Kirill Eremenko: This is episode number 311 with Smart Cities Expert Daniel Obodovski.
Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name is Kirill Eremenko, Data Science Coach and Lifestyle Entrepreneur, and each week, we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today, and now, let’s make the complex, simple.
Kirill Eremenko: This episode of the SuperDataScience Podcast is brought to you by our very own Data Science Insider. The Data Science Insider is a weekly newsletter for data scientists, which is designed specifically to help you find out what have been the latest updates and what is the most important news in the space of data science, artificial intelligence, and other technologies. It is completely free, and you can sign up at www.superdatascience.com/dsi.
Kirill Eremenko: And the way this works is that every week there’s plenty of updates and seemingly important information coming out in the world of technology, but at the same time, it is virtually impossible for a single person, on a weekly basis, to go through all of this and find out what is actually really relevant to a career of a data scientist and what is actually very important. And that’s why our team curates the top five updates of the week, puts them into an email, and sends it to you.
Kirill Eremenko: So once you sign up for the Data Science Insider, every single Friday, you will receive this email in your inbox. It doesn’t spam your inbox, it just arrives and has the top five updates with brief descriptions. And that’s what I like the most about it, the descriptions. So you don’t actually even have to read every single article. So our team has already read these articles for you and put the summaries into the email. So you can simply just read the updates in the email and be up to speed in a matter of seconds. And if you like a certain article, you can click on it and read into it further. And so, whether you want great ideas that can be used to boost your next project or you’re just curious about the latest news in technology, the Data Science Insider is perfect for you.
Kirill Eremenko: So once again, you can sign up at www.www.superdatascience.com/dsi. So make sure not to miss this opportunity, and sign up for the Data Science Insider today. That way, you will join the rest of our community and start receiving the most important technology updates relevant to your career already this week.
Kirill Eremenko: Welcome back to the SuperDataScience Podcast, ladies and gentlemen. Super excited to have you back here on the show. I’ve wanted to record an episode about smart cities for a while, and I was looking for a great guest on the topic. Actually, for a couple of years, I’ve been waiting for the right opportunity to record an episode about smart cities, and lo and behold, the opportunity came along. A couple of weeks ago, I met Daniel in San Diego. It was an absolutely random coincidence. Our DataScienceGO team and I, we were working on reviewing how well DataScienceGO went and planning the strategy for next year at Downtown Works, which is a coworking space in San Diego, and Daniel was there. And it just so happens that he’s involved with the management of that space, and we got to chatting, and I was super excited. I knew right away that I had to bring him on the show to share his insights with you.
Kirill Eremenko: So here’s a bit of information about Daniel. Daniel is the co-founder and managing partner of SCALE San Diego, a company which is dedicated to helping San Diego become a smart city. Daniel is also the CEO of The Silent Intelligence, a boutique professional services firm focused on helping digital transformation in enterprises.
Kirill Eremenko: And here are some of the topics you will hear about in today’s episode. We talked about supply chain versus removal chain; hackathons, their advantages and disadvantages; kinds of data you can expect in cities; computer vision for road conditions, a case study that came out of SCALE San Diego; analog problems versus digital solutions and why they don’t fit; what kind of skill set is most important to working on smart cities; cities versus corporations; how smart cities are different to the technology needs of corporations, how they’re similar and how they’re different; a case study about food donors and those who have food insecurities who need food, that’s another case study that came out of SCALE San Diego; the concept of a start up in residence; digital twins for cities; privacy and much, much more.
Kirill Eremenko: This podcast is packed with value. My page for notes, while I was recording this podcast, is completely filled. There is no more space there. So I’m 100% sure that by the end of this session, you will know everything you need to know about smart cities, and you’ll be ready to either dive into this space if you’re up for it or to have very informed conversations on the topic. So without further ado, I bring to you Smart Cities Expert, Daniel Obodovski.
Kirill Eremenko: Welcome back to the SuperDataScience Podcast, ladies and gentlemen, super pumped to have you back here on the show because I’ve got a very special guest calling in from San Diego, Daniel Obodovski. Daniel, welcome to the show. How are you going?
Daniel Obodovski: Thanks Kirill, doing great, how are you?
Kirill Eremenko: I’m doing fantastic. I’m very pumped about this. I watched your presentation on smart cities at the Smart City Summit. So I watched the recording just today and yesterday. Very pumped about the thing that we’ll talk about, but before we do that, how did we meet? I always love diving into these stories, they’re always so random. If you don’t mind sharing with our audience, how did we meet with you?
Daniel Obodovski: I think somebody introduced us, right? Was it Candace who introduced us at Downtown Works in San Diego?
Kirill Eremenko: Yes.
Daniel Obodovski: Yeah, I think Candace walked up to me and said, there’s a Russian guy who is part of the DataScienceGO, who we partnered with in the past. And she loves to do stuff like that. And that’s I think she then walked in, introduced us to each other, and then we started speaking Russian, and then, turned out we lived in the same city for a while, which is Moscow, yeah. So, was excited to learn about your data science projects and the things that you’ve been doing. It’s really, really, cool. I really think you’re doing an amazing job.
Kirill Eremenko: Thank you, thank you, and I can only say absolutely the same thing about you. From the things I’ve heard, everything has been fantastic so far. And what I love about the story is interesting because your company, SCALE SD, was a industry, not industry, community partner for our event, DataScienceGO, and we hadn’t met each other yet. So the event had already passed, and then, a few days later in San Diego we catch up like, “Oh, actually, our companies worked together.” It was such an interesting coincidence.
Daniel Obodovski: Yeah, totally, totally.
Kirill Eremenko: Okay, well, Daniel, I don’t even know where to get started. You’re in the space of smart cities, but you do so many different things. Maybe to get us up to speed, give us a bit about your background. Who is Daniel Obodovski?
Daniel Obodovski: All right, well, currently, I’m the CEO of The Silent Intelligence, which is a professional services consulting company focused on helping industrial companies better understand their data and apply their data towards operational efficiency and new revenue opportunities. And I’m also managing partner of SCALE San Diego. And SCALE stand for Smart Cities Accelerator, Labs and Environment. And here, we’re focused on somewhat similar things to The Silent Intelligence, which is ultimately solving urban challenges using data and technology. A little bit about my background. As you know Kirill, I was born and raised in what used to be the Soviet Union or Russia now. And at the time of the Berlin wall falling down, I moved to Berlin, and lived in Berlin for almost 10 years in total, where I had an import export company doing business with selling cellular technology cell phones to eastern Europe, and that was back in the 90’s.
