The Evolution of Smart Data Capture and the Difference it Makes to Consumers and the Enterprise

On this episode of the Futurum Tech Webcast – Interview Series, I am joined by Christian Floerkemeier, CTO, VP Product and Co-founder of Scandit for a conversation about data, the employee experience, customer experience, and how smart data capture can make a difference in the enterprise. 

In our conversation, we discussed the following:

  • An overview of smart data capture
  • How shifts in consumer behaviors and expectations have driven the need to give improved access to data to employees 
  • How smart data capture shifts tedious work to technology to empower frontline workers to add value elsewhere
  • The difference between ‘dumb’ and ‘smart’ data capture
  • Which industries can use smart data capture
  • A look into the future of smart data capture

It was a great conversation on an incredibly important topic, and one you won’t want to miss. Interested in learning more about smart data capture? Be sure to visit this page

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Daniel Newman: Hey, everyone. Welcome back to another episode of the Futurum Tech podcast. I’m Daniel Newman, your host, principal analyst, founding partner at Futurum Research.

Excited for this special interview series edition. I’ve got Christian Floerkemeier. Christian is the co-founder and CTO of Scandit, the company that we’ve been following for a number of years. In fact it goes back several years to a trip that I made to NRF where I first got introduced to the technology, had a really cool introduction, talked about it in a Forbes column that I wrote and since then, we’ve had a number of interactions and I’ve been following the company closely. Excited to have Christian here. Christian, welcome to the show.

Christian Floerkemeier: Great to be here.

Daniel Newman: I don’t know if you remember those in-person shows. Like I said, I think it was ’18 or ’19, I can’t remember for sure, but it was when we used to do that. And now we’re doing it again, but it’s like so quickly our memories have forgotten. There was a three year period of time, two and a half, three year period where we didn’t go anywhere. There were no shows, no events. We just talked like this. It became totally normal. No physical or human interaction, but you’re back out and about, on the move again.

Christian Floerkemeier: Yeah, definitely. I think we just actually had a company event. We had 500 of our employees. We grew significantly in this time. And a large part of our employees have actually never met in that sense. They’re fellow employees so we just had a 500 people event on the Swiss Alps. Everyone was super excited. Everyone’s super happy to see. So you literally see, I think, in general, people are very happy to engage again. And as you know, social interaction is so important, right?

Daniel Newman: Oh my gosh. I think we’ve seen it. I’ve said it was interesting how kind of the minute we got a version of the All clear, which is seemingly where we’re at everywhere except China right now. All the companies went back to physical events. So it’s been this really interesting dichotomy between what we talked about during the pandemic and the new normal and we’re never going to go back to events, and then the minute we were able to get back to events, every company had an event.

I’m juggling, this fall I’ve got over 60 tech companies asking me to attend their events in the next three months. And so I said to them, I said, “What happened to hybrid and remote?” And you said it just right Christian, the world wants to get social, they want to be out and about, they want to be together, they want to break bread, eat dinner, have a drink at the end of the day, get face to face with customers. I still think it’s good we’ve adopted video more pervasively, but we definitely, the new normal’s not as new. It actually feels a lot like the old normal to me.

Christian Floerkemeier: No, no, absolutely agree.

Daniel Newman: So where are you? I just, I’m asking because-

Christian Floerkemeier: Yeah, in Zurich, Switzerland. So we built the company Scandit, it started 12 years ago here in Zurich, being in some way sort of a deep tech hub in Europe, and we built the company. At this point we have offices around the globe, but Zurich is our base and headquarters, and a significant part of our team is in Zurich.

Daniel Newman: Well I’m a huge fan of your watches over there-

Christian Floerkemeier: Yeah.

Daniel Newman: And your skiing’s pretty good too. So appreciate that.

There is a pretty exciting and vibrant tech community in Switzerland, although it doesn’t get talked about a lot. I’m here in Austin, Texas. We’ve got a growing tech community. Of course everybody thinks naturally that Silicon Valley where everything is. But it’s been interesting, as my travels have grown and our business has grown, going in more parts of the world, you see the tech boom and places where you are, in Tel Aviv and just other markets that aren’t… It’s not all California, which is good. We need more of that diversity and it’s really exciting to hear that you’re building something so big in that particular region.

