Many of the latest technologies -- such as Internet of Things (IoT) platforms, big data analytics, and cloud computing -- are making data-driven and efficiency-focused digital transformation more powerful. But exploiting these advances to improve municipal services for cities and urban government agencies face unique obstacles. Challenges range from a lack of common data sharing frameworks, to immature governance over multi-agency projects, to the need to find investment funding amid tight public sector budgets.
The good news is that architectural framework methods, extended enterprise knowledge sharing, and common specifying and purchasing approaches have solved many similar issues in other domains.
BriefingsDirect recently sat down with a panel to explore how The Open Group is ambitiously seeking to improve the impact of smart cities initiatives by implementing what works organizationally among the most complex projects.
The panel consists of Dr. Chris Harding, Chief Executive Officer atLacibus; Dr. Pallab Saha, Chief Architect at The Open Group; Don Brancato, Chief Strategy Architect at Boeing; Don Sunderland, Deputy Commissioner, Data Management and Integration, New York City Department of IT and Telecommunications, and Dr. Anders Lisdorf, Enterprise Architect for Data Services for the City of New York. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: Chris, why are urban and regional government projects different from other complex digital transformation initiatives?
Harding: Municipal projects have both differences and similarities compared with corporate enterprise projects. The most fundamental difference is in the motivation. If you are in a commercial enterprise, your bottom line motivation is money, to make a profit and a return on investment for the shareholders. If you are in a municipality, your chief driving force should be the good of the citizens -- and money is just a means to achieving that end.
This is bound to affect the ways one approaches problems and solves problems. A lot of the underlying issues are the same as corporate enterprises face.
Bottom-up blueprint approach
Brancato: Within big companies we expect that the chief executive officer (CEO) leads from the top of a hierarchy that looks like a triangle. This CEO can do a cause-and-effect analysis by looking at instrumentation, global markets, drivers, and so on to affect strategy. And what an organization will do is then top-down.
In a city, often it’s the voters, the masses of people, who empower the leaders. And the triangle goes upside down. The flat part of the triangle is now on the top. This is where the voters are. And so it’s not simply making the city a mirror of our big corporations. We have to deliver value differently.
There are three levels to that. One is instrumentation, so installing sensors and delivering data. Second is data crunching, the ability to turn the data into meaningful information. And lastly, urban informatics that tie back to the voters, who then keep the leaders in power. We have to observe these in order to understand the smart city.
Saha: Two things make smart city projects more complex. First, typically large countries have multilevel governments. One at the federal level, another at a provincial or state level, and then city-level government, too.
This creates complexity because cities have to align to the state they belong to, and also to the national level. Digital transformation initiatives and architecture-led initiatives need to help.
Secondly, in many countries around the world, cities are typically headed by mayors who have merely ceremonial positions. They have very little authority in how the city runs, because the city may belong to a state and the state might have a chief minister or a premier, for example. And at the national level, you could have a president or a prime minster. This overall governance hierarchy needs to be factored when smart city projects are undertaken.
These two factors bring in complexity and differentiation in how smart city projects are planned and implemented.
Sunderland: I agree with everything that’s been said so far. In the particular case of New York City -- and with a lot of cities in the US -- cities are fairly autonomous. They aren’t bound to the states. They have an opportunity to go in the direction they set.
The problem is, of course, the idea of long-term planning in a political context. Corporations can choose to create multiyear plans and depend on the scale of the products they procure. But within cities, there is a forced changeover of management every few years. Sometimes it’s difficult to implement a meaningful long-term approach. So, they have to be more reactive.
Create demand to drive demand
Driving greater continuity can nonetheless come by creating ongoing demand around the services that smart cities produce. Under [former New York City mayor] Michael Bloomberg, for example, when he launched 311 and nyc.gov, he had a basic philosophy which was, you should implement change that can’t be undone.
If you do something like offer people the ability to reduce 10,000 [city access] phone numbers to three digits, that’s going to be hard to reverse. And the same thing is true if you offer a simple URL, where citizens can go to begin the process of facilitating whatever city services they need.
