Episode 267’s Sponsor: Silicon Labs

Silicon Labs, a leader in secure, intelligent wireless technology has launched their 2023 Tech Talk schedule. This year’s Tech Talks include a dedicated technology series for Matter, Wi-Fi, Bluetooth, and LPWAN in order to help you build the development skills needed to deliver cutting edge IoT products. Join Silicon Labs experts, industry leaders for these one-hour, live virtual trainings created for developers by developers. Accelerate your device development today by registering at silabs.com.

What is data-driven decision-making? And how can we get better at it? Neil Sahota, AI Advisor to the UN, joins Ryan Chacon on the IoT For All Podcast to discuss data-driven decision-making with AI and IoT. They explore the role of AIoT in data-driven decision-making, why data-driven decision-making is important, challenges of adopting data-driven decision-making, and what we’ll see from AI in 2023.

About Neil

Neil Sahota is an IBM Master Inventor, United Nations Artificial Intelligence Advisor, author of the best-seller “Own the AI Revolution” and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI. Neil’s work experience spans multiple industries including legal services, healthcare, life sciences, retail, travel and transportation, energy and utilities, automotive, telecommunications, media/communication, and government. Moreover, he is one of the few people selected for IBM’s Corporate Service Corps leadership program that pairs leaders with NGOs to perform community-driven economic development projects. For his assignment, Neil lived and worked in Ningbo, China where he partnered with Chinese corporate CEOs to create a leadership development program.

Interested in connecting with Neil? Reach out on LinkedIn!

About United Nations

The United Nations is an international organization founded in 1945. Currently made up of 193 member states, the UN and its work are guided by the purposes and principles contained in its founding charter. The UN has evolved over the years to keep pace with a rapidly changing world. But one thing has stayed the same: it remains the one place on Earth where all the world’s nations can gather together, discuss common problems, and find shared solutions that benefit all of humanity.

AI for Good is a year-round digital platform of the United Nations where AI innovators and problem owners learn, discuss, and connect to identify practical AI solutions to advance the UN SDGs. AI for Good aims to bring forward artificial intelligence research topics that contribute towards solving global problems, in particular through the Sustainable Development Goals. AI for Good came out of the AI for Good Global Summit 2020 which had been moved online in 2020 due to the COVID-19 Pandemic. Since moving online, AI for Good developed into three main programme streams: Learn, Build, and Connect. AI for Good also helps organize ITU’s Global Standards Symposium.

Key Questions and Topics from this Episode:

(01:09) Introduction to Neil and his work with the UN

(02:35) Why is data-driven decision-making so important?

(04:10) What has slowed migration to data-driven decision-making?

(06:26) How do we improve our ability to make data-driven decisions?

(09:45) Role of AI and IoT in data-driven decision-making

(13:15) Can AI be useful without data?

(14:37) Real world conditions that impact AIoT data

(16:10) Challenges of adopting data-driven decision-making

(20:15) AI in 2023

(25:58) Learn more and follow up


– [Ryan] Hello everyone and welcome to another episode of the IoT For All podcast, I’m Ryan Chacon. And on today’s episode, we are going to talk about why data-driven decision making is so important, and how you can improve your ability to make data-driven decisions. With me today will be Neil Sahota, the artificial intelligence advisor to the United Nations. Very exciting conversation, lots of good stuff here. Prior jumping into it, we’d appreciate if you would subscribe to our channel if you’ve not done so already. Give this video a thumbs up and hit that bell icon, so you get the latest episodes as soon as they’re out. All right, before we get into it, we have a quick word from our sponsor. Silicon Labs, a leader in secure, intelligent wireless technology has launched their 2023 Tech Talk schedule. This year’s Tech Talks include dedicated technology series for Matter, Wi-Fi, Bluetooth, and LPWAN in order to help you build the development skills needed to deliver cutting-edge IoT products. Join Silicon Labs experts, industry leaders for these one hour, live virtual trainings created for developers by developers. Accelerate your device development today by registering at silabs.com. That’s the letter s, the letter i, l-a-b-s, .com. Welcome, Neil to the IoT For All podcast. Thanks for being here this week.

– [Neil] Hey Ryan, thanks for having me on, man. I’m stoked to be here.

– [Ryan] Yeah, it’s great to have you. For our audience’s sake, let’s kick this off with a quick introduction about yourself, background, experience, kinda what you do.