Give me a quick just background. You’re one of the founders of the company. Give me the quick introduction and overview of yourself, your role and even Scandit, because it’s a really fast growing company, but I wouldn’t say it’s quite a household name yet.

Christian Floerkemeier: Yeah, we’re getting there. We’re getting there. Yeah. Hey, so about me personally, I started a long time ago. I got a degree in electrical engineering, then got a PhD in computer science and actually worked on, at the time was called the next generation barcode, which was RFID. So I was involved in an initiative at MIT called the Auto-ID Labs where we built a 900 megahertz radiofrequency identification system, really with that intention of bridging the virtual physical world with transponder technology. At the time I think it was the largest third party funded research effort at MIT, actually. And a lot of folks from Intel to Walmart invested in the technology to move that forward. So that’s my early work in that sense.

Daniel Newman: Was year was that?

Christian Floerkemeier: Say again?

Daniel Newman: What year was that?

Christian Floerkemeier: That’s 2001 to about 2005, 2004. It was a real hype in RFID. Didn’t really play out, but I stayed, I was associate director of the Auto-ID Lab at MIT, really helped building this out, and was sort of the origin of our company, because we saw you needed all these special devices to do data capture, right? Special purpose, real innovations, but it was innovations in the hardware side. And we started seeing these smartphones. They weren’t as smart as the smartphones today, but they were smarter than the, I guess, mobile phones earlier, and they all had a camera. And we were thinking “Wow, hold on.” So we already noticed in the adoption of RFID one of the biggest problems was that these barcodes were just everywhere in industrial processes, right? Yes, in some way a legacy technology from the seventies, but they were just everywhere.

In all these processes you had barcodes, you could capture them. And so that was the origin of our company where we said, “Well hold on, now barcodes are everywhere, smartphones everywhere.” Smartphone started having cameras. “If I have a camera in a smartphone, hold on. This whole data capture play is no longer going to be hardware play, but it’s going to become a software play.” And that’s in some way, is the origin of our company, where we said, in the early days we really all around… A lot of people said, “You can never own a smartphone. With camera-based scanning, you can never get good performance, never.”

Before ML, NAI got a buzzword that every startup needs to have somewhere in their marketing material. For us it was a little different. We only managed to again solve these problems by applying machine learning and advanced computer vision technologies. Sort of that blurry barcode, with a computer, a really sharp one. Out of a low resolution image of a barcode, we computer high resolution image. And so we run a lot of these fairly advanced ML and computer vision algorithms at the edge on the devices, to arrive at the same performance. Or when it comes to swiftness, reliability that you get when you build a custom hardware solution, but you can go and do it on pretty much any smartphone. And that’s really the origins of our company.

Daniel Newman: Yeah, it’s really interesting to see, whether it’s kind of a law of diffusion of innovation or just that tipping point at which a technology goes from being a, “Oh I have this flip phone with a camera on it and it lets me take photos,” and you’ve got a tiny bit of memory. I remember some of the early flip phones, the Nokia types that just had a very low resolution camera and it was like, “Oh, you capture something.” And obviously there was people like you that were starting to imagine what was possible with that. I just thought, “Gosh, these are really terrible photos, but I guess it’s convenient to be able to do it in my pocket.” And in just such a short period of time, it went from really it was nothing more than maybe a, “Oh it’s nice, kind of like having a calculator on your watch,” to it became the camera and it became legitimately a catalyst to billions of dollars of economic value.

I mean just think about social media and even the evolution of where social media started to where it went to, and now it’s the biggest sites in the world, the fastest growth, the Instagrams and the TikToks. Basically it’s all based upon the fact that you can pull out your camera and instantaneously become a creator and get content out there. Of course that’s very much in that sort of consumer world. You, Scandit, you’re a little more focused in the enterprise world, but what you do makes a big difference to consumers and especially in retail and spaces. You guys are really pioneering this concept of smart data capture. So talk about that, talk about what it is and how you define it and what smart data capture encompasses.