In like-fashion, you have to come up with a killer app with which you habituate the residents. They then drive demand for further services on the basis of it. But trying to plan delivery of services in the abstract -- without somehow having demand developed by the user base -- is pretty difficult.
By definition, cities and governments have a captive audience. They don’t have to pander to learn their demands. But whereas the private sector goes out of business if they don’t respond to the demands of their client base, that’s not the case in the public sector.
The public sector has to focus on providing products and tools that generate demand, and keep it growing in order to create the political impetus to deliver yet more demand.
Gardner: Anders, it sounds like there is a chicken and an egg here. You want a killer app that draws attention and makes more people call for services. But you have to put in the infrastructure and data frameworks to create that killer app. How does one overcome that chicken-and-egg relationship between required technical resources and highly visible applications?
Lisdorf: The biggest challenge, especially when working in governments, is you don’t have one place to go. You have several different agencies with different agendas and separate preferences for how they like their data and how they like to share it.
This is a challenge for any Enterprise Architecture (EA) because you can’t work from the top-down, you can’t specify your architecture roadmap. You have to pick the ways that it’s convenient to do a project that fit into your larger picture, and so on.
It’s very different working in an enterprise and putting all these data structures in place than in a city government, especially in New York City.
Gardner: Dr. Harding, how can we move past that chicken and egg tension? What needs to change for increasing the capability for technology to be used to its potential early in smart cities initiatives?
Framework for a common foundation
Harding: As Anders brought up, there are lots of different parts of city government responsible for implementing IT systems. They are acting independently and autonomously -- and I suspect that this is actually a problem that cities share with corporate enterprises.
Very large corporate enterprises may have central functions, but often that is small in comparison with the large divisions that it has to coordinate with. Those divisions often act with autonomy. In both cases, the challenge is that you have a set of independent governance domains -- and they need to share data. What’s needed is some kind of framework to allow data sharing to happen.
This framework has to be at two levels. It has to be at a policy level -- and that is going to vary from city to city or from enterprise to enterprise. It also has to be at a technical level. There should be a supporting technical framework that helps the enterprises, or the cities, achieve data sharing between their independent governance domains.
Gardner: Dr. Saha, do you agree that a common data framework approach is a necessary step to improve things?
Saha: Yes, definitely. Having common data standards across different agencies and having a framework to support that interoperability between agencies is a first step. But as Dr. Anders mentioned, it’s not easy to get agencies to collaborate with one another or share data. This is not a technical problem. Obviously, as Chris was saying, we need policy-level integration both vertically and horizontally across different agencies.
Some cities set up urban labs as a proof of concept. You can make assessment on how the demand and supply are aligned.
One way I have seen that work in cities is they set up urban labs. If the city architect thinks they are important for citizens, those services are launched as a proof of concept (POC) in these urban labs. You can then make an assessment on whether the demand and supply are aligned.
Obviously, it is a chicken-and-egg problem. We need to go beyond frameworks and policies to get to where citizens can try out certain services. When I use the word services I am looking at integrated services across different agencies or service providers.
The fundamental principle here for the citizens of the city is that there is no wrong door, he or she can approach any department or any agency of the city and get a service. The citizen, in my view, is approaching the city as a singular authority -- not a specific agency or department of the city.
Gardner: Don Brancato, if citizens in their private lives can, at an e-commerce cloud, order almost anything and have it show up in two days, there might be higher expectations for better city services.
Is that a way for us to get to improvement in smart cities, that people start calling for city and municipal services to be on par with what they can do in the private sector?
Public- and private-sector parity
Brancato: You are exactly right, Dana. That’s what’s driven the do it yourself (DIY) movement. If you use a cell phone at home, for example, you expect that you should be able to integrate that same cell phone in a secure way at work. And so that transitivity is expected. If I can go to Amazon and get a service, why can’t I go to my office or to the city and get a service?
