News > Interoperability

Through the GIS - A Marketer’s Adventures in Geospatial Data

The advantage of being a marketer is that you do not need to have all the answers.

This comes in particularly handy when you have approximately none of them.

Having left behind previous safe havens in literature, the arts, the gaming industry, and B2B SaaS, I decided that it was high time to jump head-first into another adventure. For preference, one that would bring some good into the world.

That’s how I, after more than a little cajoling on my part, wound up in the world of data standards and GIS at wetransform. Before making a formal foray into the depths of these topics, I set about to learn the basics.

I am told that “GIS” is a Geographic Information System, effectively a super layered digital map that holds vast amounts of information regarding what’s going on in a particular slice of the world, provided the data is right. There’s a subreddit. There are memes. I embrace the hatred of unprojected CAD files.

The standardization business is also, at its core, not that hard to understand.

Loads of people, companies, and other instances collect geospatial data. Naturally, they all structure it in their own way. This structure usually works for their personal application but, to the world at large, is a mess.

The incredibly smart people I work with have created and maintain the tools required to make sense of said mess, so other really smart people (and even some robots) can use it to make decisions. The more clean, well-structured data there is to look at, the better the odds that those decisions are the ones that’ll steer our planet in the right direction. Save the data standard, save the world.

I found myself nodding sagely at terms like “INSPIRE”, “XPlanung”, and “CityGML”. Data standards, vital to interoperability. Cool. Apparently, it’s pretty difficult to become fully compliant to those standards without our help. Less cool. However, we’re saving well over a thousand organisations an absolute boatload of time, cutting their processes down by about 80%! Important people now have more time to spend on other things. Super cool.

Some of these data standards even enable non-GIS users to work with the data. The interoperability that data standards provide can streamline processes for everyone involved, from government to business to citizens, which can save a lot of time, effort, and money. To the tune of 500 billion actual Euros, according to the European Commission’s Joint Research Centre. Good for them!

So how do we help our clients achieve that almighty interoperability? Time for me to learn more about our tools. You know, the ones I am supposed to help get to the market.

hale»studio is an open-source tool that performs data harmonisation for geospatial data. Take your source data (I am told Shapefiles are common), select a schema (the structure it needs to go into, depending on the data standard you’re aiming for), assign what goes where, run it through. Hey presto, interoperability!

hale»connect complements hale»studio by handling metadata and publishing. Compliance to INSPIRE, for example, becomes a relative walk in the park! Plenty of options to add in collaboration and workflows. Heck, after a quick intro and some setup by a more experienced colleague and I found myself using it pretty effortlessly even though I’d never really touched geospatial data before.

I ask whether I can have one of my colleagues record the processes with and without our tools for marketing purposes. I am told that while the half an hour for a hale»connect/studio demo is fine, I am not allowed to give one of our finest engineers twelve hours of solid work. I just nod.

So, our tools are good. Really good. The best, in fact. I know this because I asked.

Just as I’m starting to feel confident in this new field and all its terminology, with a pretty decent handle on our tools for someone who is not and will likely never be an end-user, something else comes up. Data spaces. That sounds cool. It has “space” in it, so it must be cool. I hop into my imaginary spacecraft and find out that data spaces are, in fact, very cool.

You see, a lot of the data currently in use is so-called “open data”. Does what it says on the tin. It’s data, it’s there, you and anyone else can use it for whatever you please. However, there is far more data out there, hidden away behind multiple layers of security because whatever it contains requires protecting. The exact location of endangered species, details regarding the population’s personal health, that type of thing. The type of thing you do not want just anyone to have access to. Which is exactly why it’s not open.

There is a lot more of that type of data than there is open data… and it could be extremely valuable.

“Data gaps” is the term that keeps cropping up as the issue to be solved. A lack of required data, not because it does not exist, but because there is no access to it at all. Not even a path.

So how do you solve an issue like that? Where data that is, for good reason, not being shared at all could have a huge impact on how vital, world-saving decisions are made?

You create the VIP-area known as a “data space”, a members-only club with different levels of access and robust governance policies to ensure that such precious data never, ever falls into the wrong hands. You can rest assured that, for a change, you’ll have complete control over what’s happening with your data.

You can train AI inside the data space. Let it learn from all the data in there, then leave all that data on its way out to do some good in the real world. Inside the data space, every process is certified and every participant is vetted. A door policy to make Berghain blush.

You can find a nicely formatted slide on the process below.

Data spaces are already included in the European Data Strategy. Sectors such as Agriculture, Environment, Energy, Finance, Healthcare, Manufacturing, Mobility, and Public Authorities are all slated to use them.

While data spaces are still very new, I feel I am in the right place with the right team to learn more about them and how they will help solve very real problems. Not only do we have the tools to automatically make all data inside a data space fully interoperable and set up the governance, with the help of the International Data Spaces Association, wetransform stands at the cradle of the Environmental Data Space. We are already building a community dedicated to identifying high value use cases, defining architectures and governance models, and testing out solutions in concrete projects.

If you want to learn more about data spaces, check out this recent article, or head on over to the Environmental Data Spaces Community landing page to get involved.

