Today, terms such as “open source” and “open data” are becoming used in everyday conversations, but what do these words really mean? The right open source definition is really hard to find. We caught up with the FIWARE Foundation’s CEO, Ulrich Ahle, at the Smart City Expo World Congress in Barcelona to find out more about FIWARE, open-source technologies, and the future of urban data.
Ulrich, it’s great to speak with you. Let’s get started with a brief introduction about yourself and the FIWARE Foundation.
My name is Ulrich Ahle, CEO of the FIWARE Foundation. FIWARE was created five years ago as an open-source foundation to provide open source technology and standards for the development of smart cities and other domains such as smart energy, mobility, agrifood, and more.
For those that don’t know, can you tell us a little bit more about FIWARE and how it operates as a foundation?
A lot of people know the Linux Foundation or the Wikimedia Foundation. We are organizing a large open-source network. This is a network of developers who are developing and using technology to create smart city platforms or vertical solutions. We are very much the spider in the web of this ecosystem. We are developing and managing the ecosystem and defining the strategy. As a foundation, we are a non-profit working for the public good. We do not engage in commercial activities. We are financed by the membership fees of our members. That’s one half of what we do. The second portion is that we participate in publicly funded research programs, but as I said, we do not participate in any commercial activities. And that’s why we define ourselves as a foundation.
When you say open source, what does that really mean? The term is used quite liberally these days! Would you say that FIWARE is truly open source?
Yes, it is truly open source. And I would like to take a moment to explain what I mean by this. First of all, everything that we provide as the FIWARE Foundation is based on four deliverables.
1. Open source software building blocks
The first is open source software building blocks. Roughly 30 of these exist, with the core block being the FIWARE Context Broker.
2. Standard APIs
The second is standard APIs. So, standard interfaces to get access to data.
3. Standard data models
The third is standard data models, and the combination of standard interfaces and standard data models are what allow us to break down data silos and help us to avoid the creation of new silos.
4. Standard reference architectures
Lastly, we have standard reference architectures. These are for the different domains where FIWARE is used. For example, Smart Cities, Smart Industries, Smart Agrifood. These reference architectures are a kind of recipe book to enable either end-users or IT companies to use these deliverables to create platforms and solutions on top of these platforms.
All of this is available on Github. Everything is available for everyone, for free, forever. You don’t have to be a member of the FIWARE Foundation to have access to this. When I say that it is truly open source, I mean that it is a technology that is not only developed by one person or one company. It is an open-source technology with a large ecosystem behind it. This is important to note because when I talk about open source I like to highlight that there is a federated ecosystem behind it and that these technologies aren’t ever relying on one person or one company, who may decide one day that they don’t want this technology to be open source anymore and may switch it to closed-source or only make it available through a paywall or license model.
Here, the Foundation acts as a guarantee that FIWARE technology will remain open source in perpetuity.
As I said, “open source” can mean many things depending on who you’re talking to. Many terms are often confused. For example, open-source and open core. How would you explain the difference?
Yes, these two terms are often wrongly used interchangeably. Open core, for example, would be solutions or products created out of open source building blocks but the integration effort of the product is not provided as open source. This is what we would call open core.
Let’s talk about cities. What would you say are the key problems faced by cities in the data space?
There are several key challenges in that area, though it often depends on the country you’re talking about. I will talk about four challenges:
1. Digital competence of acity
The main concern is the digital competence of the city, and the overall capabilities and competencies to either create such strategies, to create solutions themselves, or to create an RFP (Request for Proposal) to find a provider for this.
I’m working with a lot of cities—not consulting for payment—who are working in this direction stepwise. They start with a small RFP to get a consultant on board, who will help them create a bigger RFP, for example, a Smart City platform. The first challenge is always the topic of digital competency surrounding data, IT, and other digital services.
2. Funding
The second, of course, is getting the funding for this. Even if we’re talking about open source technology, it still costs money and effort to implement it and set it up. It’s not for free. Even in an open source environment, you still have to pay for consulting services for implementation, integration, deployment, operation, and so on. That leads to the most important question: where is the budget coming from?
For most cities, physical infrastructure, such as your water supply, electricity, and streets, demand the highest priority in the annual budget. However, there are only a few cities where digital infrastructure is valued just as highly. A lot of cities are starting to initiate smart projects with public funding and develop digital strategies this way, and here it’s important to find models that can stand the test of time and can continue to be operational when that public funding ends.
