Category Archives: opendata

Available Energy Data in The Netherlands

Which energy data is available as open data in the Netherlands, asked Peter Rukavina. He wrote about postal codes on Prince Edward Island where he lives, and in the comments I mentioned that postal codes can be used to provide granular data on e.g. energy consumption, while still aggregated enough to not disclose personally identifiable data. This as I know he is interested in energy usage and production data.

He then asked:

What kind of energy consumption data do you have at a postal code level in NL? Are your energy utilities public bodies?
Our electricity provider, and our oil and propane companies are all private, and do not release consumption data; our water utility is public, but doesn’t release consumption data and is not subject (yet) to freedom of information laws.

Let’s provide some answers.

Postal codes

Dutch postal codes have the structure ‘1234 AB’, where 12 denotes a region, 1234 denotes a village or neighbourhood, and AB a street or a section of a street. This makes them very useful as geographic references in working with data. Our postal code begins with 3825, which places it in the Vathorst neighbourhood, as shown on this list. In the image below you see the postal code 3825 demarcated on Google maps.

Postal codes are both commercially available as well as open data. Commercially available is a full set. Available as open data are only those postal codes that are connected to addresses tied to physical buildings. This as the base register of all buildings and addresses are open data in the Netherlands, and that register includes postal codes. It means that e.g. postal codes tied to P.O. Boxes are not available as open data. In practice getting at postal codes as open data is still hard, as you need to extract them from the base register, and finding that base register for download is actually hard (or at least used to be, I haven’t checked back recently).

On Energy Utilities

All energy utilities used to be publicly owned, but have since been privatised. Upon privatisation all utilities were separated into energy providers and energy transporters, called network maintainers. The network maintainers are private entities, but are publicly owned. They maintain both electricity mains as well as gas mains. There are 7 such network maintainers of varying sizes in the Netherlands

(Source: Energielevernanciers.nl

The three biggest are Liander, Enexis and Stedin.
These network maintainers, although publicly owned, are not subject to Freedom of Information requests, nor subject to the law on Re-use of Government Information. Yet they do publish open data, and are open to data requests. Liander was the first one, and Enexis and Stedin both followed. The motivation for this is that they have a key role in the government goal of achieving full energy transition by 2050 (meaning no usage of gas for heating/cooking and fully CO2 neutral), and that they are key stakeholders in this area of high public interest.

Household Energy Usage Data

Open data is published by Liander, Enexis and Stedin, though not all publish the same type of data. All publish household level energy usage data aggregated to the level of 6 position postal codes (1234 AB), in addition to asset data (including sub soil cables etc) by Enexis and Stedin. The service areas of all 7 network maintainers are also open data. The network maintainers are also all open to additional data requests, e.g. for research purposes or for municipalities or housing associations looking for data to pan for energy saving projects. Liander indicated to me in a review for the European Commission (about potential changes to the EU public data re-use regulations), that they currently deny about 2/3 of data requests received, mostly because they are uncertain about which rules and contracts apply (they hold a large pool of data contributed by various stakeholders in the field, as well as all remotely read digital metering data). They are investigating how to improve on that respons rate.

Some postal code areas are small and contain only a few addresses. In such cases this may lead to personally identifiable data, which is not allowed. Liander, Stedin and I assume Enexis as well, solve this by aggregating the average energy usage of the small area with an adjacent area until the number of addresses is at least 10.

Our address falls in the service area of Stedin. The most recent data is that of January 1st 2018, containing the energy use for all of 2017. Searching for our postal code (which covers the entire street) in their most recent CSV file yields on lines 151.624 and 625:

click for full sizeclick to enlarge

The first line shows electricity usage (ELK), and says there are 33 households in the street, and the avarage yearly usage is 4599kWh. (We are below that at around 3700kWh / year, which is higher than we were used to in our previous home). The next line provides the data for gas usage (heating and cooking) “GAS”, which is 1280 m3 on average for the 33 connections. (We are slightly below that at 1200 m3).