– [Neil] To put it simply, I’m a guy that likes to solve big complex problems and doing that through the use of emerging technology. So it’s taken me on the path of helping on things like climate change, some of the UN SDG initiatives. Helping big companies, and well, just trying to make the world a better place.

– [Ryan] Tell me about kinda the involvement you have with the UN. I know when we were first speaking and talking to kind of your team, that was a very interesting thing that stuck out to me. I know there’s some involvement there on the AI side as well, but just talk about the work you do in that capacity with the UN.

– [Neil] So I serve as the United Nations AI Advisor. So I helped them actually create the AI for Good initiative, which is using AI and some other emerging technology to actually try and make the sustainable goals a reality. So there’s 17 SDGs, as they call it, like end hunger, zero poverty, I won’t bore you with all 17, that the member nations wanna try and make a reality by 2030.

– [Ryan] Fantastic, that’s exciting stuff.

– [Neil] It is, it’s probably the most meaningful, impactful work I’ve ever done. And I’m really grateful for the opportunity to be part of that journey.

– [Ryan] Yeah, fantastic. I know we have a lot of things we want to dive into today, so let’s go ahead and get into it. At a high level, I wondered if you could talk about data-driven decision making, we talk about that as it relates to IoT a lot, but we never kind of, we don’t really refer to it as data-driven decision making. And I thought that was something that would be important to kind of discuss in a higher level detail, about why that’s so important for organizations and different initiatives to be able to, or I guess why it’s so important to be able to make decisions based on data. But can you kind of elaborate on, from your perspective, why data-driven decision making is so important for organizations, groups, alliances, you name it?

– [Neil] I mean the most straightforward reason is it reduces the bias factor, right? We’re used to, you know, experience all these things. I’m not discounting that, but historically we’ve gone from the gut and we’ve done that even in the face of ignoring data, what the data’s actually trying to tell us. And we remember the times it worked out, we kind of discounted the times that our hunch was wrong. And I think we’ve made a lot of faux pas, I guess I’ll call them, over the decades or centuries doing that. But now that we live in an age where we can collect, store a lot of data, we have all these powerful tools to help us analyze and draw insight, it seems like it’s a new untapped reservoir that companies and people are just slowly migrating to now, ’cause it’s so different than what we did before.

– [Ryan] And how would you kind of talk about the differences there? Kind of what we did before versus where we are now. And kind of what’s contributed to maybe that migration being slower in some places and obviously faster in others?

– [Neil] Let me share a story from my past. I was working with a large airline carrier group. They were trying to integrate all their operations together. And six months into the whole project, the travel expenses were about six times higher than they had estimated. And you know, for airlines, they have thin margins. They’re always watching the dollar, so to speak. And rather than look at the data and say, “Okay, where are the expenses really coming from?” The executive from one of the airlines who’s overseeing this is like, “Oh, you know, it’s all the employees kinda living it up because, you know, they’re either upset about their salaries and pension cuts, you have all these consultants.” And so they, as a result, they instituted these draconian travel policy things, like you have to have receipt for everything, you know? They had a fixed amount of how much you can reimburse for food. They’re not gonna reimburse you for lunch anymore. And okay, so, what ended up happening was the expenses started going up.

– [Ryan] Okay.

– [Neil] Right? So it didn’t actually fix the problem, it made it worse.

– [Ryan] Wow.

– [Neil] People were upset, they were trying to reach their limits now. They weren’t actually spending all that money. Ultimately seeing that, finally somebody said, “Okay, let’s get rid of that guy and figure out what’s really going on.” Looked at the data. The major contributor for the high expenses was that they were asking people to do these last minute, international trips. Like, “Oh my God, there’s gonna be a meeting in London tomorrow. You need to be there, go catch this flight.” You know?

– [Ryan] Right, right.

– [Neil] Last minute, international ticket are expensive. That was the primary reason. Once they identified that, like, “Okay, wait a second. Do people have to be there? They have to be in person.” That resolved the problem, but because the initial thing was to go by the gut, they actually exasperated the problem.

– [Ryan] Okay, interesting.

– [Neil] I think that’s the challenge we see, right?

– [Ryan] Yeah, for sure.

– [Neil] An airlines veteran of like 25 years going like, “This is what I’ve always seen. That’s probably the root cause.”