This forms some of the tactical reasons for better using frameworks, to be able to deliver such value. A citizen is going to exercise their displeasure by their vote, or by moving to some other place, and is then no longer working or living there.
Traceability is also important. If I use some service, it’s then traceable to some city strategy, it’s traceable to some data that goes with it. So the traceability model, in its abstract form, is the idea that if I collect data it should trace back to some service. And it allows me to build a body of metrics that show continuously how services are getting better. Because data, after all, is the enablement of the city, and it proves that by demonstrating metrics that show that value.
So, in your e-commerce catalog idea, absolutely, citizens should be able to exercise the catalog. There should be data that shows its value, repeatability, and the reuse of that service for all the participants in the city.
Gardner: Don Sunderland, if citizens perceive a gap between what they can do in the private sector and public -- and if we know a common data framework is important -- why don’t we just legislate a common data framework? Why don’t we just put in place common approaches to IT?
Sunderland: There have been some fairly successful legislative actions vis-à-vis making data available and more common. The Open Data Law, which New York City passed back in 2012, is an excellent example. However, the ability to pass a law does not guarantee the ability to solve the problems to actually execute it.
In the case of the service levels you get on Amazon, that implies a uniformity not only of standards but oftentimes of [hyperscale] platform. And that just doesn’t exist [in the public sector]. In New York City, you have 100 different entities, 50 to 60 of them are agencies providing services. They have built vast legacy IT systems that don’t interoperate. It would take a massive investment to make them interoperate. You still have to have a strategy going forward.
The idea of adopting standards and frameworks is one approach. The idea is you will then grow from there. The idea of creating a law that tries to implement uniformity -- like an Amazon or Facebook can -- would be doomed to failure, because nobody could actually afford to implement it.
Since you can’t do top-down solutions -- even if you pass a law -- the other way is via bottom-up opportunities. Build standards and governance opportunistically around specific centers of interest that arise. You can identify city agencies that begin to understand that they need each other’s data to get their jobs done effectively in this new age. They can then build interconnectivity, governance, and standards from the bottom-up -- as opposed to the top-down.
Gardner: Dr. Harding, when other organizations are siloed, when we can’t force everyone into a common framework or platform, loosely coupled interoperability has come to the rescue. Usually that’s a standardized methodological approach to interoperability. So where are we in terms of gaining increased interoperability in any fashion? And is that part of what The Open Group hopes to accomplish?
Not something to legislate
Harding: It’s certainly part of what The Open Group hopes to accomplish. But Don was absolutely right. It’s not something that you can legislate. Top-down standards have not been very successful, whereas encouraging organic growth and building on opportunities have been successful.
The prime example is the Internet that we all love. It grew organically at a time when governments around the world were trying to legislate for a different technical solution; the Open Systems Interconnection (OSI) model for those that remember it. And that is a fairly common experience. They attempted to say, Well, we know what the standard has to be. We will legislate, and everyone will do it this way.
That often falls on its face. But to pick up on something that is demonstrably working and say, Okay, well, let’s all do it like that, can become a huge success, as indeed the Internet obviously has. And I hope that we can build on that in the sphere of data management.
It’s interesting that Tim Berners-Lee, who is the inventor of the World Wide Web, is now turning his attention to Solid, a personal online datastore, which may represent a solution or standardization in the data area that we need if we are going to have frameworks to help governments and cities organize.
A prime example is the Internet. It grew organically when governments were trying to legislate a solution. That often falls on its face. Better to pick up on something that is working in practice.
Gardner: Dr. Lisdorf, do you agree that the organic approach is the way to go, a thousand roof gardens, and then let the best fruit win the day?
Lisdorf: I think that is the only way to go because, as I said earlier, any top-down sort of way of controlling data initiatives in the city are bound to fail.
Gardner: Let’s look at the cost issues that impact smart cities initiatives. In the private sector, you can rely on an operating expenditure budget (OPEX) and also gain capital expenditures (CAPEX). But what is it about the funding process for governments and smart cities initiatives that can be an added challenge?
How to pay for IT?