Neighbourhood Solar in Vathorst in Data

At the edge of our neighbourhood, on a section of grassland, there are plans to create a solar farm. This is a temporary set-up as the land will eventually be used to build houses. Those living in the houses overlooking those fields started a petition as they fear it diminishes their view. There’s a whiff of nimby here, but it’s also justified resistance as it flies in the face of an earlier two year long participatory project by the city to determine with those who live here how to use those fields.

The petition I think didn’t gather a lot of signatures (just over 1100 now). I somewhat tongue in cheek asked the initiators online if there was also a petition I could sign in favour of the solar fields. The Netherlands after all is running far behind its own goals concerning renewables so I feel action on a wider scale is needed.

This led to forming a small group of people looking into what can be done towards more solar using existing roofs in our neighbourhood. A constructive outcome I think, even if I have little real time to contribute. In conversation with the group I offered to look into what data might be helpful, to both determine the actual potential of solar energy in our location (how much irradience hits the surface here, and what yield does that make possible), and the latent potential (based on the current energy usage at household level in our part of town.

Data on irradience is available. As is household electricity usage on postcode level, which means more or less to block level. What I haven’t really looked at if there is open data concerning roof space. The base register for buildings and addresses contains the shapes of buildings for every building in the Netherlands, but that is only in 2D, so it doesn’t provide the shape of non-flat roofs. Getting the roof shapes would require combining the BAG with AHN, the lidar scan of the Netherlands that contains all heights (trees, buildings and whatnot). The AHN however is created as snapshots. Our area is actively being developed, and houses are continuously being added. The latest AHN scan of our area was in 2010, so is heavily outdated. Luckily the new AHN3 (the 3rd AHN) scans for this region are scheduled for this year, and will be made available as open data. So at least we’ll have recent data to work with.

I intend to play around with this data to see if something can be said about potential and latent demand for solar energy in our area.

Danish Open Data Extension and Impact Growth

Last week the Danish government further extended the data available through their open data distributor, and announced some impressive resulting impact from already available data.

In 2012 the roadmap Good Basic Data for Everyone was launched, which set out to create an open national data infrastructure of the 5 core data sets used by all layers of government (maps, address, buildings, companies, people, see image). I attended the internal launch at the Ministry, and my colleague Marc contributed to the financial reasoning behind it (PDF 1, PDF 2). The roadmap ran until 2016, and a new plan is now in operation that builds on that first roadmap.


An illustration from the Danish 2012 road map showing how the 5 basic data registers correlate, and how maps are at its base.

Steadily data is added to those original 5 data sets, that increases the usability of the data. Last week all administrative geographic divisions were added (these are the geographic boundaries of municipalities, regions, 2200 parishes, jurisdictions, police districts, districts and zip-codes). This comes after last November’s addition of the place name register, and before coming May’s publication of the Danish address book. (The publication of the address database in 2002 was the original experience that ultimately led to the Basic Data program).

The primary goal of the Basic Data program has always been government efficiency, by ensuring all layers of government use the same core data. However the Danish government has also always recognised the societal and economic potential of that same data for citizens and companies, and therefore opening up the Basic Data registers as much as possible was also a key ingredient from the start. Interestingly the business case for the Basic Data program was only built on projected government savings, and those projections erred on the side of caution. Any additional savings realised by government entities would remain with them, so there was a financial incentive for government agencies to find additional uses for the Basic Data registers. External benefits from re-use were not part of the businesscase, as they were rightly seen as hard to predict and hard to measure, but were also estimated (again erring on the side of caution.) The projected savings for government were about 25 million Euro per year, and the project external benefits at some 65 million per year after completion of the system. Two years ago I transposed these Danish (as well as Dutch and other international) experiences with building an open national data infrastructure this way for the Swiss government, as part of a study with the FH Bern (PDF of some first insights presented at the 2016 Swiss open data conference in Lausanne).

Danish media this week reported new impact numbers from the geodata that has been made available. Geodata became freely available early 2013 as part of the Basic Data program. In 2017 the geodata saw over 6 billion requests for data, a 45% increase from 2016. Government research estimates the total gains in efficiency and productivity from using geodata for 2016 at some 470 million Euro (3.5 billion Danish Kroner). This is about 5 times the total of savings and benefits originally projected annually for the entire system back in 2012 (25 million savings, and 65 million in benefits).

