Observations on the web

Eight years ago I was invited to a workshop. The Office for National Statistics were gathering together people from the statistics and linked data communities to talk about publishing statistics on the web.

At the time there was lots of ongoing discussion within and between the two communities around this topic. With a particular emphasis on government statistics.

I was invited along to talk about how publishing linked data could help improve discovery of related datasets.

Others were there to talk about other related projects. There were lots of people there from the SDMX community who were working hard to standardise how statistics can be exchanged between organisations.

There’s a short write-up that mentions the workshop, some key findings and some follow on work.

One general point of agreement was that statistical data points or observations should be part of the web.

Every number, like the current population of Bath & North East Somerset, should have a unique address or URI. So people could just point at it. With their browsers or code.

Last week the ONS launched the beta of a new API that allows you to create links to individual observations.

Seven years on they’ve started delivering on the recommendations of that workshop.

Agreeing that observations should have URIs was easy. The hard work of doing the digital transformation required to actually deliver it has taken much longer.

Proof-of-concept demos have been around for a while. We made one at the ODI.

But the patient, painstaking work to change processes and culture to create sustainable change takes time. And in the tech community we consistently underestimate how long that takes, and how much work is required.

So kudos to Laura, Matt, Andy, Rob, Darren Barnee and the rest of present and past ONS team for making this happen. I’ve see glimpses of the hard work they’ve had to put in behind the scenes. You’re doing an amazing and necessary job.

If you’re unsure as to why this is such a great step forward, here’s a user need I learned at that workshop.

Amongst the attendees was a designer who worked on data visualisations. He would spend a great deal of time working with data to get it into the right format and then designing engaging, interactive views of it.

Often there were unusual peaks and troughs in the graphs and charts which needed some explanation. Maybe there had been an external event that impacted the data, or a change in methodology. Or a data quality issue that needed explaining. Or maybe just something interesting that should be highlighted to users.

What he wanted was a way for the statisticians to give him that context, so he could add notes and explanations to the diagrams. He was doing this manually and it was a lot of time and effort.

For decades statisticians have been putting these useful insights into the margins of their work. Because of the limitations of the printed page and spreadsheet tables this useful context has been relegated into footnotes for the reader to find for themselves.

But by putting this data onto the web, at individual URIs, we can deliver those numbers in context. Everything you need to know can be provided with the statistic, along with pointers to other useful information.

Giving observation unique URIs, frees statisticians from the tyranny of the document. And might help us all to share and discuss data in a much richer way.

I’m not naive enough to think that linking data can help us address issues with fake news. But it’s hard for me to imagine how being able to more easily work with data on the web isn’t at least part of the solution.

The building blocks of data infrastructure – Part 2

This is the second part of a two part post looking at the building blocks of data infrastructure. In part one we looked at definitions of data infrastructure, and the first set of building blocks: identifiers, standards and registers. You should read part one first and then jump back in here.

We’re using the example of weather data to help us think through the different elements of a data infrastructure. In our fictional system we have a network of weather stations installed around the world. The stations are feeding weather observations into a common database. We’ve looked at why its necessary to identify our stations, the role of standards, and the benefits of building registers to help us manage the system.

Technology

Technology is obviously part of data infrastructure. In part one we have already introduced several types of technology.

The sensors and components that are used to build the weather stations are also technologies.

The data standards that define how we organise and exchange data are technologies.

The protocols that help us transmit data, like WiFi or telecommunications networks, are technologies.

The APIs that are used to submit data to the global database of observations, or which help us retrieve observations from it, are also technologies.

Unfortunately, I often see some mistaken assumptions that data infrastructure is only about the technologies we use to help us manage and exchange data.

To use an analogy, this is a bit like focusing on tarmac and kerb stones as the defining characteristics of our road infrastructure. These materials are both important and necessary, but are just parts of a larger system. If we focus only on technology it’s easy to overlook the other, more important buildings blocks of data infrastructure.