Christian Floerkemeier: Yep. I mean you hit of a very valid point. The camera on the smartphone in that sense for us, is really that catalyst of how to change some of those pretty mundane processes that we just have today. When you look at, data capture is a topic that, I think for as long as we had computing in that sense we had data capture, right? Early on you would write things down, you would type things in, and then machine vision symbols got invented with barcodes and you started capturing those, speeding up that transition. But in that sense, we also think about this as slightly dumb data capture, right? Because it’s one barcode at a time, it’s always the same interaction. You scan something, then one item, you look up some inventory record, then maybe you scan the next thing. And it hasn’t really evolved with the capabilities of today’s computing environments, right?

Because when you look at, say we work in retail, there’s a new shipment arriving, receiving, and there’s a hundred packages you need to go and scan. Today data capture is still done in a very, like 30 years ago, one at a time. So you scan 1, 2, 3, 4, 5 packages at package 50s, your colleague disrupts you, you answer something, then you don’t remember exactly where you were. So then you continue with the next one and you get to the end and two are missing and you actually don’t know, did you get it wrong now because you missed some, or… So you’re going to do the whole thing again.

When we sort start talking about smart data capture, it’s almost like a science fiction movie where you go and say, “Well you actually don’t need to go and scan each barcode and aim at it individually. But you could just take your phone, take a step back, hold it the entire scene, and start scanning 20, 30, 40, 50 barcodes at the same time, and superimpose virtual information at the same time into it where it’s telling you, hey, that package actually shouldn’t arrive here, that it should have gone somewhere else.”

So like a true augmented reality experience where, as a worker, you are shifting some of these tedious tasks to technology and on the other hand you’re really providing additional intelligence into that scene. So as a worker I can make better decisions. I see something’s already expired, something shouldn’t even be here. And I see that augmented in the physical world as I’m looking at it with my camera. And this is what, when we talk about smart data capture, we are seeing this as, “Let’s take a step forward from these old days of doing things,” where it’s really just about, “Let’s do it like 10% faster, let’s have 10% more range.” No, let’s change the entire way how we doing some of these processes.

Daniel Newman: Christian, I think if you look at the last couple years, the amount of attention things like supply chain have gotten, right?

Christian Floerkemeier: Yep.

Daniel Newman: I remember we were counting the number of ships off the ports of LA. We were talking about labor and workforce issues, potential labor strikes, a lot of the economic issues we’re dealing with right now, I think most executives and businesses all fundamentally believe technology will be our best way out of the problems that we have. And some of it’s breakthrough, super disruptive innovation. Some of it is what I’d call iterative technologies that simply just make workflows and processes go better. It’s not always going to be like the next iPhone. I mean even Apple hasn’t really done the next iPhone in 20 years since the first iPhone. It’s just a better camera and a faster processor. But it’s always innovative because of all the ecosystem shifts that it creates, what kind of apps you can run, et cetera, cetera.

So we talked a lot about the macro, we talked about supply chain, we talked a little bit about the pandemic, new ways of work. So talk to me a little bit, I made a comment earlier, I said new work is old work, but that’s not always true. In your mind, what is some of the big shifts that are making it more clear than ever that the old ways don’t work?

Christian Floerkemeier: I think when you think about, in certain areas we haven’t gone back to the pre-COVID times in some way. The worker shortage is still out there. When you look at this today, there’s, even right now where we may be entering a different macroeconomic climate, still at this point there is still a significant worker shortage. We’ve seen a gig economy that just really changed significantly how we do work in big parts of our economy, in some way tied to that worker shortage of course. And with that worker shortage and associated with that also, is that people look more at, “Do I actually enjoy doing this job, and do I add value, and do I actually enjoy doing it?” So that goes hand in hand. On one hand it’s like work experience plays a much bigger role, and then on the other hand there’s a shortage of workers, so you want to make sure that you can actually run your business with less manual work, just because you can’t find the workers.