It once again shows how there really is no rational case for selling government data, as the benefits that accrue from removing all access barriers will be much larger. This also means that government revenue will actually grow, as increased tax revenue will outstrip both lost revenue from data sales and costs of providing data. A timely and pertinent example from Denmark, now that I am researching the potential impact of open data for the Serbian government.

Can Your Freedom of Information Request Get You Killed?

Last month 27 year old Slovak journalist Jan Kuciak was murdered, together with his fiancée Martina Kušnírová. As an investigative journalist, collaborating with the OCCRP, he regularly submits freedom of information requests (FOI). Recent work concerned organized crime and corruption, specifically Italian organised crime infiltrating Slovak society. His colleagues now suspect that his name and details of what he was researching have been leaked to those he was researching by way of his FOI requests, and that that made him a target. The murder of Kuciak has led to protests in Slovakia, and the Interior Minister resigned last week because of it, and [update] this afternoon the Slovakian Prime Minister resigned as well. (The PM late 2016 referred to journalists as ‘dirty anti-Slovak prostitutes‘ in the context of anti-corruption journalism and activism)

There is no EU, or wider European, standard approach to FOI. The EU regulations for re-use of government information (open data) for instance merely say they build on the local FOI regime. In some countries stating your name and stating your interest (the reason you’re asking) is mandatory, in others one or both aren’t. In the Netherlands it isn’t necessary to state an interest, and not mandatory to disclose who you are (although for obvious reasons you do need to provide contact details to receive an answer). In practice it can be helpful, in order to get a positive decision more quickly to do state your own name and explain why you’re after certain information. That also seems to be what Jan Kuciak did. Which may have allowed his investigative targets to find out about him. In various instances, especially where a FOI request concerns someone else, those others may be contacted to get consent for publication. Dutch FOI law contains such a provision, as does e.g. Serbian law concerning the anticorruption agency. Norway has a tit-for-tat mechanism built in their public income and tax database. You can find out the income and tax of any Norwegian but only by allowing your interest being disclosed to the person whose tax filings you’re looking at.

I agree with Helen Darbishire who heads Access Info Europe who says the EU should set a standard that prevents requesters being forced to disclose their identity as it potentially undermines a fundamental right, and that requester’s identities are safeguarded by governments processing those requests. Access Info called upon European Parliament to act, in an open letter signed by many other organisations.

Data inventories for mature data governance in local government

This is the presentation I gave at the Open Belgium 2018 Conference in Louvain-la-Neuve this week, titled ‘The role and value of data inventories, a key step towards mature data governance’. The slides are embedded further below, and as PDF download at grnl.eu/in. It’s a long read (some 3000 words), so I’ll start with a summary.

Summary, TL;DR

The quality of information households in local governments is often lacking.
Things like security, openness and privacy are safeguarded by putting separate fences for each around the organisation, but those safeguards lack having detailed insight into data structures and effective corresponding processes. As archiving, security, openness and privacy in a digitised environment are basically inseparable, doing ‘everything by design’ is the only option. The only effective way is doing everything at the level of the data itself. Fences are inefficient, ineffective, and the GDPR due to its obligations will show how the privacy fence fails, forcing organisations to act. Only doing data governance for privacy is senseless, doing it also for openness, security and archiving at the same time is logical. Having good detailed inventories of your data holdings is a useful instrument to start asking the hard questions, and have meaningful conversations. It additionally allows local government to deploy open or shared data as policy instrument, and releasing the inventory itself will help articulate civic demand for data. We’ve done a range of these inventories with local government.

1: High time for mature data governance in local and regional government

Hight time! (clock in Louvain-la-Neuve)Digitisation changes how we look at things like openness, privacy, security and archiving, as it creates new affordances now that the content and its medium have become decoupled. It creates new forms of usage, and new needs to manage those. As a result of that e.g. archivists find they now need to be involved at the very start of digital information processes, whereas earlier their work would basically start when the boxes of papers were delivered to them.

The reality is that local and regional governments have barely begun to fully embrace and leverage the affordances that digitisation provides them with. It shows in how most of them deal with information security, openness and privacy: by building three fences.