Like I said before, I do not need to have all the answers. I do not know how to do your job. What I do know is that I work with incredibly smart, dedicated people who have built (and are continuing to build and maintain) solutions for the harmonisation and publication of geospatial data that, by every metric I have seen, can save you a great deal of time, effort, and money. I encourage you to have a look, perhaps even a call, to see how they can help you.

As for me, I intend to keep on learning. There is certainly enough to do.

Through the GIS - A Marketer’s Adventures in Geospatial Data

The advantage of being a marketer is that you do not need to have all the answers.

This comes in particularly handy when you have approximately none of them.

Having left behind previous safe havens in literature, the arts, the gaming industry, and B2B SaaS, I decided that it was high time to jump head-first into another adventure. For preference, one that would bring some good into the world.

That’s how I, after more than a little cajoling on my part, wound up in the world of data standards and GIS at wetransform. Before making a formal foray into the depths of these topics, I set about to learn the basics.

I am told that “GIS” is a Geographic Information System, effectively a super layered digital map that holds vast amounts of information regarding what’s going on in a particular slice of the world, provided the data is right. There’s a subreddit. There are memes. I embrace the hatred of unprojected CAD files.

The standardization business is also, at its core, not that hard to understand.

Loads of people, companies, and other instances collect geospatial data. Naturally, they all structure it in their own way. This structure usually works for their personal application but, to the world at large, is a mess.

The incredibly smart people I work with have created and maintain the tools required to make sense of said mess, so other really smart people (and even some robots) can use it to make decisions. The more clean, well-structured data there is to look at, the better the odds that those decisions are the ones that’ll steer our planet in the right direction. Save the data standard, save the world.

I found myself nodding sagely at terms like “INSPIRE”, “XPlanung”, and “CityGML”. Data standards, vital to interoperability. Cool. Apparently, it’s pretty difficult to become fully compliant to those standards without our help. Less cool. However, we’re saving well over a thousand organisations an absolute boatload of time, cutting their processes down by about 80%! Important people now have more time to spend on other things. Super cool.

Some of these data standards even enable non-GIS users to work with the data. The interoperability that data standards provide can streamline processes for everyone involved, from government to business to citizens, which can save a lot of time, effort, and money. To the tune of 500 billion actual Euros, according to the European Commission’s Joint Research Centre. Good for them!

So how do we help our clients achieve that almighty interoperability? Time for me to learn more about our tools. You know, the ones I am supposed to help get to the market.

hale»studio is an open-source tool that performs data harmonisation for geospatial data. Take your source data (I am told Shapefiles are common), select a schema (the structure it needs to go into, depending on the data standard you’re aiming for), assign what goes where, run it through. Hey presto, interoperability!

hale»connect complements hale»studio by handling metadata and publishing. Compliance to INSPIRE, for example, becomes a relative walk in the park! Plenty of options to add in collaboration and workflows. Heck, after a quick intro and some setup by a more experienced colleague and I found myself using it pretty effortlessly even though I’d never really touched geospatial data before.

I ask whether I can have one of my colleagues record the processes with and without our tools for marketing purposes. I am told that while the half an hour for a hale»connect/studio demo is fine, I am not allowed to give one of our finest engineers twelve hours of solid work. I just nod.

So, our tools are good. Really good. The best, in fact. I know this because I asked.

Just as I’m starting to feel confident in this new field and all its terminology, with a pretty decent handle on our tools for someone who is not and will likely never be an end-user, something else comes up. Data spaces. That sounds cool. It has “space” in it, so it must be cool. I hop into my imaginary spacecraft and find out that data spaces are, in fact, very cool.

You see, a lot of the data currently in use is so-called “open data”. Does what it says on the tin. It’s data, it’s there, you and anyone else can use it for whatever you please. However, there is far more data out there, hidden away behind multiple layers of security because whatever it contains requires protecting. The exact location of endangered species, details regarding the population’s personal health, that type of thing. The type of thing you do not want just anyone to have access to. Which is exactly why it’s not open.

There is a lot more of that type of data than there is open data… and it could be extremely valuable.

“Data gaps” is the term that keeps cropping up as the issue to be solved. A lack of required data, not because it does not exist, but because there is no access to it at all. Not even a path.

So how do you solve an issue like that? Where data that is, for good reason, not being shared at all could have a huge impact on how vital, world-saving decisions are made?

You create the VIP-area known as a “data space”, a members-only club with different levels of access and robust governance policies to ensure that such precious data never, ever falls into the wrong hands. You can rest assured that, for a change, you’ll have complete control over what’s happening with your data.

You can train AI inside the data space. Let it learn from all the data in there, then leave all that data on its way out to do some good in the real world. Inside the data space, every process is certified and every participant is vetted. A door policy to make Berghain blush.

You can find a nicely formatted slide on the process below.

Data spaces are already included in the European Data Strategy. Sectors such as Agriculture, Environment, Energy, Finance, Healthcare, Manufacturing, Mobility, and Public Authorities are all slated to use them.

While data spaces are still very new, I feel I am in the right place with the right team to learn more about them and how they will help solve very real problems. Not only do we have the tools to automatically make all data inside a data space fully interoperable and set up the governance, with the help of the International Data Spaces Association, wetransform stands at the cradle of the Environmental Data Space. We are already building a community dedicated to identifying high value use cases, defining architectures and governance models, and testing out solutions in concrete projects.