Daniel Obodovski: Then I got my MBA and worked with various companies, management consulting companies in Germany and with European Telcos, worked with Motorola, and then, in early 2000, 2004 actually, I moved to San Diego to join Qualcomm, and stayed with Qualcomm for almost nine years, mostly launching different connected products, which prompted my interest to the Internet of Things as an emerging trend. So then, with a buddy of mine from San Francisco, we wrote a book about the Internet of Things called, The Silent Intelligence and got so excited that I ended up leaving Qualcomm and just focusing completely on getting that off the ground and helping other companies with their Internet of Things strategy, and that one thing led to another. Kind of realized that the Internet of Things was a means to an end. Have you watched a TV show called Halt and Catch Fire?
Kirill Eremenko: No, I haven’t seen that one.
Daniel Obodovski: It’s a really, really cool one. I highly recommend it. It’s about the dawn of personal computing in America. It’s really, really cool show, and it was actually originally was recommended by Marc Andreessen. That’s how I got hooked and then I watched it and I was telling everybody. So in that show, there’s one of the main characters when he’s researching the personal computer space and it’s like in the late 80s, early 90s, saying, the personal computers are not the thing, they’re the thing to get you to the thing and that’s kind of the same journey I had with the Internet of Things. I looked at the Internet of Things and all these sensors and networks and whatnot and thought this is not the thing. It’s the thing to get you to the thing. And then the question was, what is the thing?
Kirill Eremenko: What is the thing?
Daniel Obodovski: Yes, and is it data? Not really, because the data is only as good as you can do something about it, you can make better decisions. So at the end of the day, it all came down to what problems are we trying to solve. Are there problems around, if it’s in the cities, with making better transportation or a better parking, or maybe solving homelessness, or maybe addressing digitization of records, or whatnot, whatnot, whatnot, or in the industrial space is how achieving the operational efficiency. And ultimately, that leads us to utilizing the data. And that kind of gets us how we’re going to acquire this data, and that’s where something like Internet of Things might come in.
Daniel Obodovski: So kind of what’s almost like a full circle journey starting with hardware and networks, getting to data and now finally getting to the point where we’re looking at solving problems with all those technologies.
Kirill Eremenko: Okay, and so that’s how through acquaintance with Internet of Things you got into smart cities, but why smart cities?
Daniel Obodovski: Smart cities always was kind of an intriguing topic. Back in the 2008, gosh, it’s almost 10 years ago, we did a project with MIT. So we’re launching a connected device. Basically, at that time that was very low cost asset tracker that could track pretty much anything using GPS, and was a very low cost, was very small and had long battery life. And the guys from MIT Senseable City Lab came to us and said, “Hey, we want to track trash.”
Kirill Eremenko: What?
Daniel Obodovski: “Do you want to donate us like a several thousand devices?” And our first reaction was, this is crazy. Who would want to track trash? And then when I had a discussion with them, they said, “Look, do you know how important is understanding a supply chain?” I said, “Oh yeah.” They’re people spend billions of dollars trying to understand where things are at in the supply chain. “What do you think about removal chain? Do you think people would be interested to understand where things go when they’re being thrown away?” And then that kind of triggered all the thinking about the potential about the sustainability and about importance of knowing where things go. And I was at Qualcomm at the time. We ended up not only donating several thousand devices, we also provided the engineering services and support to the MIT Senseable City Lab.
Daniel Obodovski: So that was my foray into the smart cities and actually, the project ended up being amazing. The MIT guys ended up doing in TED talk and they were in BBC and they got National Science Foundation Award for visualizing how trash goes using our technology at the time. And so that kind of was my foray into smart cities. It piqued my interest about how many problems can be addressed by getting access to more data, and then when I was writing the book, The Silent Intelligence, one of the chapters was kind of dedicated to smart cities, and we interviewed Jonathan Reichental, who at the time was the Chief Innovation Officer of city of Palo Alto. We interviewed also Senseable City guys at MIT and interviewed a lot of other people to understand and that kind of triggered more and more interest.
Daniel Obodovski: At some point in time, I was like, I’m living in San Diego, San Diego is home to my kids. My kids were born in San Diego, and might as well look at this as a living lab in our backyard, and something that we can do to help instead of complaining that this doesn’t work, that doesn’t work, we don’t have this, we don’t have that. And that basically led us to building relationships with the city officials, which by the way are great.
Daniel Obodovski: I mean, there’s a great team at the city of San Diego and that also led us to expanding those relationship and bringing in teams to work on different city problems and using technology.
Kirill Eremenko: Okay, gotcha. Very interesting. I love the philosophy of looking at the city you live in as a lab in your backyard where you can try things out and apply some data science. So how long have you been working with the city of San Diego and how is it tracking in the global scheme of things in terms of smart cities around the world?
Daniel Obodovski: Well, so I would say the city of San Diego is pretty advanced. They have some of the largest implementations of street sensors compared to a lot of other cities. And also they have a very innovative team that’s open for experimenting and trying different things, which again, I think we’re lucky that way. So we started in 2016, which is, what? Three years ago on doing, kind of trying with the Smart City Hackathon, and just seeing what was going to happen if we did a big event, and invited people to hack using real data and solving real problems. And it was at the time where the open data portal, now almost every city, at least in the US, has an open data portal where they post all the data that they digitize, open public data.
Daniel Obodovski: The city of San Diego was just kind of getting ready to launch it, and we got access to a lot of data sets that weren’t yet available and they couldn’t be accessed in any other way. And we posted all the data and said okay, if you want to solve some of the problems around the Climate Action Plan, including traffic and mobility, you can use some of these data sets. There was a great experience. We learned a lot of things about hackathons, and basically what’s good about hackathons, and what’s horrible about hackathon.
Kirill Eremenko: What’s good and what’s horrible?
Daniel Obodovski: The good things about hackathons, it’s a great way to generate a lot of excitement, and a lot of publicity about an event. Another good thing about, you can quickly see interesting teams and who’s capable of doing great work under pressure and I think-
Kirill Eremenko: It’s also a way to bring the community together.