And then I think when you look from a customer perspective, I think in COVID we’ve seen a strong growth of eCommerce and eCommerce services, because we frankly couldn’t go to the stores. But we’ve also seen this, we always talk about omnichannel, but in COVID, we’ve really seen omnichannel explode, in the sense of in-store order fulfillment, curbside pickup. And customers, in that sense, got used to these services.

And we are now seeing that businesses that launched these services or already had them before and then scaled them, are now realizing, “Well I actually need to go and look at efficiency. How can I run these processes better? How can I actually run them more efficiently? How can I run them for better customer experience?” And in-store order is one of these examples. Got launched, got huge traction, but now it’s a cost driver, it’s a customer satisfaction thing. If you don’t have accurate real-time information about what’s actually the inventory information, what’s available in the store, you can’t actually run these processes in an accurate way. So there’s a lot of trickle effects of that boom that we’ve seen, that now ripple through the different processes that are required to actually do these processes efficiently and add a good customer satisfaction.

Daniel Newman: Yeah, I definitely think there’s quite a bit going on. I was thinking about, as you were speaking, everything from the gig economy to labor workforce to what do employees now expect or gig workers expect when they come into the workforce? I think about what is the customer experience that’s expected by every customer going into a store? How do they want to see technology interact with products when you walk in?

I still remember some of my earliest interactive experiences with wine. I don’t remember which one it was, but where you could scan the wine bottle and you get all the information, and there’s one that did augmented labeling. It’s super cool, but I’m saying people want cooler experiences. Gig workers and employees want technology embedded in their work. They don’t want to do mundane tasks. And of course companies at your business and enterprise level need to be building things that remove the monotonous and mundane and repetitive tasks as much as possible just to expedite the work.

How do you get the right products on the shelf, how do you deal with fluctuation? And then of course you said like how do you get full control of your inventory so that you can actually manage a multi-point of sale eCommerce, live pickup in store, live in-store. I mean, gosh, even just going back Christian to when you see that something’s online and you say, “Oh it’s in the store and it’s available and you want it and then you get to the store and it’s not there,” and I mean how frustrating. And after years, big companies with lots of technology still make these mistakes, it still happens and maybe it shouldn’t happen.

So talk a little bit about in your opinion, you mentioned you’re a fast growth, you mentioned you’re at 500-plus employees now. That must mean that smart data capture being at the core of your value proposition must be gaining a lot of adoption. Which industries are you seeing really bite down on the potential of smart data capture, and what are you hearing in terms of the differences it’s making for their workforces?

Christian Floerkemeier: I mean when we are looking at this is, and I think we are seeing our technology really resonate, especially in those verticals where there is traditionally a lot of repetitive and somewhat tedious tasks, a lot of manual processes today. And I think the underlying theme that we’ve seen, is that when you really think about this, we as humans, we always think we’re really good at everything, but we really aren’t. There’s certain things that we just don’t excel at. And there’s other things that we are just incredibly good at.

So if anything that, it’s a very repetitive, tedious task, if we’ll do them, well we’re actually not that good at them. If you need to go on that example I had earlier, you need to go and count a hundred packages. Actually, a computing system, a smart data capture system is much better at this than any human.

But on the other hand, as a human, there’s certain things that no matter how much AI will, at least in the next 5, 10, 20 years, no AI system will beat us at empathy, engagement, flexibility. So if you’re interacting with a customer, that’s something that a human is in a unique position to go and do, to read between the lines what the customer’s asking for, how to best advise him, and to respond to concerns, to be flexible. We will not build engineering systems to do with that, to replace a human, in the short term.

I think what we really see with smart data capture, is to support the workers in those cases where, shift some of those tedious tasks to technology. And when you look in retail that is receiving tasks, that’s shelf management… If you’re in a retail store today, you have processes such as price label checks, honestly that’s the most tedious job on the planet. And when you think about how this works, you scan a barcode on the price label, then you look at what that price is, and then you look at the screen of your mobile device and you say, do they match? And if they match, great and you move on. Otherwise you mark it, you go back, print the ticket and replace it, right?