Brancato: To echo what Dr. Harding suggested, cost and legacy will drive a funnel to our digital world and force us -- and the vendors -- into a world of interoperability and a common data approach.
Cost and legacy are what compete with transformation within the cities that we work with. What improves that is more interoperability and adoption of data standards. But Don Sunderland has some interesting thoughts on this.
Sunderland: One of the great educations you receive when you work in the public sector, after having worked in the private sector, is that the terms CAPEX and OPEX have quite different meanings in the public sector.
Governments, especially local governments, raise money through the sale of bonds. And within the local government context, CAPEX implies anything that can be funded through the sale of bonds. Usually there is specific legislation around what you are allowed to do with that bond. This is one of those places where we interact strongly with the state, which stipulates specific requirements around what that kind of money can be used for. Traditionally it was for things like building bridges, schools, and fixing highways. Technology infrastructure had been reflected in that, too.
What’s happened is that the CAPEX model has become less usable as we’ve moved to the cloud approach because capital expenditures disappear when you buy services, instead of licenses, on the data center servers that you procure and own.
This creates tension between the new cloud architectures, where most modern data architectures are moving to, and the traditional data center, server-centric licenses, which are more easily funded as capital expenditures.
The rules around CAPEX in the public sector have to evolve to embrace data as an easily identifiable asset [regardless of where it resides]. You can’t say it has no value when there are whole business models being built around the valuation of the data that’s being collected.
There is great hope for us being able to evolve. But for the time being, there is tension between creating the newer beneficial architectures and figuring out how to pay for them. And that comes down to paying for [cloud-based operating models] with bonds, which is politically volatile. What you pay for through operating expenses comes out of the taxes to the people, and that tax is extremely hard to come by and contentious.
So traditionally it’s been a lot easier to build new IT infrastructure and create new projects using capital assets rather than via ongoing expenses directly through taxes.
Gardner: If you can outsource the infrastructure and find a way to pay for it, why won’t municipalities just simply go with the cloud entirely?
Cities in the cloud, but services grounded
Saha: Across the world, many governments -- not just local governments but even state and central governments -- are moving to the cloud. But one thing we have to keep in mind is that at the city level, it is not necessary that all the services be provided by an agency of the city.
It could be a public/private partnership model where the city agency collaborates with a private party who provides part of the service or process. And therefore, the private party is funded, or allowed to raise money, in terms of only what part of service it provides.
Many cities are addressing the problem of funding by taking the ecosystem approach because many cities have realized it is not essential that all services be provided by a government entity. This is one way that cities are trying to address the constraint of limited funding.
Gardner: Dr. Lisdorf, in a city like New York, is a public cloud model a silver bullet, or is the devil in the details? Or is there a hybrid or private cloud model that should be considered?
Lisdorf: I don’t think it’s a silver bullet. It’s certainly convenient, but since this is new technology there are lot of things we need to clear up. This is a transition, and there are a lot of issues surrounding that.
One is the funding. The city still runs in a certain way, where you buy the IT infrastructure yourself. If it is to change, they must reprioritize the budgets to allow new types of funding for different initiatives. But you also have issues like the culture because it’s different working in a cloud environment. The way of thinking has to change. There is a cultural inertia in how you design and implement IT solutions that does not work in the cloud.
There is still the perception that the cloud is considered something dangerous or not safe. Another view is that the cloud is a lot safer in terms of having resilient solutions and the data is safe.
This is all a big thing to turn around. It’s not a simple silver bullet. For the foreseeable future, we will look at hybrid architectures, for sure. We will offload some use cases to the cloud, and we will gradually build on those successes to move more into the cloud.
Gardner: We’ve talked about the public sector digital transformation challenges, but let’s now look at what The Open Group brings to the table.
Dr. Saha, what can The Open Group do? Is it similar to past initiatives around TOGAFas an architectural framework? Or looking at DoDAF, in the defense sector, when they had similar problems, are there solutions there to learn from?