3. Monetization of business models
That’s the third challenge, really: finding the right business models and income streams to help fund these products. This is where the data economy starts; to be able to generate income out of data. They say that data is the gold of the 21st century, right? However, very few cities have found a way to generate income through data and monetize their smart city projects.
4. Partizipation of citizens
The final notable challenge for cities is the capability to involve the citizens. This is a big problem. From our experience, it’s very important for cities to make sure that the technology they are providing is really of use for their citizens, in a way that is practical and sustainable. An excellent technology isn’t of any value if it’s not accepted, adopted, and used by the citizens. Similarly, when you have citizens that are enthusiastic about using digital technologies, if the technology that the city provides doesn’t work, it creates a negative feeling towards any new smart city development. Citizen involvement is crucial for cities wanting to create digital solutions.
You mentioned monetization and business models. What sort of monetization model can be used regarding open source solutions? License agreements or accessibility limitations?
Here we have to be careful to differentiate between open-source and open data. We talked about open source earlier, but let’s focus on what we mean by open data. Open data doesn’t necessarily mean free of charge. Open data certainly can be accessible to everyone, however, there may be certain data that you need to pay for when you access it. Let me give you an example:
When talking about smart parking, data generated by sensors deployed on a parking lot could be provided to an app or directly to the navigation system in a car, and I might be guided to the closest car parking space to my destination rather than directly to my chosen destination itself. So, I’m using a service that will take me to the nearest parking space to my destination. I could arrive directly at my destination and have to drive around for 10 minutes trying to find somewhere to park. Instead, I’ve used this data to find and potentially reserve a nearby parking space. For this kind of service, I could be willing to pay a fee to make my life easier. I will be on time for my appointment. I don’t have the frustration of driving around looking for a space. And in summary: this is an example of how data can be monetized with the end-user paying for it. It is open data that requires payment to access.
Here’s another example. Let’s stay with smart parking solutions. With these solutions, cities usually deploy sensors in parking lots or install cameras with image recognition that can identify whether a parking space is occupied or not, and a driver can be guided to the right place. There are also solutions that don’t depend on city infrastructure. For example, cars that can identify empty parking lots by themselves. One manufacturer is developing a great solution in this direction, but I can’t mention the brand name. Let’s say they’re manufacturing high-end cars that can park autonomously. They’re equipped with radar sensors, and when you’re driving along a street, these radar sensors are scanning ahead for available parking spots, measuring them, and figuring out whether they’re large enough for your vehicle to park in. Once it has identified a space, you can leave your vehicle, and the car will park in that space all by itself.
This premium car manufacturer is now willing and able to provide this data to cities. This means that cities don’t have to install their own sensors and infrastructure anymore, saving money and effort, but still receiving important parking data and information thanks to fleets of cars that are simply driving through the city.
A model for monetising this data is that the cities are paying car manufacturers for the provisioning of this data, providing services to their citizens, and saving costs in the long run.
Those are some great examples. Do you have any other examples of key challenges outside of parking and mobility?
Another obvious one would be smart lighting. Changing from traditional lighting to LED lighting saves 70% in energy costs and refining that with smart lighting solutions saves another 5% to 10%. The interesting thing about this use case is that by installing smart lamp posts, you are actively installing a new kind of infrastructure in the city. These poles can also be used for EV charging, as 5G repeaters, fog cameras, and so on. Very often, smart lighting is really the first practical vertical use case.
I’d like to steer us back to data and talk about the tricky topic of regulation. When it comes to data governance in cities, do you believe that cities should establish certain data regulations before implementing these data projects or digital solutions? How would you advise a city on developing a strong data governance plan?
I would not advise a city. I would advise a country. In my experience, it doesn’t make sense to make a siloed solution for one city. We talked about the smart parking solution before. It doesn’t make sense for a smart parking solution that’s integrated into the navigation system of my car to work in Berlin, but not work in Cologne or Brussels. My recommendation is to define smart guidelines at least at a state level, not just on a city level. During the last years, a lot of work has been done with Open and Agile Smart Cities (OASC).
OASC has started to develop a model of topics of interest and created the so-called Minimum Interoperability Mechanisms (MIMs): MIM1, standard API, and MIM2 standard data models. My strong recommendation is to define these guidelines on a country level, not just for individual cities. If it can’t be done on a country level, it should at least be done on a city level to avoid the creation of siloes within a city.