Security is mostly interpreted as keeping other people out, so a fence is put between the organisation and the outside world. Inside it nothing much is changed. Similarly a second fence is put in place for determining openness. What is open can reach the outside world, and the fence is there to do the filtering. Finally privacy is also dealt with by a fence, either around the entire organisation or a specific system, keeping unwanted eyes out. All fences are a barrier between outside and in, and within the organisation usually no further measures are taken. All three fences exist separately from each other, as stand alone fixes for their singular purpose.

The first fence: security
In the Netherlands for local governments a ‘baseline information security’ standard applies, and it determines what information should be regarded as business critical. Something is business critical if its downtime will stop public service delivery, or of its lack of quality has immediate negative consequences for decision making (e.g. decisions on benefits impacting citizens). Uptime and downtime are mostly about IT infrastructure, dependencies and service level agreements, and those fit the fence tactic quite well. Quality in the context of security is about ensuring data is tamper free, doing audits, input checks, and knowing sources. That requires a data-centric approach, and it doesn’t fit the fence-around-the-organisation tactic.


The second fence: openness
Openness of local government information is mostly at request, or at best as a process separate from regular operational routines. Yet the stated end game is that everything should be actively open by design, meaning everything that can be made public will be published the moment it is publishable. We also see that open data is becoming infrastructure in some domains. The implementation of the digitisation of the law on public spaces, requires all involved stakeholders to have the same (access to) information. Many public sector bodies, both local ones and central ones like the cadastral office, have concluded that doing that through open data is the most viable way. For both the desired end game and using open data as infrastructure the fence tactic is however very inefficient.
At the same time the data sovereignty of local governments is under threat. They increasingly collaborate in networks or outsource part of their processes. In most contracts there is no attention paid to data, other than in generic terms in the general procurement conditions. We’ve come across a variety of examples where this results 1) in governments not being able to provide data to citizens, even though by law they should be able to 2) governments not being able to access their own data, only resulting graphs and reports, or 3) the slowest partner in a network determining the speed of disclosure. In short, the fence tactic is also ineffective. A more data-centric approach is needed.

The third fence: personal data protection
Mostly privacy is being dealt with by identifying privacy sensitive material (but not what, where and when), and locking it down by putting up the third fence. The new EU privacy regulations GDPR, which will be enforced from May this year, is seen as a source of uncertainty by local governments. It is also responded to in the accustomed way: reinforcing the fence, by making a ‘better’ list of what personal data is used within the organisation but still not paying much attention to processes, nor the shape and form of the personal data.
However in the case of the GDPR, if it indeed will be really enforced, this will not be enough.

GDPR an opportunity for ‘everything by design’
The GDPR confers rights to the people described by data, like the right to review, to portability, and to be forgotten. It also demands compliance is done ‘by design’, and ‘state of the art’. This can only be done by design if you are able to turn the rights of the GDPR into queries on your data, and have (automated) processes in place to deal with requests. It cannot be done with a ‘better’ fence. In the case of the GDPR, the first data related law that takes the affordances of digitisation as a given, the fence tactic is set to fail spectacularly. This makes the GDPR a great opportunity to move to a data focus not just for privacy by design, but to do openness, archiving and information security (in terms of quality) by design at the same time, as they are converging aspects of the same thing and can no longer be meaningfully separated. Detailed knowledge about your data structures then is needed.

Local governments inadvertently admit fence-tactic is failing
Governments already clearly yet indirectly admit that the fences don’t really work as tactic.
Local governments have been loudly complaining for years about the feared costs of compliance, concerning both openness and privacy. Drilling down into those complaints reveals that the feared costs concern the time and effort involved in e.g. dealing with requests. Because there’s only a fence, and usually no processes or detailed knowledge of the data they hold, every request becomes an expedition for answers. If local governments had detailed insight in the data structures, data content, and systems in use, the cost of compliance would be zero or at least indistinguishable from the rest of operations. Dealing with a request would be nothing more than running a query against their systems.

Complaints about compliance costs are essentially an admission that governments do not have their house in order when it comes to data.
The interviews I did with various stakeholders as part of the evaluation of the PSI Directive confirm this: the biggest obstacle stakeholders perceive to being more open and to realising impact with open data is the low quality of information systems and processes. It blocks fully leveraging the affordances digitisation brings.