With smart data capture you step two steps back, take a picture, scan the shelf, matches barcodes and data automatically for you. And as a human now, you do what you’re best at, because you react to it. You see, “Oh, these two, three things are wrong, let me go and fix this, adjust this.” Because before we build robots that, in that sense, replace those shelf labor, that’s a long way away. And again, you’re sort of highlighting the flexibility of the user.

Other examples are say in just age verification. You can train, so when you look in a transportation logistic setting, last mile settings, right? Alcohol delivery today, a lot of states are clamping down on alcohol delivery to minors. As a last mile provider, it’s a significant business risk for you that you lose your license to deliver alcohol. If you can no longer deliver alcohol, you can lose a large part of your market. So you are forced to train all your employees.

But we talked about the gig economy earlier. To go and train gig economy workers, how to do an age check, to really check all the different fields on an ID, to make sure that that actually does get executed, that’s a really big task. Just the training, just the process monitoring. When you now take that to smart data capture, you just leave it to technology. The only thing is the last milers ask for the ID, have the computer vision software inspect the ID and say, “Yep, that person is above 21.” You have a proof point of this, you have compliance. You don’t need to train your employees beyond saying you are going to ask for your ID, otherwise you can’t deliver this package.

So these are just examples where smart data capture. It makes complex processes much, much easier, because they can be encapsulated in technology. They’re not automated. So it’s still the human in the loop, but the tedious task gets shifted to technology.

Daniel Newman: Now I’ve definitely thought a lot about that exact example. With all the sort of custom tailored razors and his and hers personal goods and order subscription. Things like alcohol and tobacco that have very strict governance on delivery. How we haven’t really seen big startups pop out on this yet, because there’s so much compliance, so many challenges from a compliance standpoint. But I would have to imagine an on-demand Grubhub-style alcohol service would probably do amazingly well.

It also got me thinking, as I was listening to you, just you mentioned in transportation less and last mile, but I am starting to see different parts of the world using smart data capture in airports when you’re boarding planes. So instead of a ticket, you know walk up, it scans your face, and that’s the way it actually knows that you’re the person that’s boarding the plane with the ticket. And I mean I imagine we’re going to see a lot more of that to basically validate IDs. And this is both from a safety standpoint, from a validation standpoint, to basically utilize computer vision over old-school barcodes and tickets and things that really… And by the way, like you said, puts a lot of risk in those people, like the people at the gate or the people at the ticket counter that are not professional ID inspectors. So these are things smart capture can help fix.

Christian Floerkemeier: And I think, since you mentioned the airport example, right? There’s the training aspect to it, but there’s also a complexity in TCOS aspect to it. So when you think about the typical gate today, we work very closely with Alaska Airlines, and they sort of looked at their typical boarding gate. There is a keyboard that reads the credit card, there is a screen, there’s a PC. There’s a barcode reader attached to it. So when you think about all these, and every single one of these components can fail.

So think about this from an IT complexity perspective. So you already have four, then you have a printer. So you have four or five crucial components, each of which can fail independently. And you need to go and train your boarding agents how to go and use them. And we’ve seen Alaska Airlines very successfully saying, “Well no, we’re going to do this different. We just have one device, an iPad. We just have one device on which we do everything. We do the passport checks, we do… ” In that case you’re still operating boarding passes, right? So for your example, they’re still boarding passes. They might be digital, but you’re still reading a machine vision symbol. But you have one device. One device. And to our earlier point, it’s a smart device with a built-in camera, and that’s all you have.

So in that sense now, it has many benefits, because every of your employees know how to use an app. I think it’s probably true to say every, right? It’s a very different thing if you need to be trained to use an MS-DOS alert user interface on a OPC where you can maybe do the same process in eight different ways. How to use an app if it’s well done, is very straightforward to most folks these days.

