NUS Cities Lecture Series: Transforming Cities Through Data Governance
In this NUS Cities public lecture, Dr. Jonathan Reichental—former CIO of Palo Alto, founder of Human Future, and author—explores how data governance can turn cities into smarter, more resilient, and livable places. He begins by sharing his personal journey from private‑sector technology leadership to urban innovation, establishing credibility for his perspective on city‑scale data. Reichental frames cities as the strategic frontier of the 21st century and positions data as the new oil, citing that 83 % of leaders want to be data‑driven but only 32 % achieve it. Through concrete case studies—the Pavement Condition Index hackathon, Palo Alto’s open‑data‑by‑default proclamation, the 311 service with near‑real‑time issue mapping, and the resulting ecosystem of third‑party apps—he shows how open, high‑quality data fuels citizen engagement, private‑sector innovation, and economic value. A central theme is the need to re‑position data governance not as a bureaucratic cost center but as a catalyst for innovation, revenue growth, staff retention, and risk reduction. Reichental explains that when governance is done right, companies (and cities) see higher profits, fewer cybersecurity incidents, and faster innovation cycles. He outlines practical, low‑cost governance structures: assigning data ownership and stewardship roles (often part‑time), establishing a data‑governance board, and focusing on high‑value data subsets rather than trying to manage all data. He stresses the importance of leadership vision, a clear data strategy, and robust governance as the three pillars of a data‑driven culture. The lecture also warns of the consequences of neglecting governance, citing financial and reputational damage in cities such as Baltimore and Atlanta that suffered from poor data management and cyber‑attacks. Reichental offers entry points for resource‑constrained municipalities—open‑source portals, pilot high‑impact datasets, community hackathons, and incremental staffing—to build a data‑centric workforce while maintaining cybersecurity. He proposes concrete maturity metrics (data quality, access‑request tickets, integration failure rates, presence of data owners/boards) and highlights the need for city‑wide data literacy and cultural change. In the Q&A, participants probe topics such as cybersecurity for open data, low‑cost governance models, crowdsourced and synthetic data trustworthiness, and international examples like New Zealand’s indigenous‑sensitive data framework. Reichental reinforces that data governance, when framed as an innovation driver with clear ROI, is essential for cities to thrive in the digital age.
Chapters
Good evening ladies and gentlemen and welcome to the 11th public lecture hosted by NUS Cities. Before we start the event, please ensure your mobile devices are all either on silent or powered off. Thanks. For those who don't know about NUS Cities, we are a university wide, multidisciplinary, open and inclusive collaborative platform host hosted within the College of Design and Engineering, otherwise known as cde. Through our collaborations with local and international institutions, we hope to create synergies within NUS as well as beyond. The NUS Cities Public Lecture series will investigate policies, ideas and projects developed by urban experts which aspire to create sustainable, resilient and livable cities. Through the lecture series, we hope to create a platform for discussion and retrospection and conversation to give momentum to the ongoing research and exploration regarding issues concerning cities. I would now like to introduce our speaker of the evening, Dr. Jonathan Reichental is the founder of Human Future, a Global global business and technology advisory, investment and education firm. His previous roles have included being the Software Engineer, Software Engineering Manager, Director of Technology Innovation, and he has also served as the Chief Information officer at both O'Reilly Media as well as the City of Pao Alto in California. In 2013 he was recognized as one of 25 Doers, Dreamers and Drivers in the Government of America. In 2016 he was named a Top Influential CIO in United states and in 2017 he was named one of the top 100 CIOs in the world. Dr. Eichendal is a recognized global thought leader on a number of emerging trends including urban Innovation, Smart Cities, Sustainability, Blockchain Technology, Data Governance, the Fourth Industrial Revolution, and Digital Transformation. He holds several degrees including a PhD in Information Systems. He's an Adjunct professor in the School of Management at the University of San Francisco and also interests at several other universities including nus. He has written three books on the future of cities, Smart Cities for Dummies, Exploring Smart Cities, Activity Book for Kids, as well as his latest books include Data Governance for Dummies and A Cryptocurrency Quick Start Guide. So please join me in welcoming him on stage.
Thank you Sanjana. I clean up pretty nice right? I need an updated picture so it's great to be in this room with all of you and I'm so delighted how many people are here and I should welcome also people joining by zoom. I know there's quite a lot of people joining by zoom and if you're watching this on YouTube a few weeks later, a few years later, hello to you too. I know that people do that as well. So this is fabulous. I'm just delighted to be in Singapore, although my time here is coming to an end. Thank you to Professor Ku who invited me and maybe part of the family here, and I'll always be part of that family for good. It's been an interesting six or seven weeks, but it concludes now and this is like a highlight. This is a great way to exit my, my time here. Of course, I'll return to Singapore in the not too distant future. I, I've been here six or seven weeks and I've learned a lot, but I've only learned a tiny bit about this place. I really have. I figured out today I've been thinking about it and talking to people. I'm like, I know a little bit more, but there's a lot more, a lot more to know. But I, come on, I do these things because, you know, I visit sometimes a place just briefly and, you know, two or three days I do a keynote somewhere, attend a conference and I leave. And it seems interesting to me, but I know nothing about the place I visit. And so spending a few weeks here, getting to meet people, getting to eat the restaurants, do the tourism stuff, but go to people's homes, it actually allows me to, and all of us to learn much more about a culture than just quickly visiting. So for that reason, it's been a really, a great, a great joy. So I'm going to be speaking to you for the next four hours. That's okay. Don't worry, it won't be that long, just three and a half hours. But I'm going to talk to you about data. But before I do that, I did want to add a little bit more to the wonderful, beautiful comments that Sanjana said about me. She read them exactly as I wrote them. But I'm going to now tell you a little bit more about my background and sort of my interest areas and why I'm standing here and you're sitting there. Why do I have credibility to speak on any of these topics? I am a computer science guy. That's my background, actually. That's my joy. I like technology innovation and I've studied computer science and information systems. That's the place where I sort of come from. But I got this invitation out of nowhere back in about 2011. Headhunter called me and said, hey, what would you think about helping to run a city? You know, run the innovation and technology for a city. I said, what city? Depends on the city. They said, Palo Alto. Now, some people know Palo Alto. If you don't, I'll Quickly give you background. Often called the birthplace of Silicon Valley. There's some debate over that, but there's a lot of cool stuff happening. It is really the center of the action of Silicon Valley even to today, although it's spread considerably across the Bay Area. And of course, Silicon Valley has lots of competition in the world today. That's the one thing we worry about, is that we maintain that leadership position. We never become complacent. So I thought, oh, that's interesting. I'm a technology guy, and you want me to be the head of innovation and technology for a. The heart, the birthplace of Silicon Valley. That sounds like an interesting job. And I'm interested in how cities work. I mean, honestly, if you are somebody who has never thought for a moment, how do the traffic signals work, how do we get energy, how does the trash get taken care of, how does public safety get taken care of? Or if you don't think about it, it just sort of happens. It becomes fascinating as a place to explore and suddenly learn. Wow, how this is amazing. So much goes on behind the scenes. I saw the world then through a lens that I had never considered before. So I said yes. But, you know, talk to the city manager was cool. A guy called Jim Keane, very, very good guy. He's retired now from. From public life after about 35 years of being a city manager. I took the job partly because of him, actually. This was a great guy. And I knew I could learn a lot from him about how cities run and when we were kind of making the decision, because I was a little bit hedging, because I was a private sector guy, should I work for government? You know, is it the right move for me? Honestly, candidly, I'm, you know, as a technology leader in other companies, you know, my thing was always kind of break rules, move fast, sort of the Facebook motto, you know, and then they're, I'm going to work for a city that's probably not a good match. But they. They worked on me. And the city manager said to me one day, said, jonathan, you got to make a decision. You got to commit. And I said, well, I'll make a bet on you if you make a bet on me. And we didn't know. We said, okay. We shook hands and I did it. And what's the worst case? After a few months, it didn't work out. I would leave. But it worked out in very spectacular ways, in ways I didn't even anticipate. So here's the transformation that happened to me. I thought that I was Going to go to a city and working with a great team and hiring great people and working with vendors and working with the community, we would change the community in a positive way. We would create new ways of delivering city services. Well, that didn't. That kind of happened. I mean, actually, a little bit of that happened. But the surprise was it wasn't that I changed the city, it was that the city changed me. I saw the world through a completely new lens. I didn't realize that the destiny of humanity was in cities. And this would be the great challenge. The great focus of the 21st century would be our cities. I didn't realize that most humans lived in cities. And through the 21st century, more and more would move into cities. You would be born into a city, live a life in a city, and your life would be spent in an urban context. For the majority of humans on the planet, I thought, that's pretty important. That's pretty important. As I started to travel, people were interested. What are we doing? How are we getting successes, and what have we learned in Palo Alto? As I would travel around the world to different countries, I realized very quickly that a lot of our cities are not functioning well at all. They're not creating a good life for everybody. I said, this is interesting to me. This is how I want to spend a lot of my life. Because if you can improve cities, you can improve the lives of millions, perhaps billions of people. So that's what happened to me at the city of Palo Alto. What else did we do there? I was there for seven years. I thought I'd be there for three. It was too interesting. It was too like. I was like, I'll stay another few months, stay another few months. I want to get this thing done. I want to achieve. So I kept staying, and finally, I'd done everything I wanted to do. It was time to do other things. In actual fact, the reason why I left was because I wanted to do what I was doing in Palo Alto, but with other cities. I didn't want to be just exclusive to this one little pretty town in Silicon Valley, although an important one. I thought I could be useful by helping other communities. And that's what I've been doing for the last five to six years now. When I joined the city of Palo Alto, just a little bit more of this story, because I think it is relevant to NUS cities and this topic. When I joined, the city manager said, what