Smart city success strategies
Saha: At The Open Group, as part of the architecture forum, we recently set up a Government Enterprise Architecture Work Group. This working group may develop a reference architecture for smart cities. That would be essential to establish a standardization journey around smart cities.
One of the reasons smart city projects don’t succeed is because they are typically taken on as an IT initiative, which they are not. We all know that digital technology is an important element of smart cities, but it is also about bringing in policy-level intervention. It means having a framework, bringing cultural change, and enabling a change management across the whole ecosystem.
At The Open Group work group level, we would like to develop a reference architecture. At a more practical level, we would like to support that reference architecture with implementation use cases. We all agree that we are not going to look at a top-down approach; no city will have the resources or even the political will to do a top-down approach.
Given that we are looking at a bottom-up, or a middle-out, approach we need to identify use cases that are more relevant and successful for smart cities within the Government Enterprise Architecture Work Group. But this thinking will also evolve as the work group develops a reference architecture under a framework.
Gardner: Dr. Harding, how will work extend from other activities of The Open Group to smart cities initiatives?
Collective, crystal-clear standards
Harding: For many years, I was a staff member, but I left The Open Group staff at the end of last year. In terms of how The Open Group can contribute, it’s an excellent body for developing and understanding complex situations. It has participants from many vendors, as well as IT users, and from the academic side, too.
Such a mix of participants, backgrounds, and experience creates a great place to develop an understanding of what is needed and what is possible. As that understanding develops, it becomes possible to define standards. Personally, I see standardization as kind of a crystallization process in which something solid and structured appears from a liquid with no structure. I think that the key role The Open Group plays in this process is as a catalyst, and I think we can do that in this area, too.
Gardner: Don Brancato, same question; where do you see The Open Group initiatives benefitting a positive evolution for smart cities?
Brancato: Tactically, we have a data exchange model, the Open Data Element Framework that continues to grow within a number of IoT and industrial IoT patterns. That all ties together with an open platform, and into Enterprise Architecture in general, and specifically with models like DODAF, MODAF, and TOGAF.
Data catalogs provide proof of the activities of human systems, machines, and sensors to the fulfillment of their capabilities and are traceable up to the strategy.
We have a really nice collection of patterns that recognize that the data is the mechanism that ties it together. I would have a look at the open platform and the work they are doing to tie-in the service catalog, which is a collection of activities that human systems or machines need in order to fulfill their roles and capabilities.
The notion of data catalogs, which are the children of these service catalogs, provides the proof of the activities of human systems, machines, and sensors to the fulfillment of their capabilities and then are traceable up to the strategy.
I think we have a nice collection of standards and a global collection of folks who are delivering on that idea today.
Gardner: What would you like to see as a consumer, on the receiving end, if you will, of organizations like The Open Group when it comes to improving your ability to deliver smart city initiatives?
Use-case consumer value
Sunderland: I like the idea of reference architectures attached to use cases because -- for better or worse -- when folks engage around these issues -- even in large entities like New York City -- they are going to be engaging for specific needs.
Reference architectures are really great because they give you an intuitive view of how things fit. But the real meat is the use case, which is applied against the reference architecture. I like the idea of developing workgroups around a handful of reference architectures that address specific use cases. That then allows a catalog of use cases for those who facilitate solutions against those reference architectures. They can look for cases similar to ones that they are attempting to resolve. It’s a good, consumer-friendly way to provide value for the work you are doing.
Gardner: I’m sure there will be a lot more information available along those lines at www.opengroup.org.
When you improve frameworks, interoperability, and standardization of data frameworks, what success factors emerge that help propel the efforts forward? Let’s identify attractive drivers of future smart city initiatives. Let’s start with Dr. Lisdorf. What do you see as a potential use case, application, or service that could be a catalyst to drive even more smart cities activities?
Lisdorf: Right now, smart cities initiatives are out of control. They are usually done on an ad-hoc basis. One important way to get standardization enforced -- or at least considered for new implementations – is to integrate the effort as a necessary step in the established procurement and security governance processes.
