In this special edition of the Futurum Tech Podcast – Interview Series, host Daniel Newman welcomes Jason Mann, Vice President of IoT at SAS. Daniel and Jason discussed SAS’s response to COVID-19 and the impact IoT and AIoT have had on businesses all over the world.
Analytics to Fight COVID-19
It’s almost cliche to say that we are living in an unprecedented time, but it’s still the case. SAS, like all companies, had to quickly reprioritize and pivot within the last month. The first priority obviously was the health and safety of employees and families. SAS quickly moved to equip employees with what they needed to work from home. The second priority for SAS was community-based. On the company homepage, you can find dashboards available for free that help track the spread and trends of the virus.
They also created specific resources that are focused more on the healthcare sector. SAS is working with several hospitals to help with the SIR epidemic models which help with the projections of the advancement of disease within populations. The output for these models is critical in order to project staffing and equipment needs.
The final priority is helping partners and customers with business continuity. What can SAS do to help other companies continue their business as close to usual as possible. Jason shared a great example of this effort has been seen in supply chains and food chains. SAS is working with companies to help maintain a level of consistency of delivery from the farm to the grocery store. SAS has moved quickly to use their analytic software to make decisions or help other customers and companies make decisions. They’re able to take information and create an action item. The companies that do this successfully during this time will inevitably come out stronger in the end.
Breaking Down the AIoT
In the last few years SAS has spent a lot of time and money focusing on the Artificial Intelligence of Things or AIoT. Jason described it as a convergence of AI around IoT principles, many of which have been around but are seeing renewed value in the expansion of compute. It’s an expansion of the power of analytics and a convergence of data that customers can use to make decisions in real time to solve issues faster.
Companies and Industries Benefiting from AIoT
Industries like healthcare, manufacturing and transportation are already seeing the benefits of AIoT. Jason shared a particular example of a rail company that has been able to track not just the locomotive performance and maintenance initiatives but also the operator performance and how all of those things interact and impact each other. The company is also able to use data and evaluate in real time the safety of the track, overall station performance, and potential maintenance issues. Using AIoT the company has expanded their compute capability and has improved performance and the bottom line.
AI provides the opportunity to streamline, to determine correlations, to get more intelligent as neural networks or deep learning is able to identify patterns, trends, and then create automation and activities. It’s about removing the human from the equation to make decisions faster.
SAS has also partnered with Honeywell to pair this AIoT technology with drone video surveillance to help farmers track vegetation. Farmers can take this data and then make decisions about their crops optimizing fertilizing, watering, or eradicating efforts.
AIoT and Future Technologies
There are a lot of potential use cases for AIoT and other technologies. We could easily take the surveillance and data technology and use it to determine if social distancing is happening in some areas.
Jason also discussed the potential around virtual and digital twins and virtual reality. Companies are realizing that we don’t necessarily need to be in person or in a physical location to conduct business. So as more digital or virtual twins are created for existing technologies, how will the AIoT help them operate more efficiently?
More than 220 US public companies are seeing their supply chain impacted by a virtual version of product, process, or system. When you can manufacture a product in a digital or virtual environment, you’re keeping costs low. We are seeing this with automobiles. We’re seeing it with windmills. We’re seeing it being done in a lot of heavy industrial manufacturing as well as consumer products and health care. We’re seeing all kinds of applications. This is really powerful and this is IoT data. This is AI and this is analytics at their finest.
It’s likely that moving forward, many businesses will have continuity plans that include digital twins or virtual environments in case something catastrophic like this ever happens again. This will clearly be our new normal.
If you’d like to learn more about SAS, AIoT, or the future of this technology be sure to check out their website and listen to the full episode. Don’t forget to subscribe to the podcast so you never miss an episode.
Daniel Newman: Welcome to The Futurum Tech Podcast, Interview Series. I am your host today, Daniel Newman, Principal Analyst and Founding Partner at Futurum Research. Excited for this interview series that we have today with SAS’s Jason Mann. Jason. Hey, how are you doing today?
Jason Mann: I’m doing well, Daniel. Great to be talking to you today. Wish it was under a different circumstances.