should be our vision now that we got you on board and we're paying top dollar for you want to Extract as much value as possible. Right. What do you think we should do? And I said, give me a few days to think about. I'll come back to you. I came back to him and I said, we should try to be the number one digital city in America. This was before we were using the term smart cities. And he said, wow, that sounds incredible. What does that mean? I said, I don't know, but it sounds like the right direction. Let's do it. Of course, I was only kidding, really. It meant, how can we bring digital to bear on all sorts of issues. Transportation, water, electricity, public safety, public works, libraries. How can we make the engagement between the community and the city superior? And by the way, really key, how can we make it a model for communities all over the world? Because we think in Palo Alto, where you have the emergence of great phenomena, people all over the world use. Some people love the stuff we make, some people hate the stuff we make. Or for better or for worse, a lot of us use the things we make. And so we could also do good. We can actually create new models for government using technology and try to share them with the rest of the world. So we went on this mission two years. About two and a half years later, we were named the number one digital city in America. Very, very proud of that. And we kept that position for three, four years in the top sort of three, until I left. I don't know what happened after I left, because I moved on. I was ready to do the next thing. So we really did produce a lot of new ways of running a city. A lot of emphasis on digital. And by the way, you might think, well, that doesn't seem terribly innovative. It was 2011, 2012. That's 12, 13 years ago. That's a long time ago in tech history. You know, this is an industry that's barely 40, 50 years old. So 10, 12 years ago is a quarter of the entire history of information technology. You know, we. This was a time, for example, when I would attend meetings with other CIOs in the community, in the. In the region from Cupertino and Mountain View and Redwood City places, you know, of big places where a lot of tech firms are based out of. And I would be at these meetings, the CIOs. And the question would come up, you know, what's this whole cloud computing thing? Right? I was saying, we're gonna be a cloud first city. We're gonna move everything to cloud. Cloud is the greatest thing that happened to cities. The rest of my peers were saying, we don't like cloud. Cause we don't have any control and we can do things more securely. And so the topic, the discussions around some of these contemporary technologies were still very immature. I had two things. We were going to be cloud first and mobile first. And as you'll see in my presentation here, data would be at the center of our action. So that's a little bit about my sort of story, a bit of blatant promotion here. I'm an author as well. I've written many books. Here's some of my books. If you like what you hear today and you want to learn more, go to my name right.com books and, and you can see, yes, I've written books for kids, for children, very popular recently. This was, was translated to Hindi. So it's, it's for sale in India. And this one is it actually converted, was translated to Persian. It could be bought in Iran. Otherwise they're in English. And in fact, two lucky people today are going to get copies of my Smart Cities book. I only could carry out of. I don't, I could only carry a few in my suitcase. So I have two left. I kept them for this meeting. I've signed them. So I'll have to figure out during this presentation how I'm going to give them out tonight. But I'll figure that out. Maybe somebody really, really wants this book. You know, it's for Dummies. It's a brand, right? The biggest reference series in the world, 250 million books sold. Not of my book, the Dummies books. And, but I would have written the book the same way for any publisher. This is a brain dump of basically over a decade of my insights about advising cities and building cities and running cities. And finally this is the topic today. I wrote a book on this called Data Governance of what we're going to talk about apparently today. And again, if you are interested in learning more, you like the style that I present or even if you want to know more about this topic, this is a way to learn about it. And of course all my videos and posts, all the materials I produce and by the way, so much of it is free because I'm giving it away to you. I want the world to be helped by whatever I can share the little humble piece of knowledge I can share with the world. You can go here. But I want to start with a story that will set the stage for us. So you know, if you are into cities and I think you're here because you kind of have some interest in cities, this is a picture that gets you really excited, right? If a road doesn't get you excited, you're in the wrong room, you're studying the wrong topic. Literally. Cities are about roads. That's a big part. A big part of it, right? And this is not a great road, right? You can see it's not really well constructed, it's got some potholes and it's beat up. What I didn't realize was city roads have a score. We call it the Pavement Condition Index. Pci. Some of you know it. It may be known as different things in Singapore, but every country has a way that you measure the quality of roads. And I think right away we could relate to this. Right? And when you work in a city and you ask people or you get feedback from the community, they're going to tell you about the roads. You think they're thinking about sophisticated things and they're like, that pothole outside my house is driving me crazy. And the city hasn't come to fix it. That's what the community really cares about. It's part of what they care about. Seriously. They care about budgets and roads. That's the two things they really care about. Most communities in the world, I only half kid about that. So there's a pavement condition index and it's given a number, Right. So again, the data scientists in the room. This is like, you're going to drool at this, right? This is awesome. We love this spreadsheet with columns of rows that are. Wow, I love this.
Right?
Okay, now everyone's trying to figure it out, or those of you who like data. Well, these are streets, right? You got the latitude, longitude, right. You got the street name, and then you have a score. Okay? This is the score of these streets. The score is between 0 and 100. 100 is a perfect road. It's just been surfaced. It looks beautiful. It's perfectly surfaced. Zero is kind of dirt road. It's not really a road. Right? So these roads actually are scored quite well. They're scored quite well. So an elected council or a leadership team for a city says, well, we need to know, how do we prioritize which roads to work on? How do we figure out which roads are the ones that we want to fix next? And what they have been historically dealing with are pages like this, reams of pages like this with numbers. It's really difficult. So back in about 2011, 2012 again, 12 years ago. So it's ancient times in tech. The mayor, Yahweh Ye was his name, great guy, works for Google now. Mayor of the city at the time said to me, how can we make this easier? How can we make this data easily, more easily? A thing that you can make decisions about. How can the community get engaged and make priority decisions? Remember, this is one page of hundreds. And that's just like, who wants to look through that? And so, you know, sort it and all that, you know, I said, let me think about it, let me think about it. So in true Silicon Valley style, I got a napkin and I drew an interface, right? And I'm not kidding you, this is the actual picture of the thing I drew on a napkin. And if you think I'm kidding about the napkins, I'm not. This is how big business is done in Silicon Valley, in bars, on napkins. It's like, I got an idea, I got to show you. And you draw it on a napkin because that's the only thing that's around when car figure whiskey or a cocktail, right? So I drew this and I brought this to the mayor and I said, this is a better way. We'll have a picture of the road. We'll have the score somewhere. I don't know where its score is, but one of the description of the condition. And then people can actually upload pictures themselves. This is really great because this allows people to be really engaged in their community. And the mayor said two very important things to me, which I was expecting. He said, I love this. Well, like three, I suppose. He says, I love it, but there's two issues. It's expensive to build this, and it's going to take a long time. And the mayor was absolutely correct. If we were to take. If we were to take this and convert it to this in a traditional way, this would be hard, it would be expensive. And that is a deterrent. I mean, there's lots of other things we can work on. So I had to go away and sort of think about how do we build this thing? How do we actually create this in a way that doesn't cost a fortune and doesn't take a long time? Now I'm kind of. We're kind of lucky. There's a little university in Palo Alto called Stanford University. You may have heard about it. It's a little, you know, it's just nothing much happens there. And they also have a computer science department and they have hackathons. They have these hacking sessions when programmers want ideas to work on. So I said, I'm going to get them to build it. I'm going to go up to students and have students build this. Thing. So I found out that two weekends in the future there would be a hackathon. So I plan to go up there. It helps too, to bring the mayor with you. Students love that. So I go up and I pitch the idea. I said, let's see if I go the right direction. Yes. If I take this, could you create this? And they're like, this is pretty easy, actually, because you know what software developers want? This is the easy part. What software developers want is data. Because once you have the data, you can build cool things on top of it, right? That's the. That's the. Really, the sweet spot is the actual core, interesting data. So go up with the mayor and I pitch the idea and they're all in. Everybody wants to do this. And we. The payment is going to be pizza and soft drinks. They're not going to sleep and they're going to work on this thing. And over the course of 48 hours, they built it. They built it. This is what. This is the actual picture. Picture of the road. See, this is a pretty good road, right? You see, this is nice. It's got a little crack here, but it's got a score of 83. And then the user can score it too. They can also upload pictures. It did exactly what I wanted. It took 48 hours and it cost $0. Counter to what the mayor was saying, I was pretty happy about that. It's like, I did it. Well, of course, you can't run a city like that, right? And that's not even the point of my story tonight. That's not even why I shared this with you. I shared this with you for two reasons. Number one, this is a great application to solve a big problem, so our community can make better decisions based solely on data. The interface is easy. You know where we got the photos from? Google Maps? It was free, right? We didn't even have to take photographs. It's already done. What the developers needed was to cut was. Was the data. The second thing was I proved very quickly it's possible to do things differently. That's a key learning. If we continue to do the things we've been doing in the past all the time, nothing will change, right? But I came back to the mayor with a completely new idea, got a completely different result. Now, of course, we're not going to run the city like that, but we discovered that, number one, we would use our data, of which almost every community in the world has an abundance of, and we would think differently about how to solve problems. Data became so important because I was screaming about it all the time. It's like I would ask my team, I said, I noticed we have a lot of data capturing everything, all our application, everything. And what are we doing with it? Nothing. We're just capturing it because it's a byproduct of something we're doing. I thought to myself, what would happen if we gave the data to people? Could they build solutions for us? Would the community build things that they wanted? Yeah, they would. When you ask communities to step up, they usually do. We just don't ask.