If you want to learn more about data spaces, check out this recent article, or head on over to the Environmental Data Spaces Community landing page to get involved.

Like I said before, I do not need to have all the answers. I do not know how to do your job. What I do know is that I work with incredibly smart, dedicated people who have built (and are continuing to build and maintain) solutions for the harmonisation and publication of geospatial data that, by every metric I have seen, can save you a great deal of time, effort, and money. I encourage you to have a look, perhaps even a call, to see how they can help you.

As for me, I intend to keep on learning. There is certainly enough to do.

(more)

The German version of this article can be accessed here.

Society is facing major challenges such as climate change and loss of ecosystems. For these challenges, we will have to find well-optimised, sustainable solutions. In this process, data is invaluable.

Thanks to Copernicus, INSPIRE, and the Open Data Directive more and more geodata has become publicly available. However, this data is merely the tip of the proverbial iceberg. Large swathes of relevant data remain hidden from view due to security concerns and legal obligations such as GDPR. The new European Data Strategy aims to make this pool of data more accessible by utilising a concept that’s already shown itself to be successful in the automotive industry. This concept is known as data spaces. In this article, the differences between the existing geodata infrastructure (GDI) and data spaces will be explained, as well as which issues can be resolved through the use of these data spaces, and what will need to be kept in mind when implementing them.

The automotive industry is adept at finding efficient solutions to incredibly complex issues. Their products need to conform to a myriad of both legal and scientific standards at every stage of their elaborate research, development, and production chains. In an effort to make these processes more cost-effective and secure, car manufacturers and their suppliers exchange data in a limited fashion.

Originally, companies only had a limited and often flawed insight into their supply chain. To remedy the issues that kept cropping up due to this lack of transparency, the Catena X Data Space was created. This data space allowed all participating organisations to exchange their data inside a single, secure platform. Who is allowed access to which data, and to what purpose, is decided by the organisations involved.

Bolstered by the success of this approach, 2021’s European Data Strategy builds upon the concept of data spaces to unlock the hitherto hidden potential of closed data in other sectors. In the new model, data spaces are set to support nine strategic areas: Agriculture, Environment, Energy, Finance, Healthcare, Manufacturing, Mobility, Public Administration, and Public Authorities. A data space to support the implementation of the Green Deal is already in the works. Every data space will contain a combination of public and proprietary data from both companies and governmental organisations.

What is a data space?

Governance is central to the data space concept. A combined set of rules and standards, as well as their technical implementation, that defines which roles exist within the data space and the level of access to data that each of these roles provide. For example, data providers can allow their data to be used within a training pool for AI models, but severely limit the export of that data outside of the data space. Common technical standards will have to be agreed upon as well, particularly data models such as INSPIRE, XPlanung, or 3A/NAS for the Geospatial and Environmental sectors.

Just as in GDI, source data sets will differ. Every organisation can create, house, and utilise their data in whatever manner they desire, be it on premise or in the cloud. Controlled access to that data can be securely managed through an adapter such as the Eclipse Dataspace Connector.

All data sets within a data space are interoperable. That does not mean that all data needs to conform to the same format or schema, but rather that they can automatically be integrated and harmonised as required. For this, matching- and mapping technology will be utilised, such as annotations (“This is a parcel.”). ETL tools like hale»studio and hale»connect can use this metadata to automatically prepare data for processors in different parts of the data space.

Such processing services are themselves part of the data space. How these services are allowed to access and use the data is established within the communal rules, for example whether or not they’re allowed to be temporarily cached. Trust plays an important part in this. Starting in 2022, processing services are able to obtain certifications. Once a service is certified, all participants in the data space can be certain that this service will only do exactly what it claims to do.

Which issues do data spaces solve?

The creation of a data space only makes sense where there is a concrete use case where vital data gaps can be closed through the use of previously inaccessible data. These data gaps need to be defined and thoroughly documented.

Such a data gap also exists in scenarios where there is data available, but not in sufficient quantity to train a useful AI model. Within the security of the data space, a much greater amount of training data can be made available. Since only the final AI model will be exported out of the data space, the confidentiality of the training data remains unaltered.

There is another problem data spaces solve. It is common practice for modern platforms to siphon off and sell large amounts of data without any input from the subjects of said data, be it companies or private citizens. Within a data space, rules can be established not only to secure data sovereignty, but also to allow a more balanced division of the value generated through that data. For example, through a “Pay as you go”-model.

In order to provide this data sovereignty, the data space has to be built upon hardware, software, and operating systems that have been designed and secured to allow for it. Therefore, the data spaces’ infrastructure is being created in collaboration with GAIA-X, Europe’s distributed cloud platform.

What does this mean in the real world?

The AI pilot project FutureForest.ai is a great way to illustrate the usefulness of data spaces. In this project, wetransform collaborates with the TU Munich and the TU Berlin, as well as several German state forests and forest research institutes to create a data space for forestry data. All these organisations contribute access to their data within the data space, so better decisions can be made surrounding climate-adapted forest conversion. This combines both public data, such as elevation models and land coverage maps, and private data, such as sensor data and detailed information from location mapping. The forest owners contribute their data and in return are able to leverage better decision-making models.