Daniel Newman: Yeah, it’s an interesting time out there and I’m glad you pointed that out. It is the 7th of April today that we’re recording this show and I typically, I don’t timestamp these shows, but because we are in a little bit of a unique circumstance, I think it’s important that I start out this show talking a little bit about what’s going on in the world. As I said, April 7, 2020.
We’re about three maybe four weeks now into a shelter in place order here in Chicago and we’re right in the middle of this coronavirus, COVID-19 global pandemic that’s sort of changed everything. It’s changed the way we work a little bit and it’s changed the dynamic certainly of our interview podcast.
We’re really excited to have SAS as a partner on this podcast and Jason, we’re really excited to have you on this podcast. But as I originally set out and embarked upon this podcast journey, my intention was to do a podcast to really talk about your business unit, which is IoT and it’s a really important business. It’s a rapidly expanding business. And I imagine even all the data that we’re getting now and SAS, being an analytics company, has a whole bunch of impacts on society right now.
But with COVID, I felt that all these interviews that we’ve been doing over this course of the ongoing pandemic and the ongoing response, it really is important to take a pause because people will be listening to this and they’ll want to… they want to know more than just about products and services of the tech industry, but they want to hear what the tech industry, what companies like SAS is doing. So I’m going to have you talk about that. But before I get into the details, Jason, for everyone out there that hasn’t met you before, they’ve maybe seen you and I, because we’ve done webcast, webinars, videos, podcasts, we’ve been at numerous events and shared stages and conversations. But if they don’t know you, tell everybody a little bit about Jason Mann and his role at SAS.
Jason Mann: Sure. Jason Mann, I lead our IoT division here at SAS. The focus is on the distributed supply chain, the distributed ecosystems that IoT presents from cloud all the way to streaming data. We established a division within SAS to allow speed of response to some of the value propositions we were seeing in the market. The divisions include everyone from R&D, marketing, product management, and then part of the sales function and partner engagement as well. I’ve been at SAS now about 16 years. Prior to that, spent 10 years in manufacturing in the telco industry. Again, happy to be with you here today.
Daniel Newman: Yeah, no, it’s great to have you. And you and I have had enough discussions across the globe that I definitely have seen your manufacturing experience. And with IoT, that’s been a big focus for SAS. We’re going to talk about that a little bit. We’re not just going to talk about IoT, but we’re going to talk about AIoT, which is something that SAS has really been championing. But SAS has also been championing some various efforts, especially using its analytics prowess. And for those of you that aren’t overly familiar with SAS, one of the world’s leading software companies when it comes to analytics, powering analytics, been around for… what is it Jason? How many years now?
Jason Mann: 42 I believe is the current count.
Daniel Newman: 42 years and based in Cary, North Carolina. Beautiful offices, really, really great company, great people. This past week, your Chief Operating Officer, President Oliver Schabenberger, and I said that right. I’ve met him a dozen times and I’m always a little bit just double checking myself. But he wrote a really interesting article. He talked a little bit about what the company’s doing just a couple of weeks back. But SAS has been very busy and the tech industry has been very busy, but SAS has been very busy. And before we jump into the IoT and AIoT stuff, this is where I’d love to hear just a little bit. What is SAS is a response so far been to the COVID-19 outbreak?
Jason Mann: Certainly. The prioritized response was to our employees, employees’ health and safety and that of their families. Obviously, as a global company, our goal was to align to local, state and country directives and in fact most of the company at this point is in some version of a shelter in place requirement. We also have specific company requests that, obviously, default to the local communities, but to drive some consistency in the work engagement across the company and we’re pretty much in place there. And then in fact, you see countries, like China, that are starting to get back to something closer to normal. I don’t know if you would call it normal at this point, but getting back into some of the offices and in some areas.
The second initiative was also community focused, really to understand how the company and our employees could support our local communities and governments. And with that, you’ll notice on our external site, SAS.com, there’s a dashboard available. It’s open to the public to help track with the speed and location and trends of the coronavirus.
We’ve also had some specific resources, those that are focused more on the healthcare sector that are working with several hospitals to help with the SIR epidemic models. If you’re familiar with those types of models that help with the projections of the advancement of disease within populations, we’ve been able to work to help improve the output for those which is critical to informing the hospitals or staff and equipment utilization.