One country in Europe that is really moving in this direction is Slovenia. Slovenia is currently heading the presidency of the European Union and they have put the topic of smart cities onto their agenda. They are one of the first countries that has decided that cities can use public funding for the implementation of smart city solutions, but only if they use open-source frameworks, standard APIs, and standard data models. In short, they must comply with OASC’s MIM principles. In this case, FIWARE’s NGSI and data models.
What prerequisites would you have at the state level for open data platforms?
Data sovereignty is a clear prerequisite to enable digital solutions and create acceptance from the end-user or citizen’s side. The people, or in this case city governments, must be the sovereign of their data.
What does that really mean?
It means that the owner of a data set is able to determine and technically enforce who can access certain data, what they’re allowed to do with that data, and define what they are technically able to do with that data.
Currently, there are mechanisms that are available to do this. At FIWARE, we are working closely with an organisation called the International Data Spaces Association (IDSA) and they have two core value propositions: the IDS connector, which provides the capability for authorities to interconnect different platforms. The second is data sovereignty, which enables what I described a second ago. Data sovereignty is an essential prerequisite.
Another essential prerequisite would be interoperability. Being able to create interoperable solutions with standard APIs and data models. Data sovereignty and interoperability are two very key elements.
What are the advantages of FIWARE’s solutions compared with competitors?
FIWARE has two main advantages. It’s open-source, of course, and there is a large community of developers behind FIWARE that maintain and develop the building blocks, APIs, and data models, in a continuously improving process. And also, we’re a large community of innovative start-ups, mid-size companies, and large corporates who are all using these technologies to create open-source FIWARE-based solutions. In the FIWARE marketplace, you can find around a dozen smart city platforms and more than 150 smart city solutions built on top of these platforms.
The second main value proposition is that all of these solutions and platforms use the same APIs, the same data models, and they can be combined like Lego bricks. These APIs, like Lego bricks, can be combined and used together to make something even greater. I think this is the most important value proposition: by using standard APIs and data models, we are clearly reducing the so-called vendor lock-in effect. This is all thanks to FIWARE-based solutions. Vendor lock-in won’t be completely eliminated but we can definitely and dramatically reduce this vendor lock-in effect.
Would you say that pure interoperability is at the heart of FIWARE?
Absolutely. This is something that we are bringing into an initiative called the Data Spaces Business Alliance.
Can you tell me more about this?
Sure. You might have heard of Gaia-X? Gaia-X was announced two years ago, but at the moment there isn’t much happening on the ground with Gaia-X. Not much has been implemented in the real world yet. Four organisations joined forces six weeks ago to create the Data Spaces Business Alliance. These businesses are the IDSA I mentioned before, a European organisation called the Big Data Value Association (BDVA), Gaia-X ISPL (the organisation behind the Gaia-X ecosystem), and FIWARE.
The four of us have come together to create a standard architecture for interoperable data spaces, collect existing building blocks that fit into this architecture, all to speed up the creation of real-world data spaces. Two of the main contributions from the FIWARE ecosystem are of course, standard APIs and standard data models. These are pre-requisites to create these interoperable data spaces. IDSA brings its connector and functionality to create data sovereignty. Gaia-X brings in the federated cloud infrastructure, which is operated and based on European values. And we decided to join forces and align our activities to achieve our targets faster and with less effort.
With that in mind, how would you say the topic of data is evolving? What is the future for FIWARE and Gaia-X?
The initial idea of Gaia-X was to create a federated cloud infrastructure that wouldn’t be owned by a single hyperscaler but as a federated and combined offering from different partners in Europe, in a way that’s based on a European value system. So, not situated under the Cloud Act in the US, not having the necessity to implement a backdoor, which is required from some Asian states. Instead, it’s based purely on European values. The goal is to be an alternative offering to those from other hyperscalers.
What’s very important about this alternative is that it’s providing real data sovereignty, in the way that I described it before. This allows the creator of a data set to benefit from this data. This is not always the case with existing hyperscalers. Very often, we end up paying with our data without knowing exactly what is being done with our data.
Data sovereignty and interoperability are very much at the heart of FIWARE but what is the end goal for the Foundation?
Our target or vision is to become the GSM for context information management for smart cities. GSM is the basis for telecommunication all around the world. That’s the standard, and that standard enables us to use our smartphone wherever we are in the world. And our idea with our standards, developed within the FIWARE ecosystem, is to enable that same level of standards for context information management all around the world. And we’re on a very good path using standard technology developed in Europe, but now it’s being adopted on nearly all the continents of the world. That’s our goal.
Excellent. Well, I think that’s all we have time for. Ulrich, I thank you for your insights and your time.
You’re very welcome.
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