This last decade has allowed us to make great strides in terms of spatial data accessibility, chiefly through open data initiatives. Unfortunately, a lack of attention paid to organisational frameworks and data usage conditions often still hamper progress. Through the use of data spaces, such as the one for forestry, this will change for the better.

More than Projects – the Environmental Data Spaces Community

It’s still early days in the Geo- and GIS-community when it comes to the implementation and usage of data spaces. Many projects are being launched, both nationally and internationally. In order to create a network between all the different parties currently involved in these projects and provide more developmental continuity, wetransform has established the Environmental Data Spaces Community. Aided by several partners and the framework laid out by the International Data Spaces Association, which sets the standards for data spaces, wetransform supports the creation of diverse data ecosystems with the goal of making environmental data accessible and usable inside a secure data space that protects data sovereignty.

More information surrounding the Environmental Data Spaces Community and the possibilities for those desiring to join it, can be found here.

This article originally appeared in German in gis.business 2-2022, 25-27.

The German version of this article can be accessed here.

Society is facing major challenges such as climate change and loss of ecosystems. For these challenges, we will have to find well-optimised, sustainable solutions. In this process, data is invaluable.

Thanks to Copernicus, INSPIRE, and the Open Data Directive more and more geodata has become publicly available. However, this data is merely the tip of the proverbial iceberg. Large swathes of relevant data remain hidden from view due to security concerns and legal obligations such as GDPR. The new European Data Strategy aims to make this pool of data more accessible by utilising a concept that’s already shown itself to be successful in the automotive industry. This concept is known as data spaces. In this article, the differences between the existing geodata infrastructure (GDI) and data spaces will be explained, as well as which issues can be resolved through the use of these data spaces, and what will need to be kept in mind when implementing them.

The automotive industry is adept at finding efficient solutions to incredibly complex issues. Their products need to conform to a myriad of both legal and scientific standards at every stage of their elaborate research, development, and production chains. In an effort to make these processes more cost-effective and secure, car manufacturers and their suppliers exchange data in a limited fashion.

Originally, companies only had a limited and often flawed insight into their supply chain. To remedy the issues that kept cropping up due to this lack of transparency, the Catena X Data Space was created. This data space allowed all participating organisations to exchange their data inside a single, secure platform. Who is allowed access to which data, and to what purpose, is decided by the organisations involved.

Bolstered by the success of this approach, 2021’s European Data Strategy builds upon the concept of data spaces to unlock the hitherto hidden potential of closed data in other sectors. In the new model, data spaces are set to support nine strategic areas: Agriculture, Environment, Energy, Finance, Healthcare, Manufacturing, Mobility, Public Administration, and Public Authorities. A data space to support the implementation of the Green Deal is already in the works. Every data space will contain a combination of public and proprietary data from both companies and governmental organisations.

What is a data space?

Governance is central to the data space concept. A combined set of rules and standards, as well as their technical implementation, that defines which roles exist within the data space and the level of access to data that each of these roles provide. For example, data providers can allow their data to be used within a training pool for AI models, but severely limit the export of that data outside of the data space. Common technical standards will have to be agreed upon as well, particularly data models such as INSPIRE, XPlanung, or 3A/NAS for the Geospatial and Environmental sectors.

Just as in GDI, source data sets will differ. Every organisation can create, house, and utilise their data in whatever manner they desire, be it on premise or in the cloud. Controlled access to that data can be securely managed through an adapter such as the Eclipse Dataspace Connector.

All data sets within a data space are interoperable. That does not mean that all data needs to conform to the same format or schema, but rather that they can automatically be integrated and harmonised as required. For this, matching- and mapping technology will be utilised, such as annotations (“This is a parcel.”). ETL tools like hale»studio and hale»connect can use this metadata to automatically prepare data for processors in different parts of the data space.

Such processing services are themselves part of the data space. How these services are allowed to access and use the data is established within the communal rules, for example whether or not they’re allowed to be temporarily cached. Trust plays an important part in this. Starting in 2022, processing services are able to obtain certifications. Once a service is certified, all participants in the data space can be certain that this service will only do exactly what it claims to do.

Which issues do data spaces solve?

The creation of a data space only makes sense where there is a concrete use case where vital data gaps can be closed through the use of previously inaccessible data. These data gaps need to be defined and thoroughly documented.

Such a data gap also exists in scenarios where there is data available, but not in sufficient quantity to train a useful AI model. Within the security of the data space, a much greater amount of training data can be made available. Since only the final AI model will be exported out of the data space, the confidentiality of the training data remains unaltered.

There is another problem data spaces solve. It is common practice for modern platforms to siphon off and sell large amounts of data without any input from the subjects of said data, be it companies or private citizens. Within a data space, rules can be established not only to secure data sovereignty, but also to allow a more balanced division of the value generated through that data. For example, through a “Pay as you go”-model.

In order to provide this data sovereignty, the data space has to be built upon hardware, software, and operating systems that have been designed and secured to allow for it. Therefore, the data spaces’ infrastructure is being created in collaboration with GAIA-X, Europe’s distributed cloud platform.

What does this mean in the real world?