So rather than training someone for 10 weeks how to run all these different processes in their existing environment, it’s now a half a day process. “This is the app, these are the processes, you’re familiar with the device, everything is integrated.” So we are seeing smart data capture really being a crucial part of these digital transformation processes where I’m not just… In that sense, smart data capture is just a component of an overall over the entire work experience, where I’m really looking at every single process I had and I start thinking, “How do I bring this into an app, into a real good example?”

And the example I always list, and maybe that’s my last point on this, is what I always tell our customers saying, “Look, in COVID times, there were businesses out there that scaled. When you start thinking about in-store order fulfillment and e-commerce, that scaled within a couple of weeks according to that press releases, by they added 200, 300,000 gig workers. Which in some way, when you think about this, is crazy. If we were running in business and I’d come to you and I’d say, “Hey, within eight weeks we’re going to bring on 200, 300,000 workers.” You would say, “Are you nuts? That’s crazy,” right?

But they managed. And they managed because there was really good digital experience available, an app that did the training. There was good smart data capture built in. So it actually allowed you to scale. And I think we’ve seen that example, we’ve seen this in airlines, we’ve seen this in retail, we’ve seen this in this in-store order fulfillment. And it just shows the potential of smart data capture in these digital transformation projects.

Daniel Newman: The holy grail is always, “Build once and sell many.” And that’s the holy grail. And I think these apps is, “Build once, sell many, and be able to scale,” unbelievable agility.

So we were coming to the end of our time, Christian. I got maybe a minute or two and I got one more question for you. We’ve sort of started to allude to it, but more in the present tense, where does this go? Give me, from your experience back in the RFID days and building and innovating, to now where we are today, where does smart data capture go in the next couple of years?

Christian Floerkemeier: I think from our perspective, we will always have a user interface, and the phone will be prevalent. I think we’re going to start seeing, eventually, digital eyewear playing a bigger role. But today it’s integrated in one device, the smart data capture and the user experience. We expect that we will have more of a distributed system, in a sense where you might have, we already have that today. We power robots that drive up and down retail aisles to observe what’s going on, to do data capture at scale. But then with that intelligence that you’re deriving there, driving humans to be faster and in that sense, do their job better with the intelligence they’re getting.

So we’re expecting cameras to be more omnipresent, from stationary cameras, robot cameras on drones, and just expect those to be more prevalent. Interpreting our physical world and then feeding us workers to do, make better decisions and in that sense, focus on things that we like doing, as opposed to the tedious things where today we’re still marking things maybe by hand, and recording things by hand.

Daniel Newman: There you have it, Christian. I want to thank you so much for joining me here on the Futurum Tech Podcast. This is a fascinating space to keep an eye on. I think often we become somewhat just apathetic about the technology at our disposal. It’s like, “Oh, I’ve got this super computer.” You go back to that time you were at MIT studying in ’01 to ’05, and doing RFID, and what we literally hold in our hand, these devices, would be considered high-performance super computers back in those days. How far we’ve come and how fast we’ve come.

I love your prediction about augmented. I mean I think the metaverse, buzzword or not, Christian, I think the idea of immersion being… I said this the other day, I said we’re going to, the mistake right now is that people think we’re going to go to the immersion. The immersion is going to come to us, meaning when it’s really going to work. This is all been proven if we want to come full circle to us going back to events. Meaning we want the digital experience, but we want to do it in the physical world. Smart data capture is going to be a certain tool to enable us to get there.

So I want to thank you so much. I really enjoyed having you here. Let’s have you come back sometimes soon. Let’s keep up with the Scandit story. Congratulations on the success, and have a great one.

Christian Floerkemeier: Thank you. Thank you for inviting me. Thank you.

Daniel Newman: All right, thanks everyone for tuning in here. Hit that subscribe button, check out those show notes, learn more about Scandit, what the company is doing. Very interesting company. There’s a reason I’ve been following it and there’s a reason we will at Futurum Research continue to follow what Scandit is doing. But for this episode, I got to tune out, say Bye-bye now. Thanks for being part of the Futurum Tech Podcast community. See you later.

Author Information

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.


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