We should be really clear when we are talking about “data infrastructure”, which encompasses all the building blocks we are discussing here and when we are talking about “infrastructure for data” which focuses just on the technologies we use to collect and manage data.

Technologies evolve and become obsolete. Over time we might choose to use different technologies in our data infrastructure.

What’s important is choosing technologies that ensure our data infrastructure is as reliable, sustainable and as open as possible.

Organisations

Our data infrastructure is taking shape. We now have a system that consists of weather stations installed around the world, reporting local weather observations into a central database. That dataset is the primary data asset that we will be publishing from our data infrastructure.

We’ve explored the various technologies, data standards and some of the other data assets (registers) that enable the collection and publishing of that data.

We’ve not yet considered the organisations that maintain and govern those assets.

The weather stations themselves will be manufactured and installed by many different organisations around the world. Other organisations might offer services to help maintain and calibrate stations after they are installed.

A National Meteorological Service might take on responsibility for maintaining the network of stations within it’s nation’s borders. The scope of their role will be defined by national legislation and policies. But a commercial organisation might also choose to take on responsibility for running a collection of stations.

In our data infrastructure, the central database of observations will be curated and managed by a single organisation. The (fictional) Global Weather Office. Our Global Weather Office will do more than just manage data assets. It also has a role to play in choosing and defining the data standards that support data collection. And it helps to certify which models of weather station conform to those standards.

Organisations are a key building block of data infrastructure. The organisational models that we choose to govern a data infrastructure and which take responsibility for its sustainability, are an important part of its design.

The value of the weather observations comes from their use. E.g. as input into predictive models to create weather forecasts and other services. Many organisations will use the observation data provided by our data infrastructure to create a range of products and services. E.g. national weather forecasts, or targeted advice for farmers that is delivered via farm management systems. The data might also be used by researchers. Or by environmental policy-makers to inform their work.

Mapping out the ecosystem of organisations that operate and benefit from our data infrastructure will help us to understand the roles and responsibilities of each organisation. It will also help clarify how and where value is being created.

Guidance and Policies

With so many different organisations operating, governing and benefiting from our data infrastructure we need to think about how they are supported in creating value from it.

To do this we will need to produce a range of guidance and policies, for example:

  • Documentation for all of the data assets that helps to put them in context, allowing them to be successfully used to create products and services. This might include notes on how we have collected our data, the standards used, and locations of our stations.
  • Recommendations for how data should be processed and interpreted to ensure that weather forecasts that use the data are reliable and safe
  • Licences that define how the data assets can be used
  • Documentation that describes the data governance processes that are applied to the data assets
  • Policies that define how organisations gain access to the data infrastructure, e.g. to start supplying data from new stations
  • Policies that decide how, when and where new stations might be added to the global network, to ensure that global coverage is maintained
  • Procurement policies that define how stations and the services that relate to them purchased
  • National regulations that apply to manufacture of weather stations, or that set safety standards that apply when they are installed or serviced
  • …etc

Guidance and policies are an important building block that help to shape the ecosystem that supports and benefits from our data infrastructure.

A strong data infrastructure will have policies and governance that will support equitable access to the system. Making infrastructure as open as possible will help to ensure that as many organisations as possible have the opportunity to use the assets it provides, and have equal opportunities to contribute to its operation.

Community

Why do we collect weather data? We do it to help create weather forecasts, monitor climate change and a whole host of other reasons. We want the data to be used to make decisions.

Many different people and organisations might benefit from the weather data we are providing. A commuter might just want to know whether to take an umbrella to work. A farmer might want help in choosing which crops to plant. Or an engineer planning a difficult construction task made need to know the expected weather conditions.

Outside of the organisations who are directly interacting with our data infrastructure there will be a number of communities, made up of both individuals and organisations who will benefit from the products and services made with the data assets it provides. Communities are the final building block of our data infrastructure.

These communities will be relying on our data infrastructure to plan their daily lives, activities and to make business decisions. But they may not realise it. Good infrastructure is boring and reliable.