The AI pilot project FutureForest.ai is a great way to illustrate the usefulness of data spaces. In this project, wetransform collaborates with the TU Munich and the TU Berlin, as well as several German state forests and forest research institutes to create a data space for forestry data. All these organisations contribute access to their data within the data space, so better decisions can be made surrounding climate-adapted forest conversion. This combines both public data, such as elevation models and land coverage maps, and private data, such as sensor data and detailed information from location mapping. The forest owners contribute their data and in return are able to leverage better decision-making models.

This last decade has allowed us to make great strides in terms of spatial data accessibility, chiefly through open data initiatives. Unfortunately, a lack of attention paid to organisational frameworks and data usage conditions often still hamper progress. Through the use of data spaces, such as the one for forestry, this will change for the better.

More than Projects – the Environmental Data Spaces Community

It’s still early days in the Geo- and GIS-community when it comes to the implementation and usage of data spaces. Many projects are being launched, both nationally and internationally. In order to create a network between all the different parties currently involved in these projects and provide more developmental continuity, wetransform has established the Environmental Data Spaces Community. Aided by several partners and the framework laid out by the International Data Spaces Association, which sets the standards for data spaces, wetransform supports the creation of diverse data ecosystems with the goal of making environmental data accessible and usable inside a secure data space that protects data sovereignty.

More information surrounding the Environmental Data Spaces Community and the possibilities for those desiring to join it, can be found here.

This article originally appeared in German in gis.business 2-2022, 25-27.

(more)

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Making Interoperable Data Count
10.03.2022 by Akshat Bajaj, John Boudewijn, Thorsten Reitz

Making Interoperable Data Count

Have you ever wondered how much money the government wastes due to lack of interoperability?

The European Commission’s Joint Research Centre (JRC) new report answers exactly that question. The answer is in the ballpark of 500 billion EUR.

The recently released report shows that even a minor gain will have significant impact on the bottom line for data providers, citizens, and businesses. Here’s how.

Please note that this is only a summary of some of the key findings in the report. To understand the methodology of data collection and data analysis in-depth, please access the report here.

The Data Providers’ Return on Investment

The actual benefits of the time and effort involved in improving data interoperability can seem ambiguous. These benefits are made clearer by framing them in the context of their impact on the E-Government Development Index (EGDI).

The EGDI measures 3 dimensions:

  • Provision of online services
  • Telecommunications
  • Human capacity

Interoperability streamlines the exchange of information between different parties, and the efficiencies gained positively impact each of these three dimensions. For example, more interoperable systems can boost the information exchange between different devices (this is especially critical given the rise of 5G and IoT). Or, from the online service perspective, increase citizen participation in decision-making processes such as where to set up schools by providing more efficient platforms for exchanging information.

According to the report, a mere 1% increase in the EGDI leads to an impact on the following metrics:

Gross Domestic Product (GDP): A measurement that seeks to capture a country’s economic output. The report suggested an improvement of 0.4% in GDP.

Government revenues: Refers to the total amount of revenues collected in a year. The report predicted an improvement equal to 0.07% of the GDP.

Policy performance: Refers to the development of social, economic and environment conditions for the wellbeing of citizens. The report predicted an improvement equal to 0.3% of the GDP.

Government production costs: Costs including employee compensation, government usage of goods and services, depreciation, etc. The report predicted an improvement equal to 0.3% of the GDP.

General government spending: Measured in terms of a percentage of GDP, this refers to the expenditure on delivering public goods and services such as social protection. The report predicted an improvement equal to 0.6% of the GDP.

Government effectiveness: Refers to the inputs required for the government to be able to produce and implement policies and deliver goods. The report predicted an improvement equal to 0.2% of the GDP.

For those who love statistics, here’s a table of the regression analysis:

The Citizens’ Benefits

Intuitively, saying that more efficient government processes benefit a country’s citizens makes sense. Now, the JRC’s research has quantified that benefit.

Interoperability improves public online services and reduces the bureaucratic load, which not only makes the processes more efficient for citizens who already use public services, but also motivates those who currently don’t currently use them.

According to the JRC, this can lead to 21-24 million hours saved per year across the EU27. Assuming average hourly wages as a basis, this is equivalent to approx. 473m to 543m EUR per year.

The impact per country can be seen below.

The Businesses’ Benefits

While the costs of setting up a business can vary greatly across the EU, doing so more efficiently will always make life easier.

A large range of online services is already available to business providers, from registering property to declaring taxes.

As with the citizens, the JRC assumes both a reduction in time spent by those who already use these processes (Scenario 1) and an uptick in the overall use of digital processes as they become more efficient (Scenario 2).

Currently, businesses across the EU27 spend 172 billion hours per year on setting up businesses. That’s around 250,000 lifetimes!

Increased data interoperability would save approximately 27.6 billion hours and 30 billion hours for scenario 1 and 2, respectively. That equates to 521bn EUR for scenario 1 and 561bn EUR for scenario 2. In each case, that’s more than the nominal GDP of Austria in 2021.

The impact per country can be seen below.

How do we get there?

The study clearly demonstrates the potential benefits of even a minor improvement in terms of data interoperability. The question that remains is how we can achieve that.

The EU has taken a long-term view on reaping the benefits of interoperability and making data more useful.