Whenever new smart cities initiatives are implemented, you would run them through governance tied to the funding and the security clearance of a solution. That’s the only way we can gain some sort of control.
This approach would also push standardization toward vendors because today they don’t care about standards; they all have their own. If we included in our procurement and our security requirements that they need to comply with certain standards, they would have to build according to those standards. That would increase the overall interoperability of smart cities technologies. I think that is the only way we can begin to gain control.
Gardner: Dr. Harding, what do you see driving further improvement in smart cities undertakings?
Prioritize policy and people
Harding: The focus should be on the policy around data sharing. As I mentioned, I see two layers of a framework: A policy layer and a technical layer. The understanding of the policy layer has to come first because the technical layer supports it.
The development of policy around data sharing -- or specifically on personal data sharing because this is a hot topic. Everyone is concerned with what happens to their personal data. It’s something that cities are particularly concerned with because they hold a lot of data about their citizens.
Gardner: Dr. Saha, same question to you.
Saha: I look at it in two ways. One is for cities to adopt smart city approaches. Identify very-high-demand use cases that pertain to environmental mobility, or the economy, or health -- or whatever the priority is for that city.
Identifying such high-demand use cases is important because the impact is directly seen by the people, which is very important because the benefits of having a smarter city are something that need to be visible to the people using those services, number one.
The other part, that we have not spoken about, is we are assuming that the city already exists, and we are retrofitting it to become a smart city. There are places where countries are building entirely new cities. And these brand-new cities are perfect examples of where these technologies can be tried out. They don’t yet have the complexities of existing cities.
It becomes a very good lab, if you will, a real-life lab. It’s not a controlled lab, it’s a real-life lab where the services can be rolled out as the new city is built and developed. These are the two things I think will improve the adoption of smart city technology across the globe.
Gardner: Don Brancato, any ideas on catalysts to gain standardization and improved smart city approaches?
City smarts and safety first
Brancato: I like Dr. Harding’s idea on focusing on personal data. That’s a good way to take a group of people and build a tactical pattern, and then grow and reuse that.
In terms of the broader city, I’ve seen a number of cities successfully introduce programs that use the notion of a safe city as a subset of other smart city initiatives. This plays out well with the public. There’s a lot of reuse involved. It enables the city to reuse a lot of their capabilities and demonstrate they can deliver value to average citizens.
In order to keep cities involved and energetic, we should not lose track of the fact that people move to cities because of all of the cultural things they can be involved with. That comes from education, safety, and the commoditization of price and value benefits. Being able to deliver safety is critical. And I suggest the idea of traceability of personal data patterns has a connection to a safe city.
Traceability in the Enterprise Architecture world should be a standard artifact for assuring that the programs we have trace to citizen value and to business value. Such traceability and a model link those initiatives and strategies through to the service -- all the way down to the data, so that eventually data can be tied back to the roles.
For example, if I am an individual, data can be assigned to me. If I am in some role within the city, data can be assigned to me. The beauty of that is we automate the role of the human. It is even compounded to the notion that the capabilities are done in the city by humans, systems, machines, and sensors that are getting increasingly smarter. So all of the data can be traceable to these sensors.
Gardner: Don Sunderland, what have you seen that works, and what should we doing more of?
Sunderland: I am still fixated on the idea of creating direct demand. We can’t generate it. It’s there on many levels, but a kind of guerrilla tactic would be to tap into that demand to create location-aware applications, mobile apps, that are freely available to citizens.
The apps can use existing data rather than trying to go out and solve all the data sharing problems for a municipality. Instead, create a value-added app that feeds people location-aware information about where they are -- whether it comes from within the city or without. They can then become habituated to the idea that they can avail themselves of information and services directly, from their pocket, when they need to. You then begin adding layers of additional information as it becomes available. But creating the demand is what’s key.
When 311 was created in New York, it became apparent that it was a brand. The idea of getting all those services by just dialing those three digits was not going to go away. Everybody wanted to add their services to 311. This kind of guerrilla approach to a location-aware app made available to the citizens is a way to drive more demand for even more people.