– [Ryan] Right, right, right. So let me ask you then, if I’m a company listening to this, and I obviously understand the value of data-driven decision making, but have not completely implemented enough solutions to help our company do that well, how can companies be thinking about improving their ability to make data-driven decisions? And I guess talk a little bit to that or even at a high level, just how we as a society can improve our ability to do that.

– [Neil] I think it’s two key things on that front, Ryan. The first is you gotta develop that culture. And particularly not just, okay, let’s be data-driven, but let’s put the trust into the data.

– [Ryan] Okay.

– [Neil] Sometimes the quote unquote hard facts are probably better sources than our conjecture. And that’s tough, ’cause it diminishes our role in the universe to a degree. That we’re not as special as we might think. The second is to really build out the infrastructure to make sure we have good, robust data. I remember working with, you know, a major railroad company. And they had trouble tracking where the rail cars were, they weren’t optimized, you know, stuff was breaking down. And, you know, we rolled this whole initiative, saying like, “We can put IoT sensors in the cars, on the tracks, all these places.” So we can actually, not just track things better, but we can start getting good data on the actual wear and tear of the equipment. And by having that, it actually enabled them to do preventive maintenance rather than just regular maintenance. They were actually able to take things out of service before they broke down. And for them that was an eye-opener. They never even imagined that something like that was even possible until they actually had the data.

– [Ryan] Right, right, right. Yeah, it’s been a very interesting kind of thing to watch over the last, I’ve been in the industry for six, seven years now, and just seeing how companies have evolved to first understand what these different technologies that exist are, how they can benefit their business, and then what their business can do with more data to make better decisions. And I think just like conceptually, you ask anybody, they’re gonna say, “Yeah, if you have more data, then this is fantastic, we can do something with it.” But there’s a lot of kind of nuances to how to do that well. Not just from the implementation of the solution to collect the right data, but also how to present the data, how to make sure the end user has something that’s usable for them, and things like that. And it’s been very exciting to see, kinda the transformations we’ve gone through as an industry with the technology getting more mature, the costs coming down, and these companies being able to now adopt and see the benefits of a lot of the things that have been kind of being built over the last number of years.

– [Neil] A hundred percent. You actually touch upon something really critical, Ryan, that I think sometimes gets glossed over, is the buy-in factor, right? I’ve experienced this myself and I know lots of people have, that you go, you do the due diligence, you use the data, you present the insights to the conclusions, the recommendations’ all backed, and then people are like, “Mm, no, I don’t believe that.” You know?

– [Ryan] Right, right.

– [Neil] Or, “That’s not what I wanna do.”

– [Ryan] Right, right. Yeah, it’s super interesting. Now let me ask you, ’cause, you know, given your role that you have with the UN on the AI side, and I’m sure you worked on many other AI projects, it’s been a very popular topic. You know, just to start this year off when it comes to AI across industries and how it’s being used. But as it relates to more of the enterprise IoT side, it’s been very interesting to have people kind of talk more about it. But if we kind of break that out one level further and talk about what we’ve been kind of touching on here, which is the ability to make data-driven decisions, how do you see AI and IoT working together to enable that for businesses in a more efficient way?

– [Neil] So the interesting thing here is that AI can’t really do much without IoT.

– [Ryan] Yeah.

– [Neil] You know?

– [Neil] The fuel for AI is data and that’s exactly what IoT is. And I think people forget that, you know, all the information we’re collecting through IoT sensors is really for machine consumption rather than for human consumption, ’cause there’s just a huge volume of data.

– [Ryan] Right.

– [Neil] And we’re tapping into that. Like in the UN, one of the SDGs is smart cities, that’s totally fueled by IoT. The ability to capture like traffic information, you know, the usage within a building, you know, monitoring through like verbal health devices. You need that stream of information to feed the AI system, and then the AI can, you know, monitor and find the slightest deviations, and alert a person to what’s actually going on.

– [Ryan] Yeah, that’s a great.

– [Neil] But.

– [Ryan] Yeah, no, go ahead and finish. Go ahead and finish.

– [Neil] I was gonna apologize to everyone if it’s a little bit gross, but this is the whole concept behind the smart toilet.

– [Ryan] Yeah.