Interoperability and accessibility initiatives (such as INSPIRE) have allowed us to take crucial steps towards greater data interoperability. Tools such as hale»connect and hale»studio have streamlined the implementation of these standards into a partially automated process. We’ve seen hundreds of thousands of INSPIRE compliant datasets published with our tools - as a matter of fact, hale»connect and hale»studio have even set a world record for number of datasets published, as shown below (Komm.One is a hale»connect based data provider):

However, the impact of open data standards is merely the tip of the iceberg.

Going beyond data standards, the EU data strategy continues to emphasise the importance of more accessible and more useful data. It sets clear rules on the access and reuse of data and invests in the next generation of tools that store and process data.

Data spaces are the most important part of this new generation of data initiatives. By providing granular control over what is and is not shared between certain parties, data spaces comply with the governance rules dictated by the EU data strategy. This means that previously inaccessible data (for example, exact locations of endangered species or medical data) can be utilised to a greater extent without sacrificing data sovereignity, resulting in a much larger pool of data to draw from.

wetransform is determined to ensure that this larger data pool is not only available, but is also fully interoperable. Our product lines are being extended to support the creation and maintenance of data spaces. Drawing from our expertise in the implementation of open data standards, we are now deeply involved in the creation of the Environmental Data Space. You can learn more about the environmental data space community and get involved here.

Conclusion

Improved efficiency makes government processes take less time, which comes with very real benefits to data providers, businesses, and citizens.

While some of the study’s numbers are hypothetical, the scenarios presented accurately resemble real world conditions and a 1% improvement of the EGDI is attainable.

The potential impact on the bottom lines for data providers and businesses, as well as the benefit in terms of time spent for a nation’s citizens and government officials are also realistic predictions.

A fairly straightforward way to improve the efficiency of government processes is by improving the interoperability of data, which streamlines the flow of information between parties.

The best way to make data sets interoperable is by using ETL and publication tools, such as hale»studio and hale»connect. To learn more about how these tools aid in increasing the interoperability of data, check out our hale»studio and hale»connect pages!

A big thanks to Peter Ulrich, Nestor Duch Brown, Alexander Kotsev, Marco Minghini, Lorena Hernandez Quiros, Raymond Bogulawski, and Francesco Pignatelli for pulling this report together.

Making Interoperable Data Count

Have you ever wondered how much money the government wastes due to lack of interoperability?

The European Commission’s Joint Research Centre (JRC) new report answers exactly that question. The answer is in the ballpark of 500 billion EUR.

The recently released report shows that even a minor gain will have significant impact on the bottom line for data providers, citizens, and businesses. Here’s how.

Please note that this is only a summary of some of the key findings in the report. To understand the methodology of data collection and data analysis in-depth, please access the report here.

The Data Providers’ Return on Investment

The actual benefits of the time and effort involved in improving data interoperability can seem ambiguous. These benefits are made clearer by framing them in the context of their impact on the E-Government Development Index (EGDI).

The EGDI measures 3 dimensions:

  • Provision of online services
  • Telecommunications
  • Human capacity

Interoperability streamlines the exchange of information between different parties, and the efficiencies gained positively impact each of these three dimensions. For example, more interoperable systems can boost the information exchange between different devices (this is especially critical given the rise of 5G and IoT). Or, from the online service perspective, increase citizen participation in decision-making processes such as where to set up schools by providing more efficient platforms for exchanging information.

According to the report, a mere 1% increase in the EGDI leads to an impact on the following metrics:

Gross Domestic Product (GDP): A measurement that seeks to capture a country’s economic output. The report suggested an improvement of 0.4% in GDP.

Government revenues: Refers to the total amount of revenues collected in a year. The report predicted an improvement equal to 0.07% of the GDP.

Policy performance: Refers to the development of social, economic and environment conditions for the wellbeing of citizens. The report predicted an improvement equal to 0.3% of the GDP.

Government production costs: Costs including employee compensation, government usage of goods and services, depreciation, etc. The report predicted an improvement equal to 0.3% of the GDP.

General government spending: Measured in terms of a percentage of GDP, this refers to the expenditure on delivering public goods and services such as social protection. The report predicted an improvement equal to 0.6% of the GDP.

Government effectiveness: Refers to the inputs required for the government to be able to produce and implement policies and deliver goods. The report predicted an improvement equal to 0.2% of the GDP.

For those who love statistics, here’s a table of the regression analysis:

The Citizens’ Benefits

Intuitively, saying that more efficient government processes benefit a country’s citizens makes sense. Now, the JRC’s research has quantified that benefit.

Interoperability improves public online services and reduces the bureaucratic load, which not only makes the processes more efficient for citizens who already use public services, but also motivates those who currently don’t currently use them.

According to the JRC, this can lead to 21-24 million hours saved per year across the EU27. Assuming average hourly wages as a basis, this is equivalent to approx. 473m to 543m EUR per year.

The impact per country can be seen below.

The Businesses’ Benefits

While the costs of setting up a business can vary greatly across the EU, doing so more efficiently will always make life easier.

A large range of online services is already available to business providers, from registering property to declaring taxes.

As with the citizens, the JRC assumes both a reduction in time spent by those who already use these processes (Scenario 1) and an uptick in the overall use of digital processes as they become more efficient (Scenario 2).