Right.
If you want to know how a community feels about something, ask them and they will tell you how they feel. Right. So this was so important. We said, from now on, as we create new, good quality, non sensitive data, we're going to give it away to whoever wants it. And we call this open data by default. We were the first city in America after the federal government, because around about this time, we had the first tech president. I think Clinton would sort of argue with that. But Obama, President Obama really was a tech president. He was totally a geek when it came to tech and he says that he's okay with that. So they were bringing a lot of new ideas. They were sucking up some talent from Silicon Valley, even from Palo Alto, to go to Washington D.C. and build solutions. They had this idea of open data by default. And I said, that sounds good enough for us. And this is the actual proclamation, a photograph I took back in 2014, February 3rd, actually, just almost. That's 10 years ago. Look at that. Almost 10 years to the day. I just looked at it now. We wrote a proclamation that declared that from this point on, signed by the mayor, Palo Alto would be open data by default. What does that mean? It means that all data would be made available to anyone who wanted it, but only restricted by stuff that was sensitive or regulated or protected. Right. This was a game changer. Once we started to do this, we could start to do really interesting things. Not only could we do it, but vendors and community members could do completely different things. We suddenly discovered people not only in Palo Alto, but all over the region around the world starting to build solutions around our data. Because we were giving it away. Good quality data. We were giving it away. Take this example. Here's an application that we customize. I called it Palo Alto 311. You have something similar here. Most communities have something similar. It's an app where if you see an abandoned bicycle or a pothole or Griffith, you take a picture. It goes to city hall or the administration and they fix it, and then they have to tell you when they fixed it, and then you can rate the service. It starts to hold the city administration accountable. So we're doing this. People love it. It's still actually used today. It's very much admired and used in the city, and it's in many, many communities across the U.S. and across the world. But I'm looking at this thing, and I'm very happy that my team built it, and we're really getting good results. And I said, wait a minute. We're creating a lot of data. We're getting amazing data here. What are we doing with it? Nothing. It's just a byproduct, right? So I said, what happens if we take the data and map it in near real time against the schematic of the city? And you get this. Here is a picture of the city, basically of a GIS picture of the city. And these numbers, sort of a heat map, represent the issues that are happening in different parts of Palo Alto. Like in near real time, not real time. The minute it happens, there's a little delay for a variety of reasons. You can now click on 54. There's obviously 54 tickets, 54 issues here, and you can figure out what's going on, what is going on today in that part of town that we need to address. And if the numbers are high, we ought to kind of figure out the root causes and try to fix it. For the very first time in the history of Palo Alto, a city of about 100 years old, we could tell in close to near real time the cadence of the city, the rhythm of the city. For the very first time, not super magic. We just took the data that we had and we had a coder just quickly map it against the community, and we made this available to everybody. You could check this right now. This is online right now. If you go to data, that's cityofpalawato.org, this is actually a near real time. You can see what's happening right now in the community. We're not shy. They're not shy about sharing that. So you quickly realize as I start to give you these anecdotes and stories, that data has an exceptionally important role in. In our world, in our businesses, and in our cities. Now, when I was asked to write the book Data Governance for Dummies, I had to research it. Now, I'd already been a tech guy for about, believe it or not, 30 years. And so I, you know, being in a technology role, being a leader in organizations, you. I was Exposed to all the kind of the management of data, the tools, the analytics, all the components. So I knew I had a good knowledge, a good insight into the role of data in organizations. But I was asked to write a book about data effectively. And one of the things I heard a lot of very early in my time was.
Data.
Isn't data the new oil? Like, data is the most important. So you know when you, when you're writing a book and anyone this, I'm talking to the right audience here, when you are saying something of significance, you should have some data to support it, some research. So I did my research on it, did my homework, I talked to a lot of business leaders, talked to a lot of cities. And I'll just get to the punchline for you guys. I validated, they were absolutely right. Data is the new oil today and it is the most important asset in every organization in the world today. You might not have thought about that coming into the meeting today, coming into the session and hopefully, perhaps during our short time we have together today, I will convince you of that. I will try to convince you that if it is the most important thing, are we taking advantage of that? Are we prioritizing it relative to its significance? Now the answer that you'll find, I'll tell you because we're here with friends, is we're not. Even though we know it's the most important asset, we're not prioritizing it, using it like it is. And that has to change. And I do a lot of work on that. I help organizations try to change that mindset and get the education and the tools they need to actually drive the change through data. Something incredible is happening. You know, as the world has been digitally transformed, what goes alongside digital is data, right? Where there's digital, there's data. Where there's data, there's digital. That's one of the T shirts that I made, you know. And why is that? Because we are a hyper connected world, right? Like I don't know how. There's about 100 people in this room approximately right now. There's tons of people joining us. You have at least one device on your body right now that's connected. Some of you have two, you have maybe a watch, some of you have three, you have a tablet, who knows what else you have. But it's possible that there are 300 connected devices in this room, all even though you're not doing anything, sending data and receiving data. So even doing nothing, we are producing high volumes of data in this room alone and multiply that by all the meetings, all the businesses, all the interactions in the world and you have a new era, an era of big data. In fact, we call it the zettabyte era. Zettabyte. This is a word I didn't even know existed a few years ago, right? By the way, Zettabyte is a one with 21 zeros. In other words, here's the very computer science explanation. It's a very big number, right? And you know, whether they're machines that are connected, it's kind of throwing off this what we call data exhaust, right? Stuff we don't even use. And there's, we're capturing sort of all sorts of logging stuff. We call that dark data. It's everywhere. And if we're able to analyze it, if we're able to use it in original ways, that not only is a competitive advantage, but allows us to innovate, do things we couldn't do the past. This is a very big deal. So once leaders recognize this or begin to recognize this, they want to become data driven. They're like, okay, this is incredible. We have so much of this stuff, everything we're doing, all these interactions, everything is creating this data. How do we actually start to be an organization that is driven, actually makes decisions based on this drives our strategy? How do we become a data driven? This means basically that in meetings, when you're having meetings, are people bringing up the topic? Are you making decisions based on validated good evidence? Now in an academic organization this might sound very straightforward, but I can tell you in most enterprises, the person who's making decisions, a lot of time it's gut and it's the highest paid person in the room, right? This often unfortunately happens. Those are not great decisions, right? We have the data to make decisions. Leaders actually recognize this. They actually recognize this when we survey CEOs of organizations. And even though this is a sort of a little bit of an enterprise slant, I would throw government into this bucket too. Government leaders recognize and want their agencies to be data driven. 83%. That's actually a great news story. I have always the question, what about the other 17%? What are they thinking? Like, nah, we don't want to be data driven. We're going gut. Just feeling how I feel that day. There is a role for feeling, by the way. There is a role. Before you start to challenge me on that later, somebody is thinking that. That's the question I'm going to ask them. What about like experience and skills and wisdom? That is important, but that generally is a Small slice. Today we should be making decisions based on good quality data. Now 83% of organizations want to be data driven, but the reality is only about 32% are actually achieving that goal. That's not a good number. We got to get that number up, right? We need the connection. We need the connection between the desire and the actual results. We need to make that happen. And that's generating a lot of good work for me and others. This is why you see this incredible new demand for tools, data tools and data scientists. There's a growing profession right where we need more, many, many, many more data scientists and data related jobs than we are churning out from university or have available on the market. These are high paying, good quality jobs and we need millions of more of them. That's actually, is not a great news story though. In a world where we're concerned about the future of what we'll do when AI and robotics take all our jobs. Nobody reacted to that. I think you're scared. Yeah, we're scared of the robots and the AI that's coming. That's going to take away a lot of work for sure. Displace it. Humans will do other things, but this is an area, data science and data related jobs where there will be increasing demand and we're not meeting it yet. And you can see how. So you know, overtly, we're making ourselves problems here, right? This for me is the most, this is a scary statistic, right, that 48% are making gut decisions. I expect maybe 5% or 5% of your decisions should be gut based because you, you've seen it before, you've been there, you've had 20 years ago, I saw the same thing or five years ago in a different business. I saw this, we did this and we had this great result. That's good. We don't want that. We want an organization that is data driven, where