In his book on the social value of infrastructure, Brett Frischmann refers to infrastructure as “shared means to many ends”. Governing and maintaining infrastructure requires us to recognise this variety of interests and make choices that balances a variety of needs. 

The choices we make about who has access to our data infrastructure, and how it will be made sustainable, will be important in ensuring that value can be created from it over the long-term.

Reviewing our building blocks

To summarise, our building blocks of data infrastructure are:

  • Identifiers
  • Standards
  • Registers
  • Technology, of various kinds
  • Organisations, who create, maintain, govern and use our infrastructure
  • Guidance and Policies that inform is use
  • Communities who are impacted or are affected by it

The building blocks have different sizes. Identifiers are a well-understood technical concept. Organisations, policies and communities are more complex, and perhaps less well-defined.

Understanding their relationships, and how they benefit from being more open, requires us to engage in some systems thinking. By identifying each building block I hope we can start to have deeper conversations about the systems we are building.

Over time we might be able to tease out more specific building blocks. We might be able to identify important organisational roles that occur as repeated patterns across different types of infrastructure. Or specific organisational models that have been found to be successful in creating trusted, sustainable infrastructures. Over time we might also identify key types of policy and guidance that are important elements of ensuring that a data infrastructure is successful. These are research questions that can help us refine our understanding of data as infrastructure.

There are other aspects of data infrastructure which we have not explicitly explored here. For example ethics and trust. This is because ethics is not a building block. It’s a way of working that will enable fairer, equitable access to data infrastructure by a variety of communities. Ethics should inform every decision and every activity we take to design, build and maintain our data infrastructure.

Trust is also not a building block. Trust emerges from how we operate and maintain our data infrastructures. Trust is earned, rather than designed into a system.

Help me make this better

I’ve written these posts to help me work through some of my thoughts around data infrastructure. There’s a lot more to be said about the different building blocks. And choosing other examples, e.g. that focus on data infrastructure that involves sharing of personal data like medical records, might better highlight some different characteristics.

Let me know what you think of this breakdown. Is it useful? Do you think I’ve missed some building blocks? Leave a comment or tweet me your thoughts.

Thanks to Peter Wells and Jeni Tennison for feedback and suggestions that have helped me write these posts.

The building blocks of data infrastructure – Part 1

Data is a vital form of infrastructure for our societies and our economies. When we think about infrastructure we usually think of physical things like roads and railways.

But there are broader definitions of infrastructure that include less tangible things. Like ideas or the internet.

It is important to recognise that there is more to “infrastructure” than just roads and railways. Otherwise there is a risk that we, as a society, won’t invest the necessary time or effort in building, maintaining and governing that infrastructure. The decisions we make about Infrastructure are important because infrastructure helps to shape our societies.

To help explore the idea of data as infrastructure, I want to look at the various building blocks that make up a specific example of a data infrastructure. My hope is that this will help to make it clearer that “data infrastructure” is about more than just technology. As we will see the technical infrastructure we use to manage data is just one component of data infrastructure.

The example we will use is greatly simplified and is partly fictionalised, but it is essentially a real example: we’re going to look at weather data infrastructure.

I’ve written about weather data infrastructure before. It’s a really interesting example to explore in this context because:

  • it’s easy to understand the value of collecting weather data
  • it’s a complex enough example to help dig into some real-world issues
  • It illustrates how data that is collected and used locally or nationally can also be part of a global data infrastructure

Weather data is usually open data, or at least public. But the building blocks we will outline here apply equally well to data from across the data spectrum. In a follow-up post I may explore a more complex example that illustrates a different type of data infrastructure, e.g. one for medical research that relies on researchers having access to medical records.

In the following sections we’ll look at the different building blocks that are important in building a global weather data infrastructure. The real infrastructure is much more complex.

Some of the building blocks are a bit fuzzy and have multiple roles to play in our infrastructure. But that’s fine. The world isn’t a neat and tidy place that we can always reduce to simpler components.