Currently, businesses across the EU27 spend 172 billion hours per year on setting up businesses. That’s around 250,000 lifetimes!

Increased data interoperability would save approximately 27.6 billion hours and 30 billion hours for scenario 1 and 2, respectively. That equates to 521bn EUR for scenario 1 and 561bn EUR for scenario 2. In each case, that’s more than the nominal GDP of Austria in 2021.

The impact per country can be seen below.

How do we get there?

The study clearly demonstrates the potential benefits of even a minor improvement in terms of data interoperability. The question that remains is how we can achieve that.

The EU has taken a long-term view on reaping the benefits of interoperability and making data more useful.

Interoperability and accessibility initiatives (such as INSPIRE) have allowed us to take crucial steps towards greater data interoperability. Tools such as hale»connect and hale»studio have streamlined the implementation of these standards into a partially automated process. We’ve seen hundreds of thousands of INSPIRE compliant datasets published with our tools - as a matter of fact, hale»connect and hale»studio have even set a world record for number of datasets published, as shown below (Komm.One is a hale»connect based data provider):

However, the impact of open data standards is merely the tip of the iceberg.

Going beyond data standards, the EU data strategy continues to emphasise the importance of more accessible and more useful data. It sets clear rules on the access and reuse of data and invests in the next generation of tools that store and process data.

Data spaces are the most important part of this new generation of data initiatives. By providing granular control over what is and is not shared between certain parties, data spaces comply with the governance rules dictated by the EU data strategy. This means that previously inaccessible data (for example, exact locations of endangered species or medical data) can be utilised to a greater extent without sacrificing data sovereignity, resulting in a much larger pool of data to draw from.

wetransform is determined to ensure that this larger data pool is not only available, but is also fully interoperable. Our product lines are being extended to support the creation and maintenance of data spaces. Drawing from our expertise in the implementation of open data standards, we are now deeply involved in the creation of the Environmental Data Space. You can learn more about the environmental data space community and get involved here.

Conclusion

Improved efficiency makes government processes take less time, which comes with very real benefits to data providers, businesses, and citizens.

While some of the study’s numbers are hypothetical, the scenarios presented accurately resemble real world conditions and a 1% improvement of the EGDI is attainable.

The potential impact on the bottom lines for data providers and businesses, as well as the benefit in terms of time spent for a nation’s citizens and government officials are also realistic predictions.

A fairly straightforward way to improve the efficiency of government processes is by improving the interoperability of data, which streamlines the flow of information between parties.

The best way to make data sets interoperable is by using ETL and publication tools, such as hale»studio and hale»connect. To learn more about how these tools aid in increasing the interoperability of data, check out our hale»studio and hale»connect pages!

A big thanks to Peter Ulrich, Nestor Duch Brown, Alexander Kotsev, Marco Minghini, Lorena Hernandez Quiros, Raymond Bogulawski, and Francesco Pignatelli for pulling this report together.

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We’ve brought in more features over the previous month to make your experience with hale»connect even better! Here’s what’s new:

Raster data publishing

hale»connect now supports the upload and publishing of .PNG and .GeoTIFF raster files. This enables you to include your raster data while publishing XPlanung and INSPIRE Land Use data. You can find this option in the “View Services” section in themes. Check out the new raster workflow document in our help section.

New Map View

hale»connect has a new map view based on Open Street Map and Leaflet. You can add your own WMS base map via the layer widget. You can also configure a default basemap configuration for your organization in the organization settings. Check out the documentation sections for more information.

Updated Map View
Map 1: INSPIRE Protected Sites in Germany

Deactivate Users

Administrators can now deactivate and reactivate users. Learn more here.

Autofill rule to populate dataset name

In the metadata section of a theme, users can now add an autofill rule to populate the dataset name. This enables users to select a dataset attribute, or other value, to generate the dataset name. Check out the metadata configuration option here.

Include attachments in atom feed

Haleconnect offers a new setting in the download services section of a theme. The new toggle switch “Include attachment links in Predefined Dataset download services” enables users to download attachments uploaded with their dataset directly from the atom feed. The documentation for this feature can be found here.

Attachment handling

There are now multiple ways to upload attachments on haleconnect. For customers interested in uploading XPlanung data which includes attachments, it is now possible to upload attachment during dataset creation. More information on this update can be found here.

If you have any questions, comments or concerns, don’t hesitate to contact us at info@wetransform.to

We’ve brought in more features over the previous month to make your experience with hale»connect even better! Here’s what’s new:

Raster data publishing

hale»connect now supports the upload and publishing of .PNG and .GeoTIFF raster files. This enables you to include your raster data while publishing XPlanung and INSPIRE Land Use data. You can find this option in the “View Services” section in themes. Check out the new raster workflow document in our help section.

New Map View

hale»connect has a new map view based on Open Street Map and Leaflet. You can add your own WMS base map via the layer widget. You can also configure a default basemap configuration for your organization in the organization settings. Check out the documentation sections for more information.

Updated Map View
Map 1: INSPIRE Protected Sites in Germany

Deactivate Users

Administrators can now deactivate and reactivate users. Learn more here.