the data, the good quality data is driving good decisions. So then the question is, okay, we get you, you're all with me, some of you. If you bought into what I've said to you, if you believe that this is important, what do we have to do? Well, to become data driven, you have to have a data culture, right? What is culture? Culture is the behavior, how people behave every day in an organization. Do they come to work and think that I need to get the source of that data, I need the evidence. We can't make that decision before we have good data. Those types of things are illustrative of a data driven culture. There's really three Things that lie on top of that, that make that happen. Well, the one thing that's not shown here is leadership is essential. And well, you know that without leadership in anything, nothing happens, right? You have to have leaders who are charismatic, who have vision. Then things start to happen. That's largely a requirement. But beyond that, we need to have a strategy. We need to have this thing called governance and data management. Right? So this is where we get a little technical. We need. Strategy is pretty straightforward, right? We need a sort of a, A, a document or an agreement about where we're going to go, how we're going to get there. That's what a strategy is. We should change it as conditions change, right? Data management is fairly straightforward. Every company, every organization, every university, every government has data management. Once you have data, you have data management. You're probably storing it on computers, on servers, you're backing it up, you're archiving it, you're doing stuff. Now, that is not the same as saying you're doing it well or doing it right. It just says you're doing it. But you notice possibly that I didn't mention the middle one, data governance. That's because this is poorly understood and poorly implemented. The remarkable irony is data governance is where the action is. That's how you get the results, right? The others are really important. They're like administrative. But data governance is where you actually start to get the value. You leverage the value of data and you innovate and you reduce risk and you start to see the change that you want in your organization. It's a play on a famous saying, strategy eats culture. Sorry, culture eats strategy for breakfast, right? The way people behave every day is going to always crush anything that's written in a plan. But if people believe in the plan, they will reflect that in the culture. Okay, if I've not convinced you so far, the data on data is pretty clear, right? When you look at things like income, business income, when you look at things like staff performance, when you look at attractiveness of a business, when you look at the level of innovation, the growth organizations that are data driven do better at every single category. And not even by a small amount, by a magnitude, by a big amount. Leaders have to hear this because they have to be bought into it. Sometimes they can think, data driven culture, data strategy, data governance, data management. All sounds very bureaucratic, but by the way, it sounds really expensive. Well, it's not expensive if you're making a lot more money, if your business is growing or your city is performing much better, right? It doesn't matter how much something costs if the outcome is much greater than that cost. Data driven culture is one of those things that produces results. I really want to focus now just for the remaining time I have on the data governance piece because if there's anything that I'd like you to leave with today is what is it? What is this thing? This is the question I get most often. I know what strategy is, I know what managing data, but I don't quite understand what you mean by governance. It all comes down to two simple things. That it's about increasing the value of that incredible wealth you have in the data asset and about reducing the risks to your organization. Those are the two takeaways. It's as simple as that in terms of describing it, in terms of getting there. It's hard. Now when I, when I finished writing the book and published it last, early last year, I looked at two things. Number one is how many openings were there for data governance related jobs. That's always a good indicator. And also how much were companies investing in this topic? And I was looking at this data over time and it became obvious, you know, reviewing this as I should as a being that it's data, I'm doing some data analytics. The amount of job openings for data governance were increasing and are continuing to increase. And the investment by businesses in the tools to make this happen, to increase value, decrease risk was going way, way up and it was expected to over the next, is expected over the next 18 months to continue to increase. This is why you see so many companies jumping into this, so many data companies, right? That's a good indicator that this is a growth area. And finally leaders are beginning to recognize its, its benefit. So a few little characteristics about it. Here's the thing, right? You can have lots of data, you can have it managed, you can have a strategy. Turns out staff can't often find the data they need to do the work with it, right? How do you find the data and how do you do that in government? We need to do a much better job of making available data sets and explaining them to people and how they can be used. One example I love this, always worth playing with. This is the usdata.gov site and this is their data catalog. Organizations have catalogs now. Governments have catalogs of data. So you can go in and type in, I'm looking for our price list or our product list or list of employees or whatever it is. You have to type that in and get that back and know how do I access it, right? So Data catalogs are really key, and this is a great one to play with. It's the federal government of the US and not surprisingly, there's a third of a million different data sets and all sorts of really fascinating things, all sorts of really boring things, too. But it captures the data there, and then you can use it and develop solutions just to bring it home, make it a bit personal. Now, here's the open data portal, the catalog that my team built when I was at the city of Palo Alto. We said, learn about our city through data, make decisions and build solutions. So I was very adamant about the value of this. And so you could find data sets about city services. We had data for decades about library data. Like, what were people borrowing? Not people's names, some of this is anonymized, but things like what in 1998, what books were most popular? I don't know why you'd want to know that, but it's. You can. All I can tell you is you can know that if you want to know that. Right. But mostly we wanted people to be able to, first of all, recognize that we were being transparent. You could have any data you wanted. And so we're nothing to hide. If you want the budget data, we're going to give you the budget data. I've got to tell you this quick story. When we were building this, we didn't start with 100 data sets. The plan was, let's start with two or three really easy ones, because it gets complicated. You got to vet the data, you got to clean it up, you got to make sure it's good quality. So we asked the community, we asked a group of community members, we. What data is the most important data that you want to see? What do you think? They said, anybody know? What do you think? The city of Palo Alto, what did the community want to know the data they wanted the most about? Yes. Actually budget, but salaries in particular. Yes. They wanted to know how much all the employees of the city were being paid. Very good. But they wanted to know more broadly, how were we spending money? Like, not any. Well, I can't come up with another thing, but it seems like this is the thing they were most concerned, and rightly so. I mean, it is taxpayer money. We're accountable to that. And they want to know how we're spending it. So I made it known that we were one of the first things we were going to make available on arpa. Data was going to be the most current salary information of all employees, including mine. So all of us as government Officials in the us There is no privacy. That's something you give up. I know this only too well, unfortunately, again, it's like if you buy into it, you want to be part of it. You want to do something very unique because public service is really unique and important. You also give up something doing that. You don't have to do it for your whole life. You can do for a few years, make a difference, and then go into the private sector. A bit like what I did. So you can imagine that. Who's interested in the salaries? The newspapers, they want to create stories. This is great in a small community. It's a great thing to say, look how much he's earning. Wow. There's such a big difference between those two people. They're doing the same job. So the newspapers hear that we're going to make the salary information available and we have a relationship. It's like a little bit of a soap opera in a community. When you're at City hall, it's like you're being watched and they write about you the next day. You don't want to do anything. You want to be really clean and what you say and how you behave. Otherwise, it's a story on the front of a newspaper. It's a good test, actually, what I'm going to do. How would that look in the front of a newspaper? We called it. We actually called it the Wall Street Journal test. You never want to be on the front cover of the Wall Street Journal. But Palo Alto Weekly or the newspaper in the community, Palo Alto Post, they were actually Palo Alto Weekly. I was talking to the guy who every year sort of would have this tug of war with the city, like, we need your salaries. And before I joined, they would send it over physically, like in paper form. And I was like, oh, God, you know. And then eventually it sent a PDFs. And this is before we had all these cool tools for PDFs. And I said, no, we're going to give you it in any format you want. You know, it's going to be in basic raw data format. You can download it as a PDF, as a comma, delimited file, xml, whatever version you like. You can download it and you can sort it. They're like, whatever. You're not going to do that. I said, okay, okay. So we did it. We took the salaries, we put them on this thing and we sent a note, a press release to all the newspapers and said all the government employee salaries are now online. So about a day later, I'm walking down the street and here comes the journalist. And I said, well, what do you think he said? Well, he actually said, I was surprised. And he had this sort of, you know, gloomy look to him. I thought he was going to be critical. I thought he was going to say something. You know, it's so disappointing, whatever he says, you know, I was really surprised. It was much better than I thought you were going to do. You exceeded expectations on this, right? And I'm like, oh, that's exactly what I want to hear. That's