Daniel Obodovski: … Absolutely. I couldn’t agree more with that. So it’s a community building event. It’s a great way to identify interesting talent. It’s a great way to come up with great ideas. And it’s a great way to generate a lot of publicity. And there’s a reason why big companies like Facebook or Walmart and so on, and so on, they use hackathons for identifying product ideas. There’s a, I don’t know if it’s true or not, but somebody was saying that, for example, the like button on Facebook, which for those who remember when Facebook first launched, they didn’t have a like button. So the like button on Facebook was developed in an internal hackathon. But anyway, so the point is, a lot of companies are using that for internal product development.
Daniel Obodovski: What hackathons are absolutely not good at are producing real solutions. There’s just not enough time to produce something real. So you can only come up with a very, very raw idea, and very raw prototype that will take months to turn into something real, and there’s a very little continuity in traditional hackathons. So you do a hackathon, everybody gets excited, people get awarded, people shake hands and nothing happens afterwards. And that’s what we realized that we had to fix and that led us to creation of SCALE. So six month later, we started SCALE San Diego.
Kirill Eremenko: Great. So you founded this company and so that was the idea I guess to or the vehicle to take these ideas that hackathons generate further down and turn them into a products. But before we go into some examples of that, I wanted to ask you, what types of data do cities actually have? You mentioned Internet of Things, sensor data, digitization of content, or documents and things like that. Can you name a few types or categories of data that one could expect a city to have?
Daniel Obodovski: Well, I would say actually, the time series like the IoT type or Internet of Things, like time series data is a minority, is absolute minority. What you’re going to see a lot in the cities would be things like permits, would be blueprints, would be some financial reporting information, would be all different types of records, value of certain buildings, permits that have been issued for certain areas. This type of… That’s where you have a lot of data like that, and-
Kirill Eremenko: So more static form data.
Daniel Obodovski: … Static form data, but nonetheless can be very, very useful because if you can build the history of value of a particular property, industrial property, or it’s maybe like an industrial area, or different permits that have been issued over time, there’s some really, really interesting stuff that you can uncover in the process, and there’s also when you’re looking at a lot of public records and stuff, you really have to think about what problem does this record solve? Is it for real estate firms or is it for insurance firms or is it just for citizens or is it for anybody else? So I think it’s important to think about data in broader terms, not just sensor data, temperature, whatnot, humidity, or location and so on-
Kirill Eremenko: Traffic.
Daniel Obodovski: … This is all very important stuff, but you need to think a lot broader in terms of all kinds of data. And then of course, slowly but surely, you start getting unstructured data, audio, video, although there are of course, a lot of privacy concerns.
Kirill Eremenko: And I guess the more sensors you implement, the more the flow of that data increases. I really like your analogy of water in the basement. That you have water in the basement, you put a pump, you pump it out, and then all of a sudden now, imagine you have water coming in through the windows, then you take a bucket and you push it out, now you have even more water coming in. That’s kind of like as I understood from your analogy is that, that’s how it feels working with data in a city that’s becoming smarter and smarter.
Daniel Obodovski: Well, it’s funny you say that Kirill that when I was making that statement, I was primarily thinking about cities, and then we started meeting with a lot of corporations, and they said, actually feels exactly the same. We’re living in times where most, not just the cities, but a lot of companies have more data than they know what to do with, and some have 10, 20 years worth of data sitting in their databases, and they really don’t understand what to do.
Daniel Obodovski: So I think that kind of brings us to an interesting point of, so what do you do? What type of skill sets would you need? And how do you help making this data useful? And what do you do with all this data and what are some of the different tools to utilize this data? Because for example, what cities did and they started posting this data on open data portals with the hope that some people will go there and start building stuff. And this hasn’t happened because as you know, I’m sure a lot of your listeners also know, with data, it’s not just the data itself, it’s also quality of data, quality of metadata and data documentation that can make or break your opportunity to use it.
Daniel Obodovski: If there’s a bunch of data sets, but you have no idea what these data sets represent, that’s not going to help you much. And unfortunately, that’s something that we face very, very often with cities, and not just cities, also in the industry, that there’s data, but it has such an insufficient documentation. I mean, some people know what it is but if you just post it, people are going to struggle to make heads and tails of it.
Kirill Eremenko: Totally agree, and it’s also about priorities. I like what you said in your talk. Another thing you mentioned is that a lot of cities have the same challenges, but priorities are different. Can you talk a bit about that please?
Daniel Obodovski: Sure. So cities are different because as cities are run, I would say the cities are led by elected officials, and they’re run by essentially bureaucrats, right? By the higher people who are hired. But the elected officials are elected based on certain issues that they believe are important, right? And either they convince the communities that those issues are important, or they managed to strike a chord with the communities or say yeah, yeah, those other very important issues that’s why we’re going to elect you because you get it, and you offer something to do about this. And for different communities or for different cities, those issues can be very, very different.
Daniel Obodovski: Everybody deals with climate change. In America, most cities deal with homelessness. Everybody deals with traffic congestion, everybody deals with, I mean, there’s a ton of other things we can think of, right? I mean parking issues and so on, and so on, like public safety crime, but in different cities and communities, these issues are more or less important, right? If you had a lot of wildfires, like in Southern California, then environmental issues are going to be very important because people will be like losing their properties. How do they protect themselves from wildfires, or hurricanes or stuff like this.
Daniel Obodovski: In other cities, is like crime. I would imagine Chicago is probably, would be high on that scale, while a city of San Diego is actually a pretty safe city compared to a lot of others. There’s not a lot of crime there. So when you’re offering solutions to a city or to municipality or to actually to your region, understanding those priorities is very, very important. Because very often we hear from corporations saying, hey, we were offering them a great public safety solution, and they just said they didn’t have any budget. What it was really is that it wasn’t that particular solution that they were offering wasn’t high on the priority list. It had less to do with the budget. It had more to do with that it was not a high priority, but something else was a very high priority. And, I’ll give you an example in the city of San Diego, road conditions is very important, one of the top issues for the mayor.