And then with those things kind of under our belt, it really transitions to continuity of business and not just continuity of SAS as business, but how we can support continuity of business for channel partners and customers. And in that we created an incident command system, staffed with a small number of people. I think it’s about seven people that have broad decisioning capabilities across the company. And the real goal of that group is to help customers across, again, all industries to weather the current crisis, recognizing that everyone is pivoting in some form of fashion and being able to help with those new value props. We see it in healthcare where one of the primary requests is optimization of resources, like ventilators or beds, that stays in the news consistently. Within our food chain, supply chain, supporting that, understanding how we can maintain some consistency of delivery from farmers and growers to the shelves. And then finally in the financial sectors where it’s been critical to be able to support some scenario analysis to get some understanding of viability of the institution. A lot of work across all of the team and in all industries.
Daniel Newman: Really encouraging to hear. And I’ve been sort of spreading and sharing the wealth of stories and the wealth of donations. And it’s been a really beautiful thing. And each company has varying resources in which they’re able to contribute. Some have been able to contribute largely financial. We’ve seen the pivot by companies like Dyson and with Elon Musk being able to quickly, it’s amazing to me, turn from manufacturing vacuum cleaners and cars to ventilators. And these contributions are tremendous.
And then companies like what you’re doing at SAS. I mean, a lot of people out there have heard all the momentum behind analytics and big data and the powering of business and consumers and decisioning. It’s probably not as often as it should be shared, some of the critical decisioning that softwares like SAS are helping companies make every day. And by the way have been helping companies make through many pandemics around the world. H1N1, MERS, SARS. SAS was there for all of them and helping, whether it was a COVID type response or just working with your partners to help them build dashboards and move supplies more efficiently. This is really what analytics can do and how analytics can power the world.
And so the ability to mobilize your resources so quickly to be able to build a dashboard, to be able to help the public sector get better understanding, and maybe, I hate to say this, but maybe and hopefully to help some of these leaders in different states, countries, nations to get better models, because as we’ve seen so far, the modeling has been more volatile than the stock market, in terms of what we think is going to happen, how quickly things are going to grow. And it is so important that the right tools are there, but not only the right tools but the right expertise and helping people optimize those tools, which it sounds like is a big part of what SAS is able to offer.
Jason Mann: Absolutely. And help the speed, so obviously there’s data there that transition to information and insight from data, and then perhaps most importantly, then moving that into execution. So what do you do with that visibility and fidelity that you’re getting on either disease spread or availability of equipment is critical in getting the right assets in the right place at the right time.
Daniel Newman: Absolutely. Well, thanks so much for sharing that with me and for everyone out there, definitely check out what they’re doing. We’ll put a link in the show notes to the overall COVID response that Oliver Schabenberger had written so you can check it out more. But it’s an impressive assortment of capabilities and tools that the company invested in, which again at this time it can be money, it can be supplies, it can be tools. All of this is necessary. And by the way, Jason, thank you very to SAS and pass that along from us here at Futurum and for all that you guys are doing.
Jason Mann: Thank you. I’ll do that.
Daniel Newman: Yeah, of course. Thank you. And next time I’m in Cary, which I hope is soon, hopefully is sooner than later, I’ll make sure I can hopefully pass that along myself. I’m getting cabin fever. I’m not going to lie. I don’t even know what it is. Thankfully, it’s the only fever I have right now, but I am dying to get out. I don’t miss traveling 47 weeks a year. I could definitely see going down to 20, 25 weeks. That has changed my perception of the world. But I could definitely use to get out and just see people, break bread, have dinner, wine, and hopefully if you’re listening to this, it’s like December of 2020 and we’re back to some semblance of normalcy and maybe you’re at the gym right now listening to this or you’re in the office back with your coworkers.
But I want to pivot right here. I want to talk about your business. I want to talk about IoT. I want talk about AIoT. The company has put a lot of effort into this. This has been a big focus. I’ve done a little work in the background with you and your team on this. You’ve worked with a number of other analysts and researchers. The topic of AIoT… I’m going to throw you a softball and then I want you to dive a little bit into it, but you guys got really profoundly invested in this. What sort of excited SAS and you and to really put so much effort into this emerging concept of AIoT? And if you don’t mind, just give a little, just a quick run through of what AIoT is for those that maybe haven’t really caught the term yet.