awesome. That's exactly what we're trying to achieve. Get rid of the tension between the community and city hall, create more transparency. Let's go and do stuff that matters. So it worked out really, really well. When we put the data in, companies started to build solutions. Not us, we didn't spend any money. Third party businesses started creating businesses. This one's called Civic Insight. And what does it do? It actually makes it easy for anyone in the city of Palo Alto to be able to find out what's happening in their neighborhood. Now, the underlying data here is permit data. Permit data is open in the United States. It's not a secret, right? But we never had a way for people to easily see it and understand it. What status is it at, what people could click on it, they could see pictures, all the things they wanted to do. We didn't build it, the community wanted it. A third party built it and created business. Now, when they created a business, they created jobs. When they created jobs, they created economic activity. So making city data available actually generated economic, positive change. This is a quite a big deal. They could then do this for lots of cities all over the United States. And if they wanted, they could go abroad too. So that's decisions, that's about making data valuable, making it available, searchable, queryable, all that sort of stuff, right? And you have to have some policies that drive this. You got to write those. A little bit of bureaucracy. Don't overdo it because you don't want the bureaucracy to kill the thing you're trying to achieve. Like the positive change you're trying to achieve. It's going to be right, it's going to be sized right. And we're going to be having a little conversation in a few minutes where we might talk about this. You don't apply the same data governance structure and hiring and tools in a little organization than you would with a Fortune 500 company, right? You make it the right size and you operationalize it. A data governance work piece of work is not a project It's a program, it's something that will exist indefinitely. Just how your business will run, right? The choices you'll make about how you collect data, how you protect data, who can use data, who can access data, how do you ensure that it's good quality? These are really important questions. You have to ask about that abundance that you're getting. And you ask the question, then, okay, you know, a leader is probably going to reluctantly give you permission, sort of, or approve it. They're going to say, okay, Jonathan, you made the case, right? We're going to invest a little bit. You have to show results, right? Otherwise people think this is just bureaucracy. Otherwise they think you're just putting a layer of just something else I got to care about, like, can I forward this data? Am I allowed to access this data? How do I know? Who do I need to check with to make sure this data is good? And what are the rules governing that? No, you want to do it in a way that it makes sense and that you tell the people of the organization, here are the results we're getting. We're increasing revenue, we're innovating at a faster rate, and here's examples. You've got to demonstrate that value in order that people will buy into it, be part of, part of this journey that the organization is going to take. Governments need to begin to think about data as a strategic asset, the most important asset. And they need to put in place strategy, governance and management if they want to get results. And once they start to put that in place, they need to measure it and they need to show results. This is really, really key. Wait too long, people will check out, they won't buy into it, and you won't get additional investment to see additional results. Now, as I kind of come to an end here, I did want to share one big government example for you. I have a love affair with New Zealand. Anyone that knows me, I went there a few years ago and you know, I visit a lot of places. I'm very lucky to visit the great places in the world. And I finally get an invitation to New Zealand and it's going to be, I'm going to do a talk at a conference. And I arrive and do my talk and I added a couple extra days so I could drive a little bit around the North Island. And then my plan was like, I'd be there a total of four days, whatever, leave back to the U.S. so I do great talk, the talk goes well, I do a little drive. I'm like, wow, this is just, this is Such a beautiful place, Place, love the coffee. And so I get back to the airport, I get the rental car back in, I go up to the gate finally, and I'm sitting at the gate and I didn't want to leave. First time that ever happened, I was like, I want to stay. Something had happened. I sort of kind of worked it. I don't share this with a lot of people, but I'll share it with you between us friends and the 10,000 people who are going to watch this YouTube video. No, I tell you this, I have a background. I was born in Ireland, actually, believe it or not, before I emigrated to the US and became a US citizen. And when I came to, I lived in Florida for most of my life. And then I moved to California. And I said, when I was in California, you know, now that I'm here, I gotta go to Hawaii because I'd seen all the Elvis Presley movies, right? And it was magical. I grew up this idea, Hawaii is paradise. And never, you know, I didn't think in my lifetime I'd ever go to Hawaii. But finally you go to Hawaii and if you. Well, you live in an amazing area here. But for me, this was like a paradise. This is like, wow. I couldn't believe what I just landed into, you know, palm trees and beaches and just lovely. Later on, I'm in New Zealand and I'm thinking, why do I love it here so much? Because in New Zealand there are mountains and lakes, like in Ireland, there are palm trees like in Hawaii. I realized that if Ireland, sorry, if Ireland and Hawaii had a child, it would be, it would be New Zealand. So way to mess up a joke. But you got the spirit of it right, if they had a child. Because it was two of the things I most loved. The green and the. And the countryside of Ireland and the palm trees and sandy beaches of Hawaii. So I fell in love with New Zealand. I went off on a real interesting detour there. That's okay, I'll come back and so I'll go again and you know, another few days, it's not long enough. I said, I gotta stay, I gotta stay for a while. So last year, actually I ended up teaching at the Victoria University in Wellington, the capital, for six weeks. And I got to know the country really, really well. Toured extensively in north and south island, and I loved it even more. So I have this affinity with New Zealand. That's just a kind of a little story about me. But let me tell you about their data governance. So this is something I discovered because I am There, and I'm talking about these topics, doing similar talks to how I'm doing the talk with you today. And it turns out that New Zealand, as a federal government, as a national government, they call it national, I think they decided to embark on a data governance journey. They decided that here we are, a national government, right? Not the local governments, a national government for the whole country, based out of Wellington. They have lots of departments, just like you have here in a big government. And they were all silos, they were doing good work, but they were not sharing data, they were not innovating, really. Departments weren't talking to each other. There was a lot of issues that they wanted to resolve. They wanted to run a better government, they wanted to run a better national government. And they decided the way they can do this is through a data governance strategy, a great data governance framework. That's a very big deal. Of all the ways they could solve these big problems, they decided it would be data governance, that would be the way that they would do it. And so they started small and started to build this program and they started to see results. Departments started talking to each other. There was greater collaboration, more transparency, more innovation. All the things that you would want to see, we started to see. So they started to get some good results. Of course, it wasn't without bumps and it wasn't without investment and some training, all the things you got to do, but they started to see results. But I want to kind of actually drill into something very specific. In New Zealand, there are indigenous people. They're called the Maori people, right? The Maori people. And, well, they have a very different way of thinking about the world and interacting with the world. A very beautiful way, very different from what I'm used to as someone who grew up in a Western culture in Europe and in the United States. They think they have a belief. And no expert on this, third or fourth party on this is things have spirits, right? Objects have spirits and you have to acknowledge them. You have to treat things in a certain way. They also think that data has a spirit. This is incredible. Not that I need to fully understand that, but I have to respect and appreciate it. So if data has a spirit, it needs to be treated a certain way. So when they built this data governance program, they factored in how they would treat Maori data, which is a big part of the population, in a very unique way that respected their culture. That's a huge win for building bridges and bringing those communities together in a very positive way. So the New Zealand data governance framework not only improved the functioning of their national government. But it also brought the communities, the indigenous and the non indigenous communities much closer together in a respectful way. Great story in my view. So as I come to the end of my talk here today, what do we need to do? What are the next steps? Well, we need to educate leaders, government leaders. They're not thinking enough about this, they're not doing enough about this. They have an incredible resource at city hall, at their federal agencies, they have an incredible resource and if they just prioritize it, they have a strategy, they govern and manage it. It's a game changing proposition. We can deliver government in a much superior way without massive investment, but investing gradually over time and as you get results, communicating the results. So we need to prioritize in our cities and in our governments, a data driven culture. So I wanted to just remind you if you are interested in this topic, I just spoke for a little less than just about an hour. There's a lot more to say in this topic if it's interesting to you. I have a ton of videos and books and all sorts of different articles and things that you can access many free through rykanda.com learn and I hope you will take advantage of it and enjoy it and learn and, and if you are interested in this topic, go deep in it and perhaps it might be a career for one of you. It might be a change of direction for somebody on YouTube or Zoom. And with that I just want to say great, thank you very much for your amazing attention and for being here. Thank you.