– [Neil] Right. It’s a toilet with IoT sensors in it, and as you produce waste, you know, it’s capturing that data, and AI is, you know, analyzing it. So, it seems innocuous, but, you know, thinking about it, how much data gets collected on a daily basis? As soon as you contract like a virus or as soon as you show early onset of disease, they’re gonna pick up on that, right? They’re gonna get the alert. And we know the earlier we catch something, the more easily we can actually treat it.

– [Ryan] Yeah, for sure. And I think you, you know, you extrapolate that out across different use cases, different industries, different problems that need to be solved, you really start to see how these AI models need to be kind of fed this data. And where that data comes from, like you said, is you need something to collect that data. And that’s what IoT is, whether it’s on the consumer level or the business level, IoT is about putting, you know, these sensors and collecting data off physical assets. And that is something that without IoT you can’t get the data on, which then the models for AI just kind of just sit there. They don’t have the data to kind of work with, it’s a requirement for AI to have that data, and that’s what IoT really does. So it’s been very exciting to kind of see AI grow from just something people talk about to now actually being implemented and being utilized in a lot of these IoT solutions to enable that decision making based on all that data that’s being collected.

– [Neil] A hundred percent. And I think while IoT’s become, is really the foundational structure to make AI successful, now with the growth of AI, we’re seeing in an explosion of the usage of IoT. And the last stat I think I saw was there’s, I think, 327 IoT sensors installed, like every minute around the world now.

– [Ryan] Wow. And so what happens if, so if companies aren’t able to get good data or have data in general, is there still a value to AI with particular things like, I guess, you know, without that data coming into AI, what can AI do? Or how should people be thinking about AI? Or is it really like it’s critical for AI to work the way it’s intended to as long as it has data, which is gonna come from IoT? Like, how are people utilizing AI maybe without sufficient data or enough data?

– [Neil] It’s really hard to have an effective AI solution without enough good, robust data.

– [Ryan] Okay.

– [Neil] It’s unfortunately the reality. You know, AI, it’s not programmed. That’s I think what a lot of people are not used to. It’s a different computing model. We don’t like program AI, AI learns. It’s very much akin to like how human beings learn. You know, it has a corpus, which is the dataset. It has something we call, the training dataset, I should say, what we call the ground truth rules on how to make decisions. And then you feed it lots of things and it tries things. You ask it to try and answer questions or do something, and you have human teachers to teach it. And that’s how it wires this neural network. So without enough data, enough good data, the AI can’t actually learn.

– [Ryan] Yeah, for sure. So outside of the technology itself, are there other conditions, you know, in the real world that we should be thinking about that impact that AI IoT working together and driving that data-decision making for organizations?

– [Neil] Yeah, one of the biggest actual concerns is, are we collecting the right data? So we always talk about like big data, which is like, we’ll just dump everything in. Well, there could be some, we’ve seen there’s been unintended consequences to that, that it introduces some biases that weren’t really relevant, but the AI somehow thinks that they are. And that’s gonna be a big issue, ’cause that can then skew the results. The other thing I think it we’re still trying to learn is that we don’t know how some things really work. And that it’s not about big data, it’s about medium data or the right data. Language is a great example. You wanna teach an AI to be fluent in any language, whether it’s English, French, Japanese. Rule of thumb is, it’s gotta quote unquote hear a hundred million words.

– [Ryan] Wow.

– [Neil] Right? Obviously, you know, repeated. But a human child actually becomes fluent in a language by a hearing 10 million words. So it’s not the volume, it’s some specific type of words or phrases that trigger the cognitive learning. We just don’t know what that is. And when we’re learning our first language, we don’t have the ability to explain how we’re actually learning it. So it’s a weird catch 22.

– [Ryan] For sure, that’s very interesting. I never knew that. So let me ask you, one of my last questions before we wrap up here is around challenges that businesses, or that you’ve seen businesses kind of come across when it comes to the adoption, the implementation of IoT AI solutions. I know a big piece of that is the investment that has to go into this at times, which is working on being solved, and bringing costs down, and being able to do pilots, and things like that. But there’s an infrastructure layer to it. You know, there’s a lot of different pieces that businesses need to have kind of in line for them to feel comfortable adopting, as well as a justified business case that an ROI is gonna be attached to. So from your perspective, what are some of the biggest challenges you’ve seen companies battle with that have maybe prohibited them from adopting these technologies to help them make data-driven decisions?

– [Neil] The infrastructure costs you’re talking about, Ryan, are actually probably the biggest one I’ve seen.

– [Ryan] Okay.