Autofill rule to populate dataset name

In the metadata section of a theme, users can now add an autofill rule to populate the dataset name. This enables users to select a dataset attribute, or other value, to generate the dataset name. Check out the metadata configuration option here.

Include attachments in atom feed

Haleconnect offers a new setting in the download services section of a theme. The new toggle switch “Include attachment links in Predefined Dataset download services” enables users to download attachments uploaded with their dataset directly from the atom feed. The documentation for this feature can be found here.

Attachment handling

There are now multiple ways to upload attachments on haleconnect. For customers interested in uploading XPlanung data which includes attachments, it is now possible to upload attachment during dataset creation. More information on this update can be found here.

If you have any questions, comments or concerns, don’t hesitate to contact us at info@wetransform.to

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Each data format is made with a specific purpose. The data stored in the format, however, may be consumed by a multitude of different applications and users. Since the data format was designed with certain use cases in mind, it may not be well-suited for other functions. As such, it’s important to bridge the gap between a certain format and the needs of end users who want to work with the data.

To enhance data usability, other formats or encodings can be used to complement the default encoding. In the context of INSPIRE, this can be an alternative encoding, i.e. one that fulfills all requirements of the INSPIRE Implementing Rule and thus be used instead of the default encdoing, or it can be an additional encoding.

The goal of such encodings is to act as a link between the default encoding and a use case that is not addressed sufficiently by the default encoding. There are a few questions that must be answered while chosing or developing an encoding, such as:

  • Which kind of themes and use cases are you building the alternative encoding for?
  • What model transformation rules need to be applied to match the conceptual model to the capabilities of the format’s logical model?
  • How can this encoding be implemented?

Thorsten Reitz, CEO of wetransform, presented a webinar that answered these questions. The webinar also presented GeoJSON alternative encodings that are targeted at making INSPIRE data easily usable.

The default encoding for INSPIRE data is complex GML, which is well suited for the interchange of data. Since it has been made with the purpose of interoperability, it does well in terms of providing easy exchange of complex data. Common web applications and web APIs can perform standard operations on data formats that are not nested, however, and have difficulties doing so with nested data formats such as complex GML. According to the MIG Working group, “While INSPIRE data encoded according to the current schemas can be downloaded and viewed, simple use (visualisation, simple joins, visual overlays, spatial search, …) is difficult in standard GIS clients.

The webinar described how the GeoJSON encoding can help you gain maximum value from your INSPIRE data by making the data more compatible with GIS clients. The GeoJSON alternative encoding can be used instead of the nested INSPIRE GML data. GeoJSON is designed from the ground up to easily be consumed by web applications and web service APIs, thus providing for a use case that is not well-suited to INSPIRE GML. The webinar also covered the most common issues faced while trying to create an alternative encoding, the structure of GeoJSON encoding rules and model transformation rules. It mentioned how to measure the success of alternative encoding and looked at whether the GeoJSON alternative encoding succeeded in making INSPIRE data more usable in a specific target environment.

You can find a link to the webinar here.

Each data format is made with a specific purpose. The data stored in the format, however, may be consumed by a multitude of different applications and users. Since the data format was designed with certain use cases in mind, it may not be well-suited for other functions. As such, it’s important to bridge the gap between a certain format and the needs of end users who want to work with the data.

To enhance data usability, other formats or encodings can be used to complement the default encoding. In the context of INSPIRE, this can be an alternative encoding, i.e. one that fulfills all requirements of the INSPIRE Implementing Rule and thus be used instead of the default encdoing, or it can be an additional encoding.

The goal of such encodings is to act as a link between the default encoding and a use case that is not addressed sufficiently by the default encoding. There are a few questions that must be answered while chosing or developing an encoding, such as:

  • Which kind of themes and use cases are you building the alternative encoding for?
  • What model transformation rules need to be applied to match the conceptual model to the capabilities of the format’s logical model?
  • How can this encoding be implemented?

Thorsten Reitz, CEO of wetransform, presented a webinar that answered these questions. The webinar also presented GeoJSON alternative encodings that are targeted at making INSPIRE data easily usable.

The default encoding for INSPIRE data is complex GML, which is well suited for the interchange of data. Since it has been made with the purpose of interoperability, it does well in terms of providing easy exchange of complex data. Common web applications and web APIs can perform standard operations on data formats that are not nested, however, and have difficulties doing so with nested data formats such as complex GML. According to the MIG Working group, “While INSPIRE data encoded according to the current schemas can be downloaded and viewed, simple use (visualisation, simple joins, visual overlays, spatial search, …) is difficult in standard GIS clients.

The webinar described how the GeoJSON encoding can help you gain maximum value from your INSPIRE data by making the data more compatible with GIS clients. The GeoJSON alternative encoding can be used instead of the nested INSPIRE GML data. GeoJSON is designed from the ground up to easily be consumed by web applications and web service APIs, thus providing for a use case that is not well-suited to INSPIRE GML. The webinar also covered the most common issues faced while trying to create an alternative encoding, the structure of GeoJSON encoding rules and model transformation rules. It mentioned how to measure the success of alternative encoding and looked at whether the GeoJSON alternative encoding succeeded in making INSPIRE data more usable in a specific target environment.

You can find a link to the webinar here.

(more)