Thank you Dr. Rational, for that very interesting lecture. I would now like to invite on stage the moderator for the evening, Dr. Alvin Shaw. Dr. Alvin heads the data science. Sorry, yeah. Dr. Alvin heads the data Science practice at the URA Design and Planning Lab. His portfolio is concerned with the creation of digital capabilities and strategic data sets through research and cross organizational partnerships. This includes planning and coordination of R and D projects funded by national research programs as well as in house research and technology development projects. Dr. Alvin holds a PhD in engineering from KU Leuven in Belgium, as well as a Master's of Design from Nassal College of Arts in Singapore. His scientific and technical interests are in the applications of artificial intelligence, geospatial modeling and simulation to urban planning, in particular related to integrated land use and transport planning. Please join me in welcoming him on stage.
Are we going to sit here?
We now invite the audience in the auditorium as well as on Zoom to raise their questions. Joshua. And we will be handing the mics up.
So.
Yeah.
Wow.
Okay. Right. Thank you never mind about your questions. Yeah, sorry, sorry.
Please, please go ahead.
Right.
No, I just want to say a few things. Right. That was a great lecture.
Thank you.
It really gives us some overview of how much has changed since I think technology started to infiltrate into the city and urban space.
Right, yeah.
Incidentally, you begin your lecture with streets. I began my research career looking at streets too. Back in the day, there was a platform called Fix My street, which you might have heard of, and it revolved around this data set. So yeah, I suppose everything converges on one point. Could have been there. But I hope we have a good conversation following the questions that are very eager audience have.
Sure.
Please go ahead.
Thank you.
Thank you.
We know that good quality data takes time and is expensive to collect.
Nowadays we are surrounded by cheap sensors. You can monitor whatever you want, you
can collect bunch of data.
Sensors are becoming very cheap and citizens,
laymen, lay people won't know how to
detect which is bad and which is good data.
Could you elaborate a bit about it? So the assumption is that the government is collecting it and then going to make it available because if it's an individual, of course we have no control over that. Is that the assumption? Correct. But nowadays people are collecting the data. Yes.
And not only authorities.
Yes.
People usually feel that their data are better than the data collected by authorities.
Yes. They need to buy my book and then they would get the skills to be able to manage it. I don't know that there's much we can do if a person collects data and makes decisions from it. What I can say about the city, the cities I've worked with, is I certainly would never advise that we collect sensor data and just publish it. There has to be a vetting process and it shouldn't be highly bureaucratic or like a big showstopper, but it should be sufficient to make sure that the quality is good, that it is good data, that it is not sensitive data, you know, or regulated data. So the way that we did it in Palo Alto is we had a structure and we had certain people like the city attorney would look at it, you know, our emergency services director would look at it. I would look at it in terms of, you know, from the computer science perspective and then ultimately we would publish it. So we had that vetting and it served us well. It serves communities well.
Thank you. We have a question from the back or two. Thank you.
Okay. Hi.
Hello.
So my name is May. I'm a student. Master. I'm a master student in international affairs. And so I'm coming Kind of from more of a security defense background. So my question would be more on what are the concerns that we should, you know, look out for as policymakers when it comes to cybersecurity and open data cities? And what are the drawbacks that some cities, administration could think of when, you know, being asked to become an open data city? Because when thinking about it, all this data is then accessible by so many people that could have good and bad intentions. And I mean, cyber warfare and cybersecurity is such a prevalent topic nowadays.
Sure. Thank you. Thank you for the question. In many ways, sort of the data we protect and open data are the opposite of each other. Right. So open data is stuff you don't care like, it's owned by the community, it's freely accessible, will trust people to do the right thing. We know that bad people will do bad things, but mostly people will try to do the right thing. What I can say, though, is governments are not investing sufficiently in cybersecurity, and we far too many examples of that. We had a number of cities in the US that had ransomware attacks. Baltimore and Atlanta, two big major American cities, shut down for days, in some cases a couple of weeks. So we need to spend more money. We need to hire more cybersecurity professionals in cities. When I joined the city of Palo Alto, my first executive position, my first executive decision was to hire a security manager. Now, you might say the city didn't have security. Cybersecurity manager. No, they didn't. In fact, it was very, very low priority. 2011, it's a little while ago. It's much more serious today. The funny thing is, when I kind of challenged people, why don't we have a cybersecurity professional? They said, we're not a big target. We're not being attacked. And my response at the time was, you don't know that we're not being attacked because we're not monitoring. We actually don't have the tools in place or the people the expertise to know that. And actually, when we did start to install monitoring software, the city was very frequently a subject of attack. So I think the answer for you is, you know, the best answer I can give you. And I say this to city leaders right to the face. You got a budget, you got to invest much more in cybersecurity because this problem is not going away, and it's getting a lot more complicated. Yeah, maybe next. Oh, there's. There. We have one more.
Hi, I'm looking at the. The city level. A lot of municipalities, local Governments really don't have big budgets so I imagine trying to get them to embark on this data journey is quite a big ask and they really have to look at it quite hard nosed way. So do you have any areas in your experience that you found is kind of the low hanging fruit where local governments can not necessarily get efficiency or innovation improvements but actually cost effectiveness in what they're doing? That might be a first place to look?
Well, they got to do something like doing nothing is not, I don't think is a good strategy at all. I mean we'll go back to something simple. Can people find data? Like if you don't even know what data exists and where it is and how to access it, I mean not everything else is much more difficult. So that's a nice entry point. It's also important that like a city that's, you know, again is very limited on budget. Start small starts with, with data. That's really important. An open data platform say, I mean there's many open source ones in fact, it's not expensive to do and it's not hard to do it either. So rather than, you know, the, the city hiring people and building a complicated tool, whatever, do something simple and then, and then share it with the world and then you'd be amazed that the innovation will come. But cyber security clearly is something that they have to put funds aside to. There's a major cyber attack, there'll be a lot of regret later. You know, it's, it's like, you know, you think you don't need insurance, but when something happens you're happy that you have insurance. The, the last maybe example that I, I could give you that doesn't require really any investment but has a big impact is something called data ownership. And I'll tell you just, it's very funny because I talk about this topic with business executives all over the world and I will often technology executives and I'll say to them, do you have someone who comes to work every day and worries about the important data sets in your business? Like is there five or six people that are worrying and they all put their head down, they don't want to answer the question because the answer is no. We have a very poor understanding of what even data ownership means. If it's your most important asset, somebody needs to care about it. Somebody needs to come to work in the morning and say is it secure? Is it high quality, is it complete, is it accurate? And be accountable to that. So that's more of a training and responsibility action that a City can take with low cost, but big impact.
Okay.
Yeah.
Since it's a dialogue, it's my turn to add.
This is your show. Yeah, yeah.
You speak a lot about this idea of data governance as a culture.
Right.
And I think this is a very fresh insight into this whole idea of being data driven. The challenge is, you know, it's a new concept and it's not going to be a simple undertaking. This actually involves very long term commitment for any organization and for cities in particular, city governments, it's fairly foreign. So what do you think is a realistic starting point to even conceptualize the build up of data governance as a culture? Do you think it should be people, processes, or from another perspective, perhaps you can share some views.