Jason Mann: Sure. It’s the artificial intelligence of things is the acronym breakdown. For us, it really is the convergence of AI and those principles, many of which have been around but are seeing renewed value in the expansion of compute, as we see compute and then compute distributed throughout networks is kind of the big technical change. But for us, it’s bigger than that. It’s a convergence of data, specifically data in motion and decisioning that is supported by these techniques that can be presented closer to the origin of data to help our partners and help our customers respond to real time issues on the ground, again, at the right time and right place. For us, it really is an expansion of the value of analytics as we move it farther and farther into these distributed networks.
Daniel Newman: It caught my attention right away. First of all, it’s trendy and it has got a nice ring to it. And it is really the convergence of two things, two topics that have been extremely interesting to companies, specifically in the industrial space, but really in all businesses. I mean you can certainly see these applications starting in manufacturing and becoming wildly important to helping retailers, consumers. It’s got a lot of flow that could end up becoming not only big for your business unit, but also for SAS and what you’re doing for analytics as a whole.
With that in mind, I’d love to talk about some of the applications. In advance, you sort of prepared me and you gave me some of the… a list of some of the different applications you guys are thinking about and I’ve been thinking about these as well. Take me through some of the sort of applications that SAS is paying attention to as it pertains to AIoT.
Jason Mann: Sure. Some of the best examples, as you might imagine, some of the early movers were in those industries and had the most experience around sensor data and so the industrial manufacturers, transportation, to some extent some of the healthcare fields and now you’re starting to see the application of these AI principles in neuro networks as it gets applied to computer vision. Again, this nexus of initiatives that start to cross that add additional value.
In transportation, we see some specific areas. Rail is a great example to where you’re starting to evaluate not just the locomotive performance and maintenance initiatives. Those things have been around for a while, but now you’re starting to correlate the operator performance and how the operator performance impacts the locomotive performance. Then you start to expand that, and this is another area where we’re seeing quite a lot of adoption is where just the volume of data or the speed at which things are occurring are outside human capability. And this is where you’re really starting to see excellent applications of computer vision, so things like track evaluation for safety or overall station performance, where in real time you can actually evaluate the safety of the track, the spacing of the ballast, the field in between as an example.
And we have some great partners, a great partner that we’re working with there to help bridge the gap between a domain and deployment into the locomotive and then the analysis that needs to occur, either in the distributed environments, oftentimes these trains aren’t connected or bringing it back to evaluate a broader fleet assessment of potential issues.
Expand that, the computer vision also into manufacturing, the real benefit is in those areas where you’re exposed to high volume yield issues in semiconductor manufacturing, anything that you’re seeing scale. And what we’re seeing is the application of very similar methodologies to things like x-rays that are looking down into the solar connections within boards and chips and being able to make adjustments where necessary before you’ve made 10,000 defective units. The idea of being able to correct manufacturing parameters, stop a line if needed, stop an asset in the case of locomotives are all quite important.
And then expanding into one other example in in healthcare. We’re working in Amsterdam with a hospital that is leveraging computer vision to help with the assessment of a tumor size. Both tumor size and then the change of the tumor over time is highly correlated to the type of response or response assessment. And what we’re finding is that computer vision assisted with analytics is able to get a higher level of accuracy and repeatability many times than the doctors or evaluators that were doing that. So you’re providing a more advanced foundation for the doctors, who then determine what the assessment should be. A really cross industry application. It’s an exciting area to be in at this point.
Daniel Newman: I’ve always felt the two work better together. The challenge with analytics in isolation or IoT data and isolation is that the data volumes tend to be so significant and the ability to analyze it becomes something that has a lot of opportunity for the human condition to break down, meaning the human has to model it correctly, input it, interpret it. AI provides the opportunity to streamline that, to determine correlations, to get more intelligent as neural networks or deep learning is able to identify patterns, trends, and then create automation and activities, right?