Towards mature data governance, by making inventory
Changing tactics, doing away with the three fences, and focusing on having detailed knowledge of their data is needed. Combining what now are separate and disconnected activities (information security, openness, archiving and personal data protection), into ‘everything by design’. Basically it means turning all you know about your data into metadata that becomes part of your data. So that it will be easy to see which parts of a specific data set contain what type of person related data, which data fields are public, which subset is business critical, the records that have third party rights attached, or which records need to be deleted after a specific amount of time. Don’t man the fences where every check is always extra work, but let the data be able to tell exactly what is or is(n’t) possible, allowed, meant or needed. Getting there starts with making an inventory of what data a local or regional government currently holds, and describing the data in detailed operational, legal and technological terms.

Mature digital data governance: all aspects about the data are part of the data, allowing all processes and decisions access to all relevant material in determining what’s possible.

2: Ways local government data inventories are useful

Inventories are a key first step in doing away with the ineffective fences and towards mature data governance. Inventories are also useful as an instrument for several other purposes.

Local is where you are, but not the data pro’s
There’s a clear reason why local governments don’t have their house in order when it comes to data.
Most of our lives are local. The streets we live on, the shopping center we frequent, the schools we attend, the spaces we park in, the quality of life in our neighbourhood, the parks we walk our dogs in, the public transport we use for our commutes. All those acts are local.
Local governments have a wide variety of tasks, reflecting the variety of our acts. They hold a corresponding variety of data, connected to all those different tasks. Yet local governments are not data professionals. Unlike singular-task, data heavy national government bodies, like the Cadastre, the Meteo institute or the department for motor vehicles, local governments usually don’t have the capacity or capability. As a result local governments mostly don’t know their own data, and don’t have established effective processes that build on that data knowledge. Inventories are a first step. Inventories point to where contracts, procurement and collaboration leads to loss of needed data sovereignty. Inventories also allow determining what, from a technology perspective, is a smooth transition path to the actively open by design end-game local governments envision.

Open data as a policy instrument
Where local governments want to use the data they have as a way to enable others to act differently or in support of policy goals, they need to know in detail which data they hold and what can be done with it. Using open data as policy instrument means creating new connections between stakeholders around a policy issue, by putting the data into play. To be able to see which data could be published to engage certain stakeholders it takes knowing what you have, what it contains, and in which shape you have it first.

Better articulated citizen demands for data
Making public a list of what you have is also important here, as it invites new demand for your data. It allows people to be aware of what data exists, and contemplate if they have a use case for it. If a data set hasn’t been published yet, its existence is discoverable, so they can request it. It also enables local government to extend the data they publish based on actual demand, not assumed demand or blindly. This increases the likelihood data will be used, and increases the socio-economic impact.

Emerging data
More and more new data is emerging, from sensor networks in public and private spaces. This way new stakeholders and citizens are becoming agents in the public space, where they meet up with local governments. New relationships, and new choices result. For instance the sensor in my garden measuring temperature and humidity is part of the citizen-initiated Measure your city network, but also an element in the local governments climate change adaptation policies. For local governments as regulators, as guardian of public space, as data collector, and as source of transparency, this is a rebalancing of their position. It again takes knowing what data you own and how it relates to and complements what others collect and own. Only then is a local government able to weave a network with those stakeholders that connects data into valuable agency for all involved. (We’ve built a guidance tool, in Dutch, for the role of local government with regard to sensors in public spaces)

Having detailed data inventories are a way to start having the right conversations for local governments on all these points.