Yeah, yeah, no, it's really important. Part of the answer is a little bit repetitive of the one I just gave in terms of some entry points, but I do think there's an education part too, and I think you're getting to that. City managers don't necessarily come to work and worry about this, but city administrators, city managers need to know about this topic and the team needs to know about it too. The executive team. There's an education part as, as organizations that help to educate governments, whether it's academic or like an association, the International City Managers association or the League of Cities in the United States. Some of the very formal, well respected organizations need to be talking about this because these are mayors and our city managers go to these events. They need to hear many of the things I've been saying and have good examples of where it could be very, very beneficial, both in being able to deliver a better experience to the community, but also reducing the risk of a more digital, hyper connected community. So I think the education is the entry point and then start small, just choosing a few data sets, showing some value and then building from there.
Great. Just to add on to this.
Right.
I think one of the things about data and the whole boom of the industry is that people, when they first are introduced to this concept of data, are more excited with the cool and fancy things you can do with it. As you rightly pointed out, essentially build applications, do analysis, but when it comes down to things like management, things like quality assessment, these things are often seen to be more mature. Right. How can we reframe this, in your opinion, at least for, I mean, management would probably see the value of this, but for, you know, the data worker, how can we position this to be an attractive career prospect?
Yeah, yeah. Well, one thing that comes to mind immediately is in a, in a Strong, high performing data culture. Everybody has a role, right? It's not just people with, you know, not, certainly not people just in the IT department, certainly not a leadership thing. But everybody has some sort of role and they should understand what that, what that role is and we should provide again, whether it's through whatever your system of communications is, whether it's a newsletter or an email distribution, maybe from the leader, what actual value was delivered through the work they're doing. I think also the staff member needs to know the essential nature of cybersecurity and how it's protecting the city, protecting the organization by the actions people, the people are taking. You need to explain to people why they're doing something and then they need to show the results of, of their good work. And then maybe the last point to say is this will take time is, you know, responsibilities need to be baked into their job description. You, you don't get to not, you don't get to not do data. You know, it's like, it's like you don't get to not do email or, you know, to use a computer system. You're part of the 21st century, you're part of this organization. Data literacy is going to be part of what is required from you.
Great, great. I wonder if anyone has any.
Yep.
Hi, good Evening, I'm Kritika, PhD, candidate working on governance of smart cities.
Oh, look at that.
Dibs on the book though. So I had a question on actually crowdsource data. So a lot of discussion was made on just for crowdsourcing data today, either through tournaments or open sources or I don't think you mentioned virtual labor markets, for example, if you're paying somebody to collect the data. So I'm curious to know, how does that translate to policymaking? So crowdsourced policymaking effectively, or any pitfalls associated with it. And is there a type of crowdsourced data which is more suitable for policy design? A method which is method of collecting that crowdsource data which is more suitable for policy design from your experience?
Yeah, thank you for the question. And yes, you will get a copy of the book because you asked for it. It's nice. And this is your topic, so absolutely. No, look, I think part of the answer is to be candid, to say this is not an expertise area of mine, so I don't have a great answer for you, but I will say this. One of the areas that I have seen is participatory budgeting. Right. Which you are probably familiar with a lot of good work being done in it, some places it's still experimental, some places that's how they do sort of the first phase of any type of budgeting. And just to kind of give you a little explanation on that is, you know, I was part of this twice a year for seven years. The budgeting cycle in XCITI is rigorous and exhausting. And one of the things that community wants in the United States. A lot of communities want much more input into decisions way beyond just voting every four years. They want to actually be more active in day to day operations. We try to find ways to bring people in to the conversation to be part of decision making. There's this idea that you bring together people that are interested. We say we have $500 million, we have all these issues, how would you spend the money? And we sort of have this dialogue, break into teams and people make different propositions and then it all has to reconcile because you don't have more money and you have to kind of gain some consensus. So it's kind of this sometimes a day long, sometimes multi day. And that doesn't become the ultimate budget, but it is a very well respected input into the ultimate budget that's, that's made. So it's, it's showing good results. And some communities are very comfortable with it, others are experimenting, others have avoided it. You'd have to kind of almost like look at a community that succeeded and figure out what did they do right. And why it was the approach that was suitable for them.
Okay, I have a question from the audience online.
Oh, wow.
And I think this one is particularly interesting and timely. So I'm going to just state that we'll try and answer this live. We are in the age of AI and we have seen what AI is capable of. So Mario, who is tuning in, is interested to know how do we know if data is synthetic or fake or accurate? And since AI is now able to dump data faster than humans, what should we do and what are your opinions on this?
I'm trying to understand the question fully. So we obviously we are collecting data as a government, so we have a little bit more control over that and we're vetting it and we're, you know, there's a level of diligence that we're applying to it. I mean, the bigger question is because AI clearly can create fake information, hallucinations and so one of the ways that we're thinking that we may begin to solve this is through the notion of a watermark, right? Something that certifies that the data is authentic and real. And in fact this is part of much of the proposed regulation around the world for AI is that, for example, an image would have a watermark in it, something that's detectable, computationally detectable, not visually detectable, that you would know that it actually has authenticity to it. Whether this provides us with a good solution is to be determined. Many of the tough questions in AI aren't answerable right this second here in February 2024. And in fact, the bigger questions, the more complicated ones that are going to come. There are more complicated ones that are going to come that we will have to grapple with as a society and make a decision.
I think personally the issues will arise when we crowdsource such information and we can't distinguish what's synthetic and what's the real stuff that people's giving us.
Right.
And when we are building tools to do ground sensing, and part of that ground sensing is in information that's created by people in response to us. Yeah, that's where the risk is. We don't know who's giving what to us. And I think the need to verify that information, it's going to be quite important. Right. So yes, I hope, Mario, we did respond to your question. You left a very long list of questions. But I've got to moderate this, so perhaps you can take one from the audience.
Hi, I'm Valen. Thanks for the lecture. I have a question on the data driven culture. As we push for this culture to be, to have in the organization, it seems to be, it seems there is going to be additional workload, mental, physical. How do we, and how do they push for a smoother transition for maybe people on the ground level to transition to this digital driven, data driven culture?
Sorry, I didn't quite understand it.
My question is mainly, it seems like there's more work to be done at this on the ground level. Like maybe for the workers, they have to collect data to log in the data and also maybe data literacy and this is additional work on top of their own job scope. So how can we enable a smoother transition to a data driven culture in let's say organizations like companies or MNCs?
Okay, okay, thank you. Thank you. Well, gradually is the answer. You can't, it's been, we know over time that you can't mandate a data culture or you can't actually mandate a culture. Cultures are fostered. They are created through strong vision, strong leadership incentives. And so you got to create the right conditions and recognize that every organization is going to transition to this if indeed they ever do at their own rate. Right now, if you're a new organization, you start on day one. We're going to be data driven. That's something else. But most organizations, they've been around for a while, cities have been around for a long time and they're introducing something new. So I think it has to be done consistent with the existing culture, done over time. Learn what's working, what's not working, stop what's not working, continue what is working, and start some new things. Finally, I love that you brought up data literacy.
Right.
This is a key part here. We haven't really as a society yet recognized that in addition to reading and writing and mathematics, that data literacy is probably another core skill required to succeed in a 21st century economy. So people do need to acquire skills they don't have. And in universities and schools, greater emphasis needs to be placed on learning data literacy. Any others? Oh,
wow.
Good questions.
Yeah, good questions. And it keeps coming. I'm surprised. I prepared a whole list, but I probably will not ask them.
Yep, thank you very much for the lecture. The rankor. So I just wanted to ask about with regards to changing mindsets in terms of, you know, data governance and the importance of data and I mean, you gave several examples from the us, from New Zealand, things like that. I suppose in the Singapore context it's a little different because, yeah, maybe a little controversial, but our government's a bit slow to this kind of mindset of providing information and we don't really have the culture of, you know, freedom of information acts that you have in the US or other countries. So what would you say is a good way to change this mindset in a very like where the Singapore government says that you don't really need to know this data and so we don't really need to provide this data for you.
That is deep.
I travel enough to know not to answer that question. No, I won't answer that question because I don't think it's appropriate for me to. But I mean, yes, I put a lot of emphasis on opening data and making it available to others. But let's not lose sight of the fact that typically data governance is largely done behind the scenes. It's done within the organization. So the question might be, or the assessment might be, to what degree is data governed within Singaporean enterprises and government entities? Is a government entity within Singapore getting the value they think they should from certain data sets to deliver more innovation? Is it sufficiently secured and is it accessible by the people that should have access to it? So this is sort of maybe a, a kind of a. First of all recognizing that this has value, but also making an assessment of where you're at and figuring out where that gap might be to get you to a better, to a better place. So, you know, a lot of data governance, by the way, lives and exists in the private sector, right. We now see it emerging in a, in a significant way in the public sector and within academic organizations too.