Daniel Obodovski: So when one of the companies that emerged from our program, Road Reader that developed a computer vision solution for detecting cracks on the road, and not just detecting cracks, but also align them or sync them with the way that cities evaluate road conditions. That immediately got everybody’s intention. And now they’re gearing up, doing trial with the city. Although they just started literally like six or seven months ago. So it all depends on how high is what you’re offering on the priority. And I think that’s very important for companies and also for people to understand.
Kirill Eremenko: Fantastic. Love that example, and hopefully that goes well as a great testament to how technology can really improve cities to be smarter and in this particular case, road conditions. I wanted to ask you, what did you mean when you said analog problems, this was in your talk, analog problems don’t conform to digital solutions?
Daniel Obodovski: Yes, so that’s something I’ve been thinking about for some time, and that is actually with the cities it’s the most apparent. It’s also applicable for companies but cities is most apparent. As somebody who used to work as a large high tech corporation and kind of seeing the world through the lens of a high tech corporation, which most technology corporations, no matter what they say, they develop technologies, and then they think about what problems they’re going to solve with them.
Daniel Obodovski: They develop a lot of technologies. Some technologies don’t go anywhere, others are developing very, very fast. I mean, even if you think about the cellular networks like 2G, 3G, 4G, now we have 5G networks, every single time that networks were developed or deployed, people have very, very vague understanding how people are going to be using it. It just so happened that all other variables were there, and the phones were developed, and they had the capacities and the operating system and developer communities and everything else and everything else, so that we’re totally utilizing those networks and even more and even demanding more, but let me go back to the original question.
Daniel Obodovski: So the way technology companies think is, we can basically solve everything with technology, and we can add more sensors, and if we add more networks, and if we do this and this and that, that’s going to make things better, and so on, and so on, and so on. But if you’re on this other side of the equation, and if you are working for a city, what you’re seeing every day is not sensors and networks, you see homeless people on the streets who don’t have a place to go. You see a huge congestion and people cannot get from point A to point B and spending hours in traffic. You see people looking for parking, you see a lot of complaints with that, you see people go into the DMV, which is Department of Motor Vehicles and spending hours there in line.
Daniel Obodovski: So that’s what we call analog problems, and if you look at every one of them, it’s not obvious how you can solve with a 5G or how you can solve it with more sensors and remember, we we’re just talking a minute ago about how everybody’s drowning in data. So okay, so the city employees are drowning in data, right? And then there is a technology company that comes in and says, “Hey, we’re going to install this machine learning platform, and we’re going to put this network.” And the guys at the city goes, “All right. So you’re going to take all that amount of data that I already don’t know what to do with, and you’re going to multiply it a thousand times. Thank you very much.” And it’s actually doesn’t solve any of their problems, it’s just generating more problems for them. Because all it does, it just creates more data, and they all ready don’t know what to do with the data that they have.
Daniel Obodovski: So that’s what we call the gap between analog problems and digital solutions-
Kirill Eremenko: Gotcha.
Daniel Obodovski: … So somebody has to wrap their head around those analog problems and take a traffic for example, or parking or whatnot, and break it down into parts that can be improved by technology, and then bring them all back together. Because just by adding a parking meter, connecting the parking meter to the Cloud, you haven’t really solved the parking problem, you just added parking meter to the Cloud.
Kirill Eremenko: Yeah, it kind of reminds me of the whole situation back in around 2015 when there was this hype about Hadoop data lakes and every company like every CEO and their [inaudible 00:31:32] were installing data lakes were like tens of millions of dollars in their company, not knowing what then to do with it. It took years to roll out, two, three years. Or if it’s not on-premise, in the Cloud, maybe a bit faster, but still, they’re like, “Oh, we’ll put a data lake in. We’ll have all our data swimming around in that data lake.” And they didn’t know what to do with it and just in the end just turns into these, what? Proverbially is now called the data swamp.
Daniel Obodovski: Yes. That’s a great example, and I think we have similar things happening right now with kind of machine learning and artificial intelligence. And I think that’s why what you do and your company does is so important by educating people and giving them the right skills and looking at the problems, because today just going and hiring a lot of data scientists doesn’t really solve the problem. It’s kind of exactly like what you were describing with data lakes. Data scientists is a very, very important skill set, but needs to be applied accordingly, and if a lot of data scientists are just thrown at the problem, that’s very, very vague, and very badly defined, and very, very ambiguous, they’re going to fail. They’re not going to succeed.
Kirill Eremenko: Totally agree with you. And speaking of skill sets, what would you say are the necessary skill set? Is there anything unique about the skill set of data scientists must possess in order to help a smart city as opposed to work in the industry, work for a corporation?
Daniel Obodovski: Yes and no. It’s interesting, so UCSD just has recently formed Halicioglu Data Science Institute.
Kirill Eremenko: Yeah, it’s just… I was there two weeks ago on a tour. It’s really cool.
Daniel Obodovski: Yeah. They have amazing people there, and both the faculty and the students. And one of the classes led by Brad Voytek.
Kirill Eremenko: Mm-hmm (affirmative). The first data scientist at Uber. He was on this podcast a few months ago.
Daniel Obodovski: Yes, Brad is awesome.
Kirill Eremenko: Amazing guy.
Daniel Obodovski: So Brad actually teaches it’s not a purely data science. So Brad’s background is also cognitive sciences. He does research brain aneurysm-
Kirill Eremenko: He was a neuroscientist or something like that.
Daniel Obodovski: … That’s right, yeah. So he’s a very versatile and very well educated person. So he teaches his class, not just from a standpoint of building algorithms, but from the standpoint of solving problems. And I think that’s very, very important skill set, and it cannot be, I would say, emphasized enough of how do you… So it goes beyond data science, it goes more into data analytics, right?
Kirill Eremenko: Mm-hmm (affirmative).
Daniel Obodovski: And data analysis, not just data analysis, like business analysis and data analysis. Like how do you think about a process and how do you digest something very, very vague, like parking or traffic and start zooming in on what things you can solve. I mean, I’ll give you an example, another team that emerged from our program is Fresh Start. They looked at the issue of food insecurity. And food insecurity is another one of those audacious, kind of hard to understand problems. Like how do you solve it? What’s the problem with food insecurity? Is it behavioral? Is it logistical? So they just zoomed on the logistical part. They went and made sure, they talked to all the people and the food banks and so on and so they zoomed in on the logistical part. What they realized that there’s a lot of food donors, restaurants and hotels that have excess food that they don’t know what to do with. I mean, they’re just throw it away, and they would love to donate it.