3: Getting to inventories

To create useful and detailed inventories, as I and my colleagues did for half a dozen local governments, some elements are key in my view. We looked at structured data collections only, so disregarded the thousands of individual once-off spreadsheets. They are not irrelevant, but obscure the wood for the trees. Then we scored all those data sets on up to 80(!) different facets, concerning policy domain, internal usage, current availability, technical details, legal aspects, and concerns etc. A key element in doing that is not making any assumptions:

  • don’t assume your list of applications will tell you what data you have. Not all your listed apps will be used, others won’t be on the list, and none of it tells you in detail what data actually is processed in them, just a generic pointer
  • don’t assume information management knows it all, as shadow information processes will exist outside of their view
  • don’t assume people know when you ask them how they do their work, as their description and rationalisation of their acts will not match up with reality,
    let them also show you
  • don’t assume people know the details of the data they work with, sit down with them and look at it together
  • don’t assume what it says on the tin is correct, as you’ll find things that don’t belong there (we’ve e.g. found domestic abuse data in a data set on litter in public spaces)

Doing an inventory well means

  • diving deeply into which applications are actually used,
  • talking to every unit in the organisation about their actual work and seeing it being done,
  • looking closely at data structures and real data content,
  • looking closely at current metadata and its quality
  • separately looking at large projects and programs as they tend to have their own information systems,
  • going through external communications as it may refer to internally held data not listed elsewhere,
  • looking at (procurement and collaboration) contracts to determine what claims other might have on data,
  • and then cross-referencing it all, and bringing it together in one giant list, scored on up to 80 facets.

Another essential part, especially to ensure the resulting inventory will be used as an instrument, is from the start ensuring the involvement and buy-in of the various parts of local government that usually are islands (IT, IM, legal, policy departments, archivists, domain experts, data experts). So that the inventory is something used to ask a variety of detailed questions of.

bring the islands together

Bring the islands together. (photo Dmitry Teslya CC-BY

We’ve followed various paths to do inventories, sometimes on our own as external team, sometimes in close cooperation with a client team, sometimes a guide for a client team while their operational colleagues do the actual work. All three yield very useful results but there’s a balance to strike between consistency and accuracy, the amount of feasible buy-in, and the way the hand-over is planned, so that the inventory becomes an instrument in future data-discussions.

What comes out as raw numbers is itself often counter-intuitive to local government. Some 98% of data typically held by Dutch Provinces can be public, although usually some 20% is made public (15% open data, usually geo-data). At local level the numbers are a bit different, as local governments hold much more person related data (concerning social benefits for instance, chronic care, and the persons register). About 67% of local data could be public, but only some 5% usually is. This means there’s still a huge gap between what can be open, and what is actually open. That gap is basically invisible if a local government deploys the three fences, and as a consequence they run on assumptions and overestimate the amount that needs the heaviest protection. The gap becomes visible from looking in-depth at data on all pertinent aspects by doing an inventory.

(Interested in doing an inventory of the data your organisations holds? Do get in touch.)

SODW Notes: 8 Citizen Generated and Local Sensor Data

This week, as part of the Serbian open data week, I participated in a panel discussion, talking about international developments and experiences. A first round of comments was about general open data developments, the second round was focused on how all of that plays out on the level of local governments. This is one part of a multi-posting overview of my speaking notes.

Citizen generated data and sensors in public space

As local governments are responsible for our immediate living environment, they are also the ones most confronted with the rise in citizen generated data, and the increase in the use of sensors in our surroundings.

Where citizens generate data this can be both a clash as well as an addition to professional work with data.
A clash in the sense that citizen measurements may provide a counter argument to government positions. That the handful of sensors a local government might employ show that noise levels are within regulations, does not necessarily mean that people don’t subjectively or objectively experience it quite differently and bring the data to support their arguments.
An addition in the sense that sometimes authorities cannot measure something within accepted professional standards. The Dutch institute for environment and the Dutch meteo-office don’t measure temperatures in cities because there is no way to calibrate them (as too many factors, like heat radiance of buildings are in play). When citizens measure those temperatures and there’s a large enough number of those sensors, then trends and patterns in those measurements are however of interest to those government institutions. The exact individual measurements are still of uncertain quality, but the relative shifts are a new layer of insight. With the decreasing prices of sensors and hardware needed to collect data there will be more topics for which citizen generated data will come into existence. The Measure Your City project in my home town, for which I have an Arduino-based sensor kit in my garden is an example.

