across the business. And then moving up to that next layer of event management where they can go in and create any events that when this condition exists, I want to notify by email or text, this group of people, or maybe I want to auto trigger a work order in the enterprise asset management system, based on that condition to automate and close the loop on that asset management workflow. And then at that top layer, this rich visualization layer that brings all this information and puts it in the context of the consumer of the information and their role within the business. And all of those applications, then leveraging this data model, so that I can build things out from a data model perspective. And all the the calculations associated maybe with that asset class, or the events with that asset class are the visualization screens for that type of asset, automatically update as I add new assets to my system, so incredibly powerful and scalable across the business. And then what they have done is leverage the Aveva Data Hub technology, which takes this information from the ads securely, transports it to the cloud, where the data is further contextualized. And then made available to dominions, customers in a very secure way and on a selective basis. And as part of that, the information’s there. So on a selective basis, they can now make that information available to their ecosystem of partners and suppliers and OEMs, that can then take that information and translate it back into some value proposition for Dominion Energy.

David Greenfield: And, as you mentioned there, that automatic recognition and of devices, on assist on a network and the uploading, the automatic uploading of their data has been a major advance here over the past several years and kind of easing this process of making sure all the data is collected. But then, of course, as we said, we have all the data on the back end that can kind of work through and figure out what's important and what's not and how to use it. 

Sean Gregerson: I think that's a challenge in the industry is, in many cases, we just think that we are going to collect the data and archive it, and then we'll figure out later how to use it. But I really think you need to think about how you're going to contextualize that data and do that at its source, so that the information is usable across the business.

David Greenfield: As you mentioned, considering Dominion’s success, but still not losing sight of this data rich information poor gap that, you know, we've been discussing, what lessons can our audience take away from this to start closing this gap in their operations?

Sean Gregerson: Yeah, I think it's really just focusing on implementing this information. digital twin is the starting point, there's a lot of different definitions of the digital twin process, digital twin engineering, digital twin design, digital twin, reliability, digital twins. So there's all these different definitions, and we won't get into that today. But it's really the information digital twin, I think becomes the foundation of that unlocking the value of all these other capabilities that we have available to us today. And so really starting with that is the infrastructure and foundation is is really a great starting point. And at Aveva, we're really have some really great technologies to be able to do this. And I think we're in a unique position. Because we understand at the deepest level, the engineering, design, commissioning operations and asset management domains, that our customers have to efficiently operate across their business. And so we're able to take all this information from engineering, from operations, from asset management, from a financial data sources, infuse all that together and present it in a way that's, that's meaningful for for our customers.

David Greenfield: So Shawn, to wrap up our discussion here today, the obvious goal of using operations data is improving operating efficiencies, boosting profits, you know, but what about objectives, like sustainability, which may or may not always have a direct monetary benefit, but are of course no less important to a business success. How does this figure in?

Sean Gregerson: Yeah, that's a really thoughtful question. And I think that when we leverage data, as a true asset for our businesses, it's amazing the benefits and gains that can be achieved, and there's perhaps no better or immediate way to improve the sustainability of an industrial opera operator, then, to operate their assets that they have today in a more efficient, more effective way. And so there's just an increase Double opportunity that that can be gained with today's technology with the existing assets that our customers have. And it's a great way to get some quick wins on improving the sustainability of the business and in business, we often have to rationalize our decisions. But in this case, I think we truly can have both we can reduce the carbon intensity of the products that we produce, and at the same time, improve the profitability of our businesses and our businesses and planet can profit together.

David Greenfield: Well, thank you for joining me for this podcast, Sean, and thanks to all of you for listening in. Please keep watching this space for more installments of Automation World Gets Your Questions Answered. And remember that you can find us online at AutomationWorld.com. And subscribe to our print magazine at SubscribeAW.com to stay on top of the latest industrial automation technology insights, trends, and news.

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