Daniel Obodovski: The problem is what today when they call it food bank and say, “Hey, can you come pick up the food, we have a food to donate?” They go, “Yeah, we’d love to, but it’d have to be in two days or maybe in a week.” And by that time is already too late. The food’s going to spoil. So what they did, they basically build a platform and they integrated with Grubhub, and Instacart and Postmates APIs, and they figured it out. If they automatically schedule a pickup, those guys will go pick up and instead of going to the food bank, they’ll just deliver it to those who need this food the most.
Daniel Obodovski: So it’s kind of an on-demand, demand, supply and matching solution. That if you think about it, it’s not super crazy high tech, but it actually solves a very big problem and it also generates the tax receipts for whoever makes the donation so they immediately know how much tax credits they can receive. So this type of a skill set is very, very critical, because then you can actually, once you figure out the problem that you’re solving, then you can put the people who can write the algorithms.
Kirill Eremenko: Very interesting example. So it’s not about just being very good at AI or machine learning, it’s rather identifying the problems and coming up with solutions or the steps or I don’t know, the framework, in this case, all right, we have a problem that the food bank is not accepting food for a certain period of time, let’s create a solution. It’s kind of like a skill set to come up with ideas for solutions. It’s a very interesting… Indeed, data scientists need that skill in general to create algorithms, create models, and so on. But it sounds like here in smart cities, the predominant weight goes on that skill as opposed to the artificial intelligence or the coding skills.
Daniel Obodovski: Well, I would think that the same thing is applicable to corporations and we worked with a lot of corporations in The Silent Intelligence side. And I mean, that’s very, very similar. I think maybe the difference with the cities is, with the cities you’re going to find a lot of antiquated data. Do you know what an aperture card is?
Kirill Eremenko: No.
Daniel Obodovski: An aperture card is a punch card with an image or with an actually a micro image. And I didn’t know what they were until I saw one myself a few months ago. And where basically, the micro image could be like a blueprint, and the punch card acts like a metadata. So this thing is from 1960s or like 70s. And believe it or not, cities have millions of those. So a lot of old blueprints and stuff is sitting on those. Now imagine if you’re getting a request for some of those old blueprints and you have to go through like a machine reading all those punch cards. It’s crazy, right?
Kirill Eremenko: Mm-hmm (affirmative).
Daniel Obodovski: So this all needs to move to the 21st century, but somebody has to do it, right? And somebody needs to help them move all those old stuff and the way that this data was captured to a data lake, right? Or something that you can easily access data and do different interesting stuff with it.
Daniel Obodovski: So I think one thing is you’re going to face a lot of antiquated tools in data and stuff. Another thing is obviously like we talked before, understanding the priorities of a particular city and so on is very important. I think a city might have a lot more different stakeholders than a typical corporation would. And I think probably some of the biggest differences is corporations are driven by monetary value, while cities not necessarily always driven by monetary value. Cities are definitely driven by efficiencies, but they’re also driven by creating value for citizens, businesses and other constituencies. So this is something to consider.
Kirill Eremenko: Totally agree with all that. I would also add that cities are highly dependent on communities. You need to keep that in mind as well when you’re creating. So even if it might not be efficient, it might not be profitable, and so on but if it’s good for the community, then that’s what needs to happen.
Daniel Obodovski: Yes, absolutely.
Kirill Eremenko: How do these projects get funded? Not always, or does the city always have enough money to invest into a project, like a data science, data analytics project, even in the cases where it might not be a profitable project, but might be just good for the community, good for efficiency of the city?
Daniel Obodovski: Well, cities definitely… So the thing about cities is not that cities don’t have the money. The problem today with cities is they’re not very efficient with their money, and they do a lot of things. The problem with a lot of cities today is that the procurement process is broken and it needs to be fixed. It takes a very, very long time, and I surely understand why it was designed the way it is at a time, it made sense, but it doesn’t make sense anymore because very often when they go into the procurement and they procure software solution, it takes a long time. And by the time they finished procuring it, and actually it’s time to implement the problem that we’re trying to solve, already changed, the process already changed, and we see it all the time when cities spend maybe three or five million dollars or more on something, and then they realize it doesn’t do what they’re supposed to do, and they go back to the city council and say, “Hey, sorry, it doesn’t work. Can we have more money?” And so on, right? And so this is something that needs to change.
Daniel Obodovski: So to your question, there is money. It’s just the way this money is spent, needs to change. And I think it’s starting to change because a lot of cities already realizing that’s a problem and it needs to be addressed. Instead of spending money on huge projects up front, it’s better to go incremental, and make sure that whatever technologies they’re implementing, they’re solving a problem. I think it would make a lot more sense to bring something and do a project for 50 grand or a 100 grand, make sure it’s doing what it’s supposed to do, and then scale it from there. Especially with the way technology is evolving and the way that we’re understanding the problem. Yeah, so the money is there. It just needs to be looked at a little bit different.
Kirill Eremenko: Did you run into those procurement issues? And how do you go about them at SCALE SD?
Daniel Obodovski: Well, I wish I had a simple answer to that. We’re still working through it and figuring a lot of things out and ultimately, I think we will. I think there’s some good, positive developments. There’s this thing that called Startup in Residence that you might have heard of, that came out of San Francisco. I think this model is still being kind of tested and figuring out but the idea behind Startup in Residence is, if you’re branding a startup that’s solving an important problem for the city, that’s one of the top priorities, you can have an accelerated procurement.
Daniel Obodovski: I think they’re many other ways to address it, and those are some of the things that we’re working on. And I think, considering that everybody is aware of this issue, I think we’re going to see some changes in the next couple of years.
Kirill Eremenko: Gotcha, and you also mentioned that infrastructure projects are going to be the main focus for smart cities. Can you expand a bit on that please?
Daniel Obodovski: Well, so they already are. So infrastructure projects, involving different types of infrastructure, you see of course, the road conditions and the bridges and the buildings, you also have the water infrastructure, you have utilities infrastructure, you have telecommunications infrastructure, which is like fiber optical cable, and then of course, all the, like 5G networks in cities, femtocells, and so on, and so on, and so on and so on.