You mentioned the rail example where it could identify an anomaly in the physical construction of the track in a certain location and it could actually act upon notification, scheduling of maintenance and in some cases other things where it’s maybe technological, it could be self-healing too, right, where they could identify a flaw in something in terms of connectivity. Say that the wireless connectivity of the locomotive goes down, which is has a impact on vocation analytics and it could actually immediately identify that anomaly, reset the router or the right equipment and/or then finally notify the analyst that would be responsible to sort of try to remote in and fix the situation before finally identifying the local potential person on site that could restore it. So you have a whole path of correction, you improve the uptime and you’ve really paired together that massive data and the power of AI and automation, really extreme automation to make that business run more efficiently and more safely as well.
Jason Mann: I think that’s the key that the last term there. It really is about automated response. The idea of taking the human out of the loop, and as I mentioned earlier, whether it’s changing the parameters of a manufacturing line or we had a great example in the utility space where issues happen within the distribution lines, whether it’s a tree falling on it or something blowing up within the support infrastructure and being able to identify the cause of that, again, is a high level driver on the response and being able to correctly determine the origin will very quickly allow you to assign the correct response and being able to do that in the seconds versus the time that it would take to insert a human is critical to uptime and efficiency and all of those goals that we’re chasing.
Daniel Newman: Yeah. I’d just like to quickly touch one of the notes you’d sent to me, it was a little bit about drones and robots and I think there’s some platitudes that would probably apply directly to all this stuff we talked about in the beginning of the show with COVID, but also more generically speaking in application. Things like drones right now I have a really interesting AIoT application in terms of crowd management.
Jason Mann: Sure, sure. We can get into the creepy factor pretty quickly when we start talking about surveillance. But if you take some applications that had been applied, we’re working with a partner right now in this space, again, in utility, but this is around vegetation identification. A big percentage of their op expend is keeping the lines clear of vegetation encroachment. We partnered with Honeywell to deliver a solution that allows us to use the drone video.
We then take that, assess the species type. From there, we’ve talked about that transition from data to information to execution. They can then take that information and help with optimization of eradication efforts or clearing the lines.
That foundation is then a great quick start when the question is now, hey, can we use similar types of footage from a drone to help validate or verify social distancing for example. I know we’ve all seen footage from the Fort Lauderdale spring breakers on the beach and by all accounts there was no social distancing there. But it’s certainly a technology that is proven and that can work when in fact you need to transition the focus from something like vegetation management to something that is of critical concern for our healthcare.
Daniel Newman: Yeah, those are some great examples, Jason, and for me, I think they’re… like you said, we can quickly get into the creepy factor, but I think that’s always going to be a blurred line from now into the future. I think we realize that in certain circumstances extraordinary measures are going to be required. We are certainly in a time where those limits are going to be tested and if technology can help, it’s certainly a safer for humanity and for society. And it’s a really great application as you said, of where AI and IoT could come together to solve a lot of problems.
Now, you gave me some thoughts about the future of AIoT. You talked a little bit about digital twins and supply chain and you talked about virtual reality, factory of the future. I’d love for you to quickly kind of either run through them or pick one or two of those and just talk a little bit about what you’re thinking about those applications for AIoT.
Jason Mann: I think it’s an interesting premise. What are we going to do in the future? Where are we headed? I think our current condition changes everything. If you think… you mentioned virtual twins, the idea of establishing a virtual replica, whether it be, historically, that was normally a system or a mechanical group, but now the idea of virtual twins is expanding into manufacturing lines or factories or even cities at this point. And being able to have confidence that that is representing a twin, right, is going to be critical as we move forward. And I think the fact that we are socially connected now is going to open a path to many of those avenues where many times they may have been suspect as to whether they represented an actual replication of what we’re seeing on the actual. I do believe we’ll continue to see an expense there. We’ll get better at it as we are able to replicate other systems.