There’s a lot of potential for valuable usage of sensor data in our immediate living environment, whether citizen generated or by corporations or local government. It does mean though that local governments need to become much more aware than currently of the (unintended) consequences these projects may have. Local government needs to be extremely clear on their own different roles in this context. They are the rule-setter, the one to safeguard our public spaces, the instigator or user, and any or all of those at the same time. It needs an acute awareness of how to translate that into the way local government enters into contracts, sets limits, collaborates, and provides transparency about what exactly is happening in our shared public spaces. A recent article in the Guardian on the ‘living laboratories’ using sensor data in Dutch cities such as Utrecht, Eindhoven, Enschede and Assen shines a clear light on the type of ethical, legal and technical awareness needed. My company has recently created a design and thinking tool (in Dutch) for local governments to balance these various roles and responsibilities. This ties back to my previous point of local governments not being data professionals, and is a lack of expertise that needs to addressed.

SODW Notes: 7 Local Data May Need National Coordination

This week, as part of the Serbian open data week, I participated in a panel discussion, talking about international developments and experiences. A first round of comments was about general open data developments, the second round was focused on how all of that plays out on the level of local governments. This is one part of a multi-posting overview of my speaking notes.

Local open data may need national data coordination

To use local open data effectively it may well mean that specific types of local data need to be available for an entire country or at least a region. Where e.g. real time parking data is useful even if it exists just for one city, for other data the interest lies in being able to make comparisons. Local spending data is much more interesting if you can compare with similar sized cities, or across all local communities. Similarly public transport data gains in usefulness if it also shows the connection with regional or national public transport. For other topics like performance metrics, maintenance, quality of public service this is true as well.

This is why in the Netherlands you see various regional initiatives where local governments join forces to provide data across a wider geographic area. In Fryslan the province, capital city of the province and the regional archive collaborate on providing one data platform, and are inviting other local governments to join. Similarly in Utrecht, North-Holland and Flevoland regional and local authorities have been collaborating in their open data efforts. For certain types of data, e.g. the real estate valuations that are used to decide local taxes, the data is combined into a national platform.

Seen from a developer’s perspective this is often true as well: if I want to build a city app that incorporates many different topics and thus data, local data is fine on its own. If I want to build something that is topic specific, e.g. finding the nearest playground, or the quality of local schools, then being able to scale it to national level may well be needed to make the application a viable proposition, regardless of the fact that the users of such an application are all only interested in one locality.

A different way of this national-local interaction is also visible. Several local governments are providing local subsets of national data sets on their own platforms, so it can be found and adopted more easily by locally interested stakeholders. An example would be for a local government to take the subset of the Dutch national address and buildings database, pertaining to their own jurisdiction only. This large data source is already open and contains addresses, and also the exact shapes of all buildings. This is likely to be very useful on a local level, and by providing a ready-to-use local subset local government saves potential local users the effort of finding their way in the enormous national data source. In that way they make local re-use more likely.

SODW Notes: 6 Local Targeted Outreach is Key

This week, as part of the Serbian open data week, I participated in a panel discussion, talking about international developments and experiences. A first round of comments was about general open data developments, the second round was focused on how all of that plays out on the level of local governments. This is one part of a multi-posting overview of my speaking notes.

Local outreach is key: open data as a policy instrument

Outreach to potential users of open data is needed, to see open data being adopted. Open data can help people and groups to change the way they do things or make decisions. It is a source of agency. Only where such agency is realized does open data create the promised value.

When local governments realize you can do this on purpose, then open data becomes a policy instrument. By releasing specific data, and by reaching out to specific stakeholders to influence behavior, open data is just as much a policy instrument as is setting regulations or providing subsidies and financing. This also means the effort and cost of open data initiatives is no longer seen as non-crucial additions to the IT budget, but gets to be compared to the costs of other interventions in the policy domain where it is used. Then you e.g. compare the effort of publishing real time parking data with measures like blocking specific roads, setting delivery windows, or placing traffic lights, as they are all part of a purposeful effort to reduce inner city traffic. In these comparisons it becomes clear how cheap open data efforts really are.

To deploy open data as a policy instrument, the starting point is to choose specific policy tasks, and around that reach out to external stakeholders to figure out what these stakeholders need to collaboratively change behaviours and outcomes.
E.g. providing digital data on all the different scenario’s for the redesign of a roundabout or busy crossing allows well informed discussions with people living near that crossing, and allows the comparison of different perspectives. In the end this reduces the number of complaints in the planning phase, increases public support for projects and can cut planning and execution time by months.