Daniel Obodovski: So you can think about some of this different layers of infrastructure is almost like building to a nervous system of the city. And kind of the, if we fast forward a thing, like what should the ultimate smart city be? And you think about a city like an operating system. Think about your smartphone, and the operating system on your smartphone has a whole bunch of APIs that allow access, kind of a well defined access to the accelerometer to the screen, to the GPS chip, and to some other resources, memory-
Kirill Eremenko: Silica.
Daniel Obodovski: … And so on, and so on, and so on.
Kirill Eremenko: Apps you install in your phone can get access to those parts of your smartphone through the APIs.
Daniel Obodovski: That’s right, that’s right. And so, which makes it very easy for developers to, as soon as they identify a need, or a problem, they can quickly go and write an application that addresses that need, that utilizes all those pre-built functionality. Well think about the same kind of way of looking at the city, where you have certain functionalities that are available to take advantage of whether it’s regarding parking, or street lights and maybe other things that others can quickly develop applications that improve traffic, or maybe improve some internal city processes, maybe improve financial reporting or improve permitting or improve inspections or improve whatnot, whatnot, whatnot, whatnot. So that’s ultimately where we need to get to. And there’s still a lot of work ahead. But I think right now we’re at the stage where a lot of this infrastructure being laid out.
Kirill Eremenko: Wow, I love that analogy with the smartphone and the APIs. That would be really cool once we get to that point. I guess the difference is that you as a smartphone owner, you choose which app you want to install, whereas the city… So every smartphone owner can make their own decisions, what they want to install, and there’s a competitive market of smartphone apps. But on the other hand, as a city you only get one shot at this, you’re like, all right, which one are we going to pick and hence, the whole procurement process and so on. Do you think something like a virtual city would be beneficial where people who want to develop these apps can actually go on apps or solutions, can actually go and try out, how is my computer vision for road conditions going to work or how’s this food challenge connecting food donors with people in need for food without food and security? How’s that going to work? Is that something that’s planned? These virtual cities or do you think that might be a good thing to help people develop these products faster?
Daniel Obodovski: Oh, absolutely. We’re actually just recently had a conversation with the head of planning department, and we talked about digital twins, and how having digital twins for city buildings, and for city roads and a lot of actually having a digital twin for the city would be a great thing for many, many, many different reasons. So I think if I understood you correctly, when we we’re talking about the virtual city, I think that, that goes the direction of digital twin. Is that correct?
Kirill Eremenko: Yep, yep, yeah. Exactly, yeah.
Daniel Obodovski: Yeah. That makes perfect sense, right? So you can run things and test things on the digital twin, and then push them to the physical equivalent, and that can be great for many reasons.For example, you create a simulation for traffic lights, right? How can you guide emergency vehicles through the city the fastest possible way? Today, of the way it works is that it’s like 1980s technology. So the fire trucks, they’re flushing strobe lights at the traffic lights, and they change to green when they approach, right? And as they’re doing that they’re generating a lot of noise by the sirens, which like not very pleasant to put it mildly. Why are they doing this? Because they’ve been doing this for like hundred plus years before the sirens that were the bells and stuff, right?
Daniel Obodovski: But think about solving this problem using the adaptive lights and the moment you know that a fire truck supposed to roll off or an ambulance, you start switching the lights that flush out the traffic and open streets so that a fire truck can go through an open streets while everybody else is waiting and try to simulate a couple of scenarios.
Daniel Obodovski: What if you have like one fire track and one ambulance? What if you have two ambulances and three fire trucks? And see how that’s going to affect the traffic. I mean, running scenarios like this on the digital twin would make perfect sense.
Kirill Eremenko: Mm-hmm (affirmative). Yeah, absolutely. I actually heard this specific problem. I heard of a city in Spain, I don’t know which one. I think it’s, there we go, it’s in Santander where they actually developed something similar to that. Not in a digital twin, I think, but it was through these traffic lights. But I totally get your point, yeah. Digital twins would kind of make that analogy even more real with the smartphone where people can actually try things out, and the city doesn’t have to bet everything on one solution before knowing what the likely outcome of that is going to be.
Daniel Obodovski: That’s right.
Kirill Eremenko: A quite important question, and something that you touched upon during our conversation a bit earlier is privacy. With all these sensors popping up all over the world. Sometimes I know in the UK at one of the train stations, at least one of the train stations because I know the brother of a person, also like through one of my connections. I know the guy who’s in charge of this whole company, who’s running his company. When you look at advertisement, like one of those banners or like those changing flashing signs. When you look at it, it’s actually counting how many people are walking past, what you’re looking at, what your interest is and how much time you’re spending in front of the ad, and so on, things like that.
Kirill Eremenko: So that’s just one example of sensors, starting to slowly get into the space of maybe invading privacy. What have you seen in the space of smart cities in terms of privacy? And how do on one hand the government officials react to that? And how does the population react to that?
Daniel Obodovski: So one of the biggest problems that we’re having today is the, I would say, so the cameras are becoming cheaper, and it’s very easy to install, right?
Kirill Eremenko: Mm-hmm (affirmative).
Daniel Obodovski: And the capabilities of cameras are growing exponentially. I mean, look at all the stuff you can do with your smartphone camera. Now, what’s disconcerting is, we as citizens, not only don’t we know what can happen with if somebody starts capturing our video data, we have no control over it, right? And, of course, it’s not a huge leap to think of how some of this data can be used against us, with all the best intentions. How can some of this… And the problem is not just that somebody has to have ill intentions per se, but it’s just we don’t understand the capabilities of a lot of machine learning algorithms.
Daniel Obodovski: I mean, considering that we don’t really know what’s happening inside some of the deep learning machine learning algorithms, how it will identify a certain thing. Like if we don’t know, how can we be sure that it’s actually going to protect citizens and not discriminate it? So I think the concern is very valid. The thing is of course there are a lot of technologies out there for anonymizing. So the moment a camera captures a human face or a license plate, you would immediately block it, and it will never send this data to anywhere, right? The technology is out there. But a lot of people don’t know what it is.