– [Neil] It’s, you know, in some cases it’s like, “Well,” like with the farmers, “this will help me grow more crops. I can improve crop yield like 30%.” You can try and figure out an ROI from that. But a lot of this is also kind of prevention. And so the threat of loss of crops, loss of business, that’s much harder to quantify, right? And particularly when it’s not a guarantee, there’s just some probability, that’s what I see people or organizations really struggle with, because that’s the whole, you know, like preventative maintenance.

– [Ryan] Yeah, sure.

– [Neil] You know? Or upkeep of the business. That’s the more critical aspect, business continuity. And they don’t know really how to quantify that well. And most organizations look at IT infrastructure as a cost center.

– [Ryan] Okay, okay.

– [Neil] And we know how it is, especially with the down global economy, that people look to minimize investments into cost centers or try and cut from cost centers, which unfortunately takes away the underpinnings from being able to do some of these things. “So if you’ve got something working good enough, why should I pour more money into it, you know?” It’s like, “why am I trying to fix something that’s not broken?” Is what I often hear.

– [Ryan] Yeah, how can they see the benefit without the investment which is always tough to battle with. I know one of the things we’ve been advocates for is the more adoption we see in different industries and the more publicity that these success stories and these solutions are getting, the more likely it is that other organizations that face similar problems, or in similar industries, will start to hopefully come around to the adoption of a lot of these solutions, that there’s other people are seeing success with, because they’ll be more tried and true, you know, from a implementation standpoint. And especially in like the industrial space, there’s a lot of benefits and value these technologies will have. But there are people who are very hesitant because to your point, it works. If it’s not broken, why are we gonna work on fixing it? But until they start to see the real value of a lot of these things, that maybe they weren’t able to realize before, the data they can collect, the decisions they can make, the way they can not only hopefully cut down the cost by becoming more efficient, but also potentially drive new business models and revenue streams. Once they start seeing that, we hope that that kind of brings down the fear or the hesitancy that they might have when it comes to adopting solutions across all industries.

– [Neil] No, a hundred percent. And I think, there’s just an inflection point we haven’t reached yet, that when enough companies are actually doing this, it’ll force everyone to do it. Otherwise, you’re at a disadvantage. It’s not so much that it’s a competitive advantage anymore, it’s just you’re behind the curve.

– [Ryan] Right, for sure. Absolutely, I think that’s what it’s kind of becoming now. It maybe wasn’t so much a few years ago, but now it’s becoming a thing where in order to compete, you need to be able to make these decisions based on data using IoT to collect that data, and then AI to run those models which just gets the data, to be even more useful and better for that end user. So this is all great stuff. I really appreciate you kind of taking the time. What are your thoughts, kind of like throughout the rest of the year? I mean AI obviously, has been blowing up beginning of this year with more kinda at least it seems like more things that consumers can interact with, like ChatGPT and things along those lines. But just generally speaking, where do you kind of see AI going this year? Is it something that, I don’t wanna say there’s more hype than really needs to be, ’cause it is a lot of very exciting stuff, but do you see that hype continuing to grow? Do you see new applications from an AI perspective, kind of just rolling out across industries? What are your kind of thoughts just going on through the rest of this year that you’re kinda looking forward to or excited to kind of see develop or potentially grow?

– [Neil] Well, Ryan, the hype will always grow, but I think in terms of actual practicality, I think the big thing we’re gonna see is, you know, with DALL-E 2, ChatGPT, you know, the more adoption of generative AI, which has been around for like six, seven years actually, I think this is gonna be a real precursor to a field in AI we call artificial empathy, where the machines, even though they can’t feel the emotions, can recognize in other people and adjust how they interact with us. So we can kind of talk about that realism factor. I mean imagine ChatGPT can pick up that you’re feeling a little down and tries to cheer you up. I think now we’re at the cusp of seeing that become more the norm. And ’cause I think the technology, at least around artificial empathy, is starting to reach that stage and with the explosion of generative AI, you’re seeing more and more people kind of asking for this now, that they it’s believe possible, that they’re gonna be open to actually experimenting and embracing artificial empathy.