What are your thoughts on, you know, benchmarking the effectiveness of data governance in an organization and perhaps at another level, trying to assess the maturity of that culture within the organization? Do you have some perspectives on this?
Yes. In fact, maybe one of the, you know, early steps you take in determining, like if you want to change your environment and you believe there's a more positive way to do something, you need to understand where you're at and then sort of figure out the journey to get from where you're at to where you want to be. So, you know, so you've got metrics, right? I actually wrote down a couple of examples of metrics here if I can put my finger on them. Maybe not. So, yes, okay. Things. Well, look at the heart of all of this is data quality, right? So data quality are things like how complete is a data set, how many errors are in it, when was it last updated, how many people access it, that should access it. So you start to see quite a lot of neat metrics. You can benchmark that around your, your data quality. Then how many issues occur in the organization that are data related? Like issues that happen, like, for example, with system integration, you know, there's these. In many reasonably complex organizations, you have batches, batch batch processes that move data and they transform it and they do cool things with it. They sometimes fail. Right. And that is often a reflection of, you know, data management and data governance. So if you're getting good quality data, moving that data, you have good processes in place, you should see a drop in the area. Also things like person logging, like I need access to this data, I should have access to, but I don't have access to it, or I did have access to it, I don't have access to it anymore. Tickets like that are another way to actually validate. What was the second part of the question after the benchmark?
Well, it's people aspect, right? How do you recognize that an organization has matured? Or maybe we can ask the question in anecdotal way. Are you able to recognize organizations that are at their infancy versus one that's perhaps more mature yeah,
Some of it will be obvious like, do you have data owners? Do you have policies around data? Do you have a chief information security officer? Some things you could, in five minutes you could determine if this is a more mature organization than one that maybe hasn't really been putting enough emphasis on it. So those are some of the initial ways to determine. Maturity is also where you, if you're able to sit in on meetings or listen to how people speak, there's a language that's used in a data driven culture. People ask certain questions a certain way. They say, you know, what's the evidence for that? Or could I see the report on that? And they know how to use tableau. They know how to use some of the cool features of Excel. Right. We still do a lot of the world's decision making and financials on Microsoft Excel. A lot of the world runs on spreadsheets. So we could make an assessment of tool usage and sophistication on the tool usage. We do have to train our people. We, you know, we do. There should be no expectation that a person can fully embrace a data culture and be comfortable with it on day one. You know, I'll tell you this quick anecdotal story has to do with project management. I had a staff member a few years ago who every time their job was project manager and they would do a project and after a few months I would check in, how's the project going? And invariably the project was late, wasn't going in the right direction, they were spending more money, whatever. And I'd give him some advice, he'd get some advice from his manager and he'd do another project. And again, we saw these issues and we're like, something is not right here. You're not succeeding as a project manager. And then I had the thought to ask him, have you ever been trained in project management? He said no. He had no formal training in project management. So here I was, I didn't hire him, I just, I came to the organization. He's part of my team here. He was, I was giving him the responsibility of leading important projects and he was failing because we hadn't trained him. So I said, stop, no more projects. We're going to train you. And we sent him to project management school. He got his project management professional certification as PMP and he became one of our best project managers. And his life was better, the projects were better. So we shouldn't expect people to be superstars in work if we haven't invested in them and given the skills to do the work. And that applies to data, of course. Yeah.
Yeah. Those are really great insights. I wonder if we have other questions.
We do, but he's at the mic.
Oh, is there someone in the back.
If you want to keep the conversation going?
Hi. If I'm making a plug, my title is actually data Governance as of like three weeks ago, so.
Nice. With who?
The.
The Office of Data and Intelligence within nus.
Amazing.
Yeah, isn't it? So they. They took me out of the. The R and D kind of thing, and now they're. They're escalating up to, you know, management level. It's kind of cool. So I'm kind of finding my way here, but nice. The. Thank you for showing that. The green Stanford campus.
It's wonderful.
But I'm gonna connect you back to
Alvin
when he said he used your data.
Right.
How do you surface that kind of thing? So you release data to the world, you release data to the organization, but you then want to know downstream usage, probably not just from the unicorns that could build it into an app and then surface it up to the city. So what's the strategy for that kind of bubbling up or at least getting a pulse of that kind of.
Of thing?
Thank you for that. Well, congratulations on your new work. And that's. I'm. I'm so happy you're here. Hopefully I said something that was accurate tonight, you'd be able to validate it. We were always curious. It's a great question. We were always curious, what were people doing with our data? And I'll give you the candid answer. We didn't know. Right. And so the question is, do we need to know? You know? Yeah, I think you're saying it. We eventually determine that hopefully there's people getting value from it, and then if we discover it or if they let us know, we're very happy. But we don't need to know what they're doing. You don't need to know what others are doing with your data unless it's nefarious and comes back to haunt you at some point, but it typically doesn't. And most people try to do the right thing. I was always just fascinated when I would be maybe watching a YouTube video, whatever, and suddenly there would be somebody who would mention our data. And I'm like, wow, it's been used in Melbourne, Australia, and I didn't know for a certain reason. My hope, really, if it's used beyond the region that it's applicable to, in this case Silicon Valley, we're inspiring somebody. We're actually inspiring this kind of work in other places in the world.
Cool. Great. Okay. Maybe I will end this with a final question.
Okay. Yeah.
And this is, this is something that I thought may come to the minds of people in management. And it's about, you know, the cost involved in doing all of this. Creating new jobs, new processes, people spend time and training, infrastructure, all new things. Right. So I suppose, you know, you must have dealt with all this friction within an organization like a city, right?
Yeah.
What was your approach and can you share some experience on how do you start to convince people to accept the buildup of such processes and roles?
You know, it's the number one response that most leaders have to this topic. When you have explained what it is, they get it and then they say it sounds very expensive and it sounds bureaucratic and we have other things to do. And in a private company, you know, in a for profit business, I should say everything is focused on let's make money, you know, and anything that seems like an overhead or bureaucracy is not very attractive. So when you hear certain people talk about data governance, they will talk about policies and they will talk about risk. And these are not necessarily exciting things for leaders. I tend to talk about this topic through the lens of innovation, actually. That data allows us to make better decisions and to innovate faster, to understand our customers, understand our communities. So really focusing much more on the value part of it rather than the sort of, the more kind of behind the scenes, like insurance, you got to kind of do that because it's important. But we don't really want to talk about it. But we want to. If you're a leader, if you're a city manager or you're a CEO, I want to convince you that this incremental investment over time will start to actually bring enormous value. Remember early in my slides I said there's a direct link between data governance and company performance. Direct. This is not a gray area. When data governance is done positively in a really right way, companies make more money, they have higher retention of staff, they innovate more greatly and they reduce their cybersecurity events. They do reduce risk. So I think for the reluctant organization, super focusing on value and innovation, you know, you got to make mention of the protection, the kind of risk part of it too. That's, that's really key. And then it's like, let's not spend a lot of money, but we do need to assign some people with some responsibilities. Not full time, part of a job. We've got to do things like, you know, data ownership, data stewardship, these are kind of the roles that need to be in place. Some, what's called a data governance board, you know, some sort of committee entity that actually has oversight for this. So there's a bit of a talent part, the people part to it and then, you know, you've got to understand the data, you have the valuable data. Not all data is equal. We know. I think that's an obvious statement, right? So in any data governance environment, you're focusing on high value data. Typically there's going to be a lot of data. You just, it is what it is, just forget it. What you want to do is really focus on what's important to you. So that's actually going to be a subset, so you're more laser focused on that. And then there's going to be some, a bit of a, bit of a, kind of let's take a leap and start to invest in some tools, you know, some better analytics, you know, better reporting and let's start to see, you know, can, can we drive, you know, our strategy, our vision in a way that data governance and data management, good data management can support? So yeah, there's a diplomacy to this and then there's just on the ground facts, right? You know, these Baltimore and Atlanta and the other cities around the world that were really damaged financially and reputationally and also weren't able to deliver services. How do other cities avoid that? They got to do some smart moves and one of them is begin to think of what does data governance look like in their organization.
Great.
Yeah.
Oh, wow.
Please, very short.
I have to ask selfishly, can I have the second copy of the book?
You can. That solved the problem. Thank you very much. Yeah, yeah, totally.
And I hope everyone is happy with the evening.
Yes. Is that it? We're concluding. Thank you very much.
Thank you.
Thank you.
Thank you, Jonathan.
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