Daniel Obodovski: So because they don’t know, there’s a lot of concern. So I think the big problem right now is educating the public about what it is, what it can do, how it can be used, because the backlash against cameras in California, in general, is huge. Because people don’t understand what’s going to happen with their data, and they don’t have any control over it, right? And yet cameras can solve a lot of problems in cities. They can or not cameras per se, but the data from cameras and the intelligent algorithms can address crime, they can track economic activities, they can be helpful for retailers, and businesses, they can help address traffic problems, they can help understand how people use bike paths, and so on, and so on, and so on.
Daniel Obodovski: So there’s a huge potential of using the data but right now, there’s a lot of backlash and confusion, and somebody needs to start to explaining what’s possible, what can be done or what should be done. I think right now, that’s a big problem.
Kirill Eremenko: Okay. And so, any kind of predictions, where do you think it will go? Will it become at some point easier, or do you think it’ll just get harder from here?
Daniel Obodovski: Well, so for now, a lot of cities just decided not to install cameras, and we tried to look at the best practice examples and we couldn’t find any, right? We couldn’t find a single city that would say, “Okay, we’ve figured it out how to deal with video data and make sure that everybody feels safe and never at the same time don’t feel like their privacy is being invaded.” We haven’t found those, not yet. So somebody will figure this out eventually, and I hope that the solutions will be fine. They’re just not there yet. And that’s why we’re seeing a lot of backlash against cameras.
Daniel Obodovski: So kind of some of the maybe interim solutions is if you have the cameras that point on the ground, so they’re not capturing any faces, like for road conditions, for example, right? Or they’re capturing on sides of the buildings to capture graffiti information, stuff like this, and so on. So that’s one part. The other part is demonstrating the solutions that actually can help traffic. I think that’s actually an interesting point that I’d like to make.
Daniel Obodovski: So there’s a big problem with understanding of technology by communities, right? And when a city goes and spends 30, 40, or whatever million dollars on a new technology, whatever that is, and the citizens don’t feel any improvement, don’t feel anything, then the reaction would be, okay. You just spent all that money of public money. Why didn’t you put it into a school to improve a school or a hospital or something like that?
Daniel Obodovski: And what needs to happen is, we need to tell more stories of successful implementation of the new infrastructure, new technologies, new networks, new sensors, and so on, and so on. Because when people are going to see how it is improving their lives, from the daily basis, the traffic got better, that the parking became better, that there are more bike paths as a result, and there’s this and there’s this and there’s that, then I think there’s going to be much more, much better interest embracing of technology.
Kirill Eremenko: Very interesting, very interesting. I totally agree, it’s semi corporation, right? Like when you go in and you do some data science projects, you help people or if a company has establishing data science department, they help different divisions. You make so much more of a difference if you come back and you then present and you explain what kind of difference you made and how you helped those people in that department. You instantly could be billed advocates or the data science and technology part of the work.
Daniel Obodovski: Absolutely.
Kirill Eremenko: Yeah. Okay, very interesting. I’m glad we went through how corporations and cities are similar and how they’re different at the same time. Another thing I wanted to ask you, you said, you looked at different examples for this specific video problem, camera problem. By the way, with the camera, it almost feels like there should be, somebody should invent a camera that just on a hardware level does not record faces. Like that would probably be, it’s hard, is even impossible to think about. But if somebody can create something like that, that will be like a billion dollar opportunity right away right there.
Daniel Obodovski: I think it’s true. I think it’s a great idea. But also, the thing is when a person goes down the street and sees a camera pointing at him, or pointing at his or her backyard, try to explain them that it’s actually on the hardware level, doesn’t capture their face and see what their reaction is going to be. So that’s why I’m saying a lot of education needs to happen.
Kirill Eremenko: Yeah. That’s right. So what I wanted to ask you is what are some of the most audacious examples of cities becoming smart, like using technology to combat some of their local problems? Unnecessarily in San Diego, but across the nation in the US or even globally that you’re in this space? You’ve obviously heard of some really cool crazy examples, I thought it’d be a good idea to share some of them with our audience to inspire of what is possible.
Daniel Obodovski: I would actually take a bit of a contrarians stance here.
Kirill Eremenko: Sure.
Daniel Obodovski: And I say it’s not the crazy out there applications that will make a difference. It’s maybe less about creating some crazy, out there technology. It’s more about connecting the dots with the things that are there in helping improve processes. Some of the very successful smart city companies like OpenGov, for example. OpenGov, developed a financial reporting tools for cities. Well, the data is just to help them analyze… Well, actually help them share financial data in a consistent manner between departments. I think they’re in thousand cities worldwide. And, so again, don’t get me wrong. I’m not saying don’t do sophisticated technology, it’s actually does require quite a bit of analytical capabilities and writing algorithms and whatnot, and a lot of cases it requires understanding of your really sophisticated stuff.
Daniel Obodovski: For example, digitization of data, right? It can be a very manual labor intensive task, or it can be totally automated task, if you can do the automatic document tagging, and automatic document type recognition, you can save a ton of money and time and this can be a multibillion dollar opportunity. I mean, there are companies already doing this in all fairness, right? But not necessarily for cities or not necessarily for certain applications. But what I’m trying to say there’s a ton of opportunities where you help cities do what they’re already doing, just doing a lot faster, a lot of better. Think about the permitting process. Sometimes you need to wait for months to get a permit.
Daniel Obodovski: Can you reduce this down to hours using machine learning, where you would automate a lot of these tasks, or inspections, can you make sure that you don’t need to have like a physical inspector go inside and inspect it because maybe you’re using cameras and maybe you’re using something else. That can save a ton of time and money. There’s a lot of huge opportunities, I would say, the biggest opportunities that we are already seeing and are going to see in the next few years, are not necessarily because of some crazy cutting edge technology, it’s because of solving some very, very real problems by connecting the dots, If that makes sense.
Kirill Eremenko: Yeah, yeah, that totally makes sense as a very valid point. What about RPA, do you see cities starting to use robotic process automation? Because we’re seeing that a lot in corporations, and a lot of consulting firms are actually switching to predominantly doing RPA because I think it’s like the next five years, RPA is predicted to disrupt 9% of the global work with 230 million jobs. Are cities embracing RPA or not yet? What are you seeing?
Daniel Obodovski: Well, with the cities, I mean, you have several issues, right? You have unions in some cases, and you have a lot of concerns about the people losing jobs, and so you have to be sensitive of that. I think there’s definitely huge potential, it’s just you need to be very careful in the way you present that and make the case. And that case needs to be that you’re not trying to get rid a lot of city employees, you’ll want to free up their time to do more meaningful things. I think this as with cities, is how you tell the story is almost as important as what solution you’re providing.