These type of interventions result in public savings and better public service outcomes, as well as in increased trust between local stakeholders and government.

SODW Notes: 5 Local is where you live, but not the data pro’s

This week, as part of the Serbian open data week, I participated in a panel discussion, talking about international developments and experiences. A first round of comments was about general open data developments, the second round was focused on how all of that plays out on the level of local governments. This is one part of a multi-posting overview of my speaking notes.

Local is where you are, but not the data professionals

The local government is closest to our everyday lives. The street we live on, the way we commute to our work, the schools our children attend, the shopping we do and where we park our vehicles for it, the trash to take away, the quality of life in our immediate surroundings, most if not all is shaped by what local government does. Using open data here means potentially the biggest impact for citizens.

This effect is even stronger where many tasks are delegated to local and regional levels of government and where central government is less seen to be leading on open data. This is the case in for instance Germany. In the past years the states and especially municipalities have been the trail blazers in Germany for open data. This because also important things like taking in refugees is very much a local communal matter. This has resulted in open data apps to help refugees navigate German bureaucracy, learn the local language, and find local volunteers to connect to. Similar initiatives were visible in Serbia, e.g. the Techfugee hackathons. In the Netherlands in recent years key tasks on social welfare, youth care and health care have been delegated to the local level.

There is however a crucial difference between local government and many national public sector bodies. At national level many institutions are data professionals and they are focused on one specific domain or tasks. These are for instance the national statistics body, the cadastral offices, the meteorological institute, the highway authorities, or the business register. Municipalities on the other hand are usually not data professionals. Municipalities have a wide variety of tasks, precisely because they are so close to our everyday lives. This is mirrored in the variety of types of data they hold. However local governments in general have a less well developed overall understanding of their information systems, let alone of which data they hold.

This is also apparent from the work I did to help evaluate the EU PSI Directive: where the maturity of the overall information household is lower, it is much harder to embed or do open data well and in a sustainable manner. The lack of mature data governance is holding open data progress and impact back.

SODW Notes: 4 Fragmentation of Community, Sectoral Splits

This week, as part of the Serbian open data week, I participated in a panel discussion, talking about international developments and experiences. This is one part of a multi-posting overview of my speaking notes.

On the fragmentation of community, and the withdrawal into sectors

When open data was in the phase where it was mostly about awareness raising, it was also very much an internationally connected network of people involved. They would meet up regularly at various pan-European events, and frequently exchange experiences. It seems to me that has changed, and that broad network has fragmented. I realize this is caused by the need to focus on actual projects and implementation work, and also by open data becoming more common place. That open data has become a more routine part of various other work and initiatives means also open data is becoming a point of discussion in events not centered on open data. It is a sign of increasing maturity, but we’re also losing something.

The fragmentation of the European network of people interested in open data, means we all are generally less aware of what is happening elsewhere, the solutions others find in overcoming organizational barriers to openness, the ways other groups find valuable ways to use open data etc. It can also mean stakeholders don’t realize opportunities or solutions are within their reach, and have already been done elsewhere. This then means a reduction in the agency of those stakeholders, while the stated intent of opening data is to increase that very same agency.

There are many active open data efforts in many countries, and it is now usually a more integral part of how various sectors organise themselves. In the geo sector as well as e.g. in journalism awareness of data is alive and well. Next month e.g. there’s the International Journalism Festival, and at least one panel there focuses on data (Titled “conversations with data”). Within data journalism there is currently more focus on investigative work, and that usually means it’s not focused on openly available data as much. In other sectors we see similar things. In academic research circles that depend on shared infrastructure (think the LHC at CERN in Geneva, or radio telescopes), data sharing is common too. In other research circles data awareness may be less developed yet. Archiving is another sector where attention for data has become commonplace. However all those efforts are less connected to general open data efforts, and less part of a shared understanding or narrative. This reduces the potential re-use of eachother’s insights and experiences and again diminishes the speed of development and overall impact.