You’re seeing a lot of work right now around synthetic data creation. Things like facial detection and creating human images is something that’s in the realm now. And we can only assume that that technology will expand operationally into factories and other systems. I think we’ll continue to see adoption and application of analytics in those areas-
Daniel Newman: One of the data points you shared was that more than 220 US public companies are basically, they’re seeing their supply chain impacted by a virtual version of product process or system. And so this is something I’ve extensively studied, call it the digital twin or like you said, virtual production, virtual twin. But basically taking certain things you’re doing and creating whether that could be product design, for instance. I had studied closely one of the world’s largest bicycle manufacturers and think about the process of testing, deploying, building, manufacturing, a new bicycle. It could be a 5, $10,000 bicycle. How quickly can you get that in market? How do you make it as light as possible, as sturdy and robust as it can be? Keeping margins in check so that the product is profitable and yet understanding the customer that’s going to buy it and what they’re going to be willing to pay for it.
When you can do all that in a virtual environment, run it through paces and tests and this is… we’re seeing this with automobiles. We’re seeing it with windmills. We’re seeing it being done in a lot of heavy industrial manufacturing as well as consumer products and health care, of course. We’re seeing all kinds of applications. This is really powerful and this is IoT data. This is AI and this is analytics at their finest.
Jason Mann: Absolutely, and I do continue to see some convergence of those areas. And I mentioned the virtual reality, it’s a right now thing that’s certainly relevant in how you go to the grocery store now, right? Most people aren’t traveling and they have to engage through some other online methodology and some of these are supported by virtual reality and being able to have a sense of being in the store and ordering your products. Expand that into any type of retail engagement, and I think today we are training the public that that’s a viable option versus the actual travel and engagement in a brick and mortar. I think you’ll see that change.
Daniel Newman: Yeah. That sits right there on the border of something, I think some people want and some people don’t want, but I think having the option and doing both… I tend to look at a lot of technologies and the roles as more and then or meaning that I don’t think every job will be displaced. I think it’s and. Jobs will be done better.
I think travel won’t be eradicated by virtual reality. But you know what, what if you could actually walk the… you’re going to Bora Bora and you think you want to spend… you want to get one of those little tiki huts and you want to be out in the ocean. Well, why don’t you have a chance to put on the VR goggles and spend 10 minutes there, to decide, do you want to spend $6,000 a day to go on vacation? And obviously, there’s much more practical examples of being able to visit a store, experience a new product, visit, a place that you want to see. You want to go to Rome or you want to go, right, to somewhere you’ve never been. And just have that chance to experience before deciding if you want to immerse yourself.
Daniel Newman: And the last one you sort of touched on, and then I want to shift over to some of SAS’s specific focuses, but you did talk a little bit about the factory of the future as well, and mentioned that in some of the surveys you’ve been doing, you’re finding that companies are rapidly digitalizing their factory operations. I think this is something a lot of people know, but this is another area that you’re really seeing as a scaling up quickly.
Jason Mann: We do. We referenced it in the information we sent over, a 2018 Gartner survey where 41% of the respondents cited knowledge management as a constraint. So having an understanding of when you’re presented with these substantial changes… I mean, you think about the changes in our daily life, how you can respond to that, how you can identify skill sets within your organization and get those applied to the right place at the right time. What are those essential roles and not essential roles? I know most companies that are listening out there today, have gone through the process of identifying essential and nonessential employees, right? Those are the only ones that can come on campus. So how can you do that? And oftentimes these virtual twins or simulations or virtual reality can help with that overall assessment.
I do think it’s going to continue. In our local management meetings, we’ve had discussions about what happens when we get back to business as usual, BAU. And it’s my contention that we won’t. I don’t think it’ll ever be business as usual versus three or four months ago. I think opportunities to engage online, to do things like we’re doing now, will be more commonplace and will be more easily substituted for the travel that you talked about, the field engagement from field resources. There’s so many opportunities to leverage our learnings from this unfortunate event.
Daniel Newman: Yeah, I think we’ve all come to realize that certain things we used to do one way can absolutely be accomplished a different way. It will be then just deciding about the… I always like to say the human condition or behavior. How much do we revert back to naturally? How much is going to change us forever and how gravity is… they talk about the markets with V shape versus a U shaped recovery. I think it’s a little bit of that. I think for certain, when it comes to behavior, it’s going to be a very long U shape, meaning I think certain things will come back. People will travel again. People will go back to offices. I think we’re social beings by and large and we like to be with other social beings.