Kirill Eremenko: Yeah, yeah, absolutely. Well, this has been a crazy ride. We’re just running short on time. I’m sure we could keep talking for ages. What would your recommendation be for those who have been listening to this episode and are quite excited about the opportunities that smart cities present? And maybe they’re not ready to go out there and start a startup of their own and pitch their services to cities, but they want to eventually be prepared for maybe one day joining a company that works in smart cities or joining a city, like I’m sure San Diego’s probably starting to hire, already hiring a data scientist. What are some of the, I don’t know, inspirations that you can share or recommendations for further education and things to look into for the future?
Daniel Obodovski: Well, if they are somewhere close enough to San Diego, they should definitely check us out, which is SCALE San Diego or scalesd.com, and come meet with us, talk to us and we’ll be happy to see what kind of projects they might be of interest. We’re always looking for people willing to work on projects. Outside in wide world I think other cities might have somewhat similar initiatives that we have, maybe a little bit different the way we do them. But I think every city recognize the need of kind of what we do, bridging the gap between urban challenges and technology, and they have different organizations.
Daniel Obodovski: City Digital in Chicago is one example. I mean, almost every city has something like that, and I think it’s good to reach out to those and talk to them. But ultimately, it’s good to think about some of the problems that you are trying to sell the new community, something that you’ve experienced yourself, and maybe it’s been your frustration with going and getting a driver’s license or anything else and think about, how would you solve the problem using data science? And then you can look up, okay, what data is available? Can you build a prototype? Okay, look at the open data portal of your city. Look what data says they have available? Can you start doing something with them, and then go reach out to either the cities or reach out to some of the companies that are doing smart city solutions, and there’s a high chance that you will have an interesting conversation as the starter.
Kirill Eremenko: Fantastic, fantastic advice. I love that. Start building a portfolio of projects based of that data that cities make available publicly. Daniel, we’re out of time. Thank you so much for coming on this show. Really appreciate you, amazing episode. Loved diving into this podcast. I’d love to stay in touch personally, and of course, for our listeners, what are some of the best ways to get in touch? You mentioned scalesd.com. What are some of the other ways that people can get in touch, follow your career? Or maybe even ask you about some projects that you’re working on?
Daniel Obodovski: They can definitely shoot me an email, on do@scalesd.com or find me on LinkedIn and connect to me on LinkedIn, which is I think is linkedin/danielo.
Kirill Eremenko: Yeah, very simple. linkedin/danielo, and of course, scalesd.com and the silentintelligence.com as well. All right. On that note, one more question actually, for you, what’s a book that you can recommend to our listeners to help them with their careers or lives?
Daniel Obodovski: It’s interesting, because what I’m going to say is also kind of may be a little bit contrarian. So I’ve been reading The Prince by Machiavelli, which is obviously the book that is hundreds of years old, but it actually addresses a lot that might explain of the political landscape that we seeing right now. And a lot of concerns that people might have. I’m not going to go into any more detail than that. But just to say, it’s a book that was written hundreds of years ago in Italy, by somebody who analyzed a lot of countries, a lot of empires, a lot of governments and a lot of rulers and came up with a whole set of recommendations so to say, which can be said, a little bit questionable ethically to put it mildly but it’s a still a very important book to read, maybe to understand a lot of things that are going on.
Kirill Eremenko: Like what thing?
Daniel Obodovski: How certain leaders of certain countries, also some of the elected officials that we have here, maybe making their decisions and how they’re doing certain things. And one of the things that probably was, that hit me really, I thought, is it really possible that in the 21st century, we can run countries the same way we did 1000 or 2000 years ago? This cannot be possible and yet, sometimes after reading Machiavelli appears, it actually is possible. I’m not necessarily sure it’s a good thing, but that’s my conclusion.
Kirill Eremenko: Okay. So kind of it helps understand the risks or the things to look out for. And as they say, history repeats itself, so why not see the negatives that can happen?
Daniel Obodovski: Yes. Well at least that’s the book that I’ve been reading right now.
Kirill Eremenko: Gotcha. Okay, so there you go, The Prince by Machiavelli to stay alert and probably helps with those privacy issues as well, that we’re talking about here.
Kirill Eremenko: All right, Daniel, thank you so much for being on this show today, sharing your insights and knowledge. I really wish San Diego to become the smartest city it can possibly be, and yeah, I’ll chat to sometime soon.
Daniel Obodovski: Thanks Kirill.
Kirill Eremenko: Thank you, ladies and gentlemen, for being part of the show today. I hope you enjoyed this conversation as much as I did. I personally learned so much about smart cities, from today’s chat with Daniel. I liked everything but my favorite part, probably from the curiosity perspective and from the inspiration perspective is the concept of digital twins for smart cities. I think that’s something that is going to happen. It’s on the horizon, probably going to take a couple of years. But eventually, more and more cities will actually have their digital twins, where you can like an app developer for a phone, you can join in and plug in your solution and see how it would work in that city.
Kirill Eremenko: How would your solution to traffic or parking or document digitization or food or homelessness, how would your solution impact the city. I think that’s very exciting. That’s something to look forward to and that gets me excited about the space of smart city.
Kirill Eremenko: As usual, you can get all the show notes for this episode at www.superdatascience.com/311. That’s www.superdatascience.com/311. There you’ll find the transcript for this episode, any materials that we mentioned on the show, plus all the URLs where you can find Daniel and connect with him. If you are part of San Diego or you’re near San Diego then make sure to check out SCALE SD as Daniel mentioned, they’re always looking for talent for people who are interested and passionate about the space. And on the other hand, if you’re a corporation looking for some sort of help with your technological transformation processes, then make sure to check out silentintelligence.com.
Kirill Eremenko: And one final call to action, if you know anybody who’s interested in smart cities, or who might be interested in smart cities, or maybe someone who lives in San Diego, send them this episode, spread the love and share the knowledge. The episode is very easy to share, just send them the link, www.superdatascience.com/311.
Kirill Eremenko: And on that note, thank you so much for being here today. I really appreciate your time and I can’t wait to see you back here next time. Until then, happy analyzing.