– [Ryan] Yeah, well I watched the movie “Megan” a couple weeks ago and that was a very interesting part of it. Obviously, you know, it’s a movie, but they had built it to be pretty empathetic, to build a connection with the person that it was, you know, building a relationship with from a friendship standpoint. And that was very interesting to see. And as this Megan doll was kind of talking and working with this child, or building a relationship with this child, there was an argument or conversation about like needing to go to school. And the human girl was basically like, “I have this, you know, friend, this doll robot, that it’s teaching me everything.” Like, and it can ask you a question. It’s giving, you know, precise answers. It can be taught or told to give her lessons and things. And it just made me think about like the evolution of a ChatGPT type thing. ‘Cause it’s not just a search engine, right? It’s giving you answers. It’s giving you different kind of responses than you would get if you just kind of need to go search and do your own research for Google. And being able to enable that kind of thing with this, the empathetic side, or the empathy side that you’re talking about, could be really powerful for humans in general. And I think it’s really exciting to kind of follow along and see what happens there.

– [Neil] A hundred percent. I mean we’re moving towards the holy grail of our own personalized AI concierge.

– [Ryan] Yeah, yeah. No, it’s super exciting space. And I think one of the things I’ve mentioned to people when they’ve asked me just like, how do I see like a tool like ChatGPT influencing like the marketing or the content space? I definitely think it’s an enabler and is definitely gonna help. But there’s still a element that obviously need the humans to be involved in from the creative standpoint. And the more applications that are built using these tools, like we’ve seen a lot of people take the OpenAI APIs and build functional tools that have a intended purpose as opposed to just an open-ended, you know, type something into the search box. I think that’s what’s gonna be most interesting, is how people use the functionality, and the dataset, and the APIs, to build tools with an intended purpose for end users to just get better results and do more with. So very exciting space to be involved in. And I’m sure your day-to-day is probably very exciting to have to talk about this, and just kind of stay on the front lines of learning what’s gonna happen.

– [Neil] A hundred percent. I’m glad you made that really excellent point, Ryan, because I know we talk a lot about artificial intelligence, but the real push within the industry is to move towards hybrid intelligence, which is augmenting human capabilities with machine abilities. And that’s exactly what’s happening. Look at the arts world. You got a lot of musicians and sculptors now using these tools to enhance or evolve their craft.

– [Ryan] Yeah, yeah. It’s exactly right. I’ve been reading a lot of content around that, because you’re gonna, you know, you see people, and even companies, publications out there that said, you know, “With AI, we’re going to basically just create AI-created content.” And that kind of fell flat on its face really quick, as far as like people just did not, there’s no authenticity behind it. You know, the human element isn’t there that provides that quality in the content. So, you know, they had to kind of backtrack from that. And then you have obviously the Microsoft versus Google. Microsoft incorporating it into Bing, and then you have Google basically kind of in a position now where if they can find a way to catch AI-created content, they can basically punish sites and take that away from people as a means to create content, ’cause it brings down the quality and so forth. That I’m very interested to kind of see how that whole battle plays out, because it’s a new kind of situation to kind of have to consider with AI-created content, and how AI plays a role in a lot of these spaces that were very human driven. But to your point, I think it’s gonna be more of like a supplement and support to becoming more efficient and doing pieces of their job better. But at the end of the day, there’s still a piece that really I don’t think it can replace.

– [Neil] No, no, not at all. No, everyone out there don’t worry. We’re not trying to replace you with the machine. Yeah, absolutely. Well, Neil, thank you so much, man. I really appreciate your time. From our audience’s standpoint, if, you know, after listening to this, they wanna follow up, touch base with you, kind of engage further on anything we talked about, like what’s the best way for them to engage or kind of stay up to date with everything you have going on?

– [Neil] You can come to my website, which is just my name, neilsahota.com. I’m always pushing out latest, greatest stuff in the industry and some content. And you can always follow me on social media. So I’m very active on there.

– [Ryan] Fantastic, well, really appreciate your time. It’s great conversation. Excited to have you be part of this, and look forward to getting this out to our audience.

– [Neil] Awesome, thanks, Ryan. Thanks for having me on.

Hosted By
IoT For All
IoT For All
IoT For All is creating resources to enable companies of all sizes to leverage IoT. From technical deep-dives, to IoT ecosystem overviews, to evergreen resources, IoT For All is the best place to keep up with what's going on in IoT.
IoT For All is creating resources to enable companies of all sizes to leverage IoT. From technical deep-dives, to IoT ecosystem overviews, to evergreen resources, IoT For All is the best place to keep up with what's going on in IoT.