I’ve said this before, Jason, events online did not need to be done in person for a long time. The technology has been there to do it, but all the meals, the breaking bread, the glasses of wine with friends, the coffee in the morning with a customer one-on-one before the keynote. That’s the stuff that people really miss. It’s not necessarily the actual content, it’s all the kind of happenings in between. I mean you and I have built a good rapport professionally doing events like this, Jason, but I think we to an extent built a professional or even a friendship out of the meals and the things that we did more socially at events. And I think that’s how we become close to our customers. That’s when we have the chance to really build those depths. You’ve heard those old adages about people buy from people they like. It’s a little harder to like people when you only see them on video or on the telephone than it is when you really get a chance to get to know.
But I again, I think this is an and. I think companies will scale back tremendously and I think it will take a number of quarters, if not years, before we will ever get back to the level of events and travel if at all, if we ever do that we did.
Jason Mann: I agree. I think that’s the case. I one other item that I wanted to mention that I think will change substantially is through constant cost cutting and the goal for efficient use of company dollars. You saw the focus on what if and scenario analysis kind of fall by the wayside. The idea of if something catastrophic occurs we will figure out how to deal with it there and having something that has now been presented that’s larger and more unexpected than anything in our lives, I think you will see that get elevated again, the idea of these continuity of business plans put in place and a lot of that will be based on some of the virtual capabilities and digital twins that we talked about earlier. I think after some of this cools down, we’ll revisit what it means to have a insight built on data for the next thing that might occur, right.
Daniel Newman: Yeah, I’m with you. I think the winner of this, of COVID-19… I shared a tweet, Jason, that was sort of like who in your organization or what drove the digital transformation? It was like CTO, CIO, CEO and COVID-19 and the joke was kind of like COVID… so many companies are going to expedite their transformational efforts because of this. They’ve learned they weren’t ready for a fully digital type of business and this was almost that marquee moment to see. This is what a fully digital transformation transformed business needs to be able to operate in a world that’s completely digital.
I want to ask one last question about kind of AIoT and SAS, and by the way, Jason, thank you and thank you SAS for all the time here. Really enjoyed it. Please definitely check out SAS.com/AIoT to learn more. Talk a little bit about what you see as AIoT and SAS and kind of where that’s going to go in the future for the company.
Jason Mann: I think the focus will be continued in engagement in the distributed ecosystems. We’ve invested in extensive amount of effort and time into plugging into these distributed environments across the industries that we’ve talked to earlier, manufacture, industrial manufacturing, transportation, healthcare, retail, oil and gas. Our investment still lies within those target industries. Our pivot in the last 18 months has been around less the technical focus on assurances and validation that it could happen to a refinement of how that’s applied against specific use cases and outcomes. Even use cases is getting to be a diluted in the market. We’re really focused on generating outcomes for those levers that really move bottom line efficiency and cost or top line revenue growth for our customers. And I expect that to continue to be the driver for the foreseeable future.
Daniel Newman: Yeah, that’s tremendous. It’s been really interesting. I’ve been tracking what you’re doing with… that SAS has been doing with AIoT for a while. Caught my attention first just based upon how catchy AIoT really was with AI and IoT being such great emerging trends, but it made a lot of sense. It was a well-positioned campaign. Very interesting.
Some great resources for everyone out there to check out. As I said just a few minutes ago, SAS.com/AIoT. If you’re out on the move or out on a jog, please check out the show notes when you get a chance and click on that and learn more about it. Definitely appreciate you, Jason, you joining me here on the show.
Jason Mann: Thanks for having me.
Daniel Newman: Yeah, thank you SAS for being part of The Futurum Tech Podcast Interview Series. Thanks for all that SAS is doing around this, a COVID-19 response. Really appreciate it. For everyone out there, of course, thank you for tuning in and listening to this episode of The Futurum Tech Podcast. We hope you hit that subscribe button.
We hope you stick with us. Lots more interviews, lots more great content on our weekly shows. Keep tuning in, keep hearing about all that’s big and important in tech. For now, for the show, for Daniel Newman and Futurum Research, I’m out of here. We will see you soon.
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Image Credit: SAS
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