A definition of data infrastructure

Before we begin, let’s introduce a definition of data infrastructure:

A data infrastructure consists of data assets, the standards and technologies that are used to curate and provide access to those assets, the guidance and policies that inform their use and management, the organisations that govern the data infrastructure, and the communities involved in maintaining it, or are impacted by decisions that are made using those data assets.

There are a lot of moving parts there. And there are lots of things to say about each of them. For now let’s focus on the individual building blocks to explore ways in which they fit together.

Identifiers

Imagine we’re planning to build a global network of weather stations. Each station will be regularly recording the local temperature and rainfall. In our system we’ll be collecting all of these readings into a global dataset of weather observations.

So that we know which observations have been reported by which weather station, we need a unique reference for each of them.

We can’t just use the name of the town or village in which the station has been installed as that reference. There are Birminghams in both the UK and the US, for example. We might also need to move and reinstall weather stations over time, but may need to track information about them, such as when they were installed or services. So we need a global identifier that is more reliable than just a name.

By assigning each weather station a unique identifier, we can then attached additional data to it. Like it’s current location. We can also associate the identifier with every temperature and rainfall observation, so that we know which station reported that data.

Identifiers are the first building block of our data infrastructure.

Identifiers are deceptively simple. They’re just a number or a code, right? But there’s a lot to say about them, such as how they are assigned or are formatted. It can be hard to create good identifiers.

When identifiers are open, for anyone to use in their data, they have a role to play that goes beyond just providing unique references in a database. They can also help to create network effects that encourage publication of additional data.

Standards, part 1

Our weather stations are recording temperature and rainfall. We’ll measure temperature in degrees Centigrade and rainfall in millimetres. Both of these are standard units of measurement.

Standards are our second building block.

Standards are documented, reusable agreements. They help us collect and organise data in consistent ways, and make it easier to work with data from different sources.

Some standards, like units of measurement are global and are used in many different ways. But some standard might be only be relevant to specific communities or systems.

In our weather data infrastructure, we will need to standardise some other aspects of how we plan to collect weather data.

For example, let’s assume that our weather stations are recording data every half an hour. Every thirty minutes a station will record a new temperature reading. But is it recording the temperature at that specific moment in time, or should it report the average temperature over the last thirty minutes? There may be advantages in doing one or the other.

If we don’t standardise some of our data collection practices, then weather stations created by different manufacturers might record data differently. This will affect the quality of our data.  

Standards, part 2

Every data infrastructure will rely on a wide variety of different standards. Some standards support consistent measurement and data collection. Others help us to exchange data more effectively.

Our weather stations will need to record the data they collect and automatically upload it to a service that helps us build our global database. In a real system there are a number of different ways in which we might want a weather station to report data, to provide a variety of ways in which it could be aggregated and reused. But to simplify things, we’ll assume they just upload their data to a centralised service. Centralised data collection is problematic for a number of reasons, but that’s a topic for another article.

To help us define how the weather stations will upload their data we will need to pick a standard data format that will define the syntax for recording data in a machine-readable form. Let’s assume that we decide to use a simple CSV (comma-separated values) format.

Each station will produce a CSV file that contains one row for every half-hourly observation. Each row will consist of a station identifier, a time stamp for the recordings, a temperate reading and a rainfall reading.

The time stamps can be recorded using ISO 8601, which is an international standard for formatting dates and times. Helpfully we can include time zones, which will be essential for reporting time accurately across our global network of weather stations.

We also need to ensure that the order in which the four fields will be reported is consistent, or that the headers in the CSV file clearly identify what is contained in each column. Again, we might be using weather stations from multiple manufacturers and need data to be recorded consistently. Some stations might also include additional sensors, e.g. to record wind speed. So ideally our standard will be extensible to support that additional data. Taking time to design and standardise our CSV format will make data aggregation easier.

Every time we define how to collect, manage or share data within a system, we are creating agreements that will help ensure that everyone involved in those processes can be sure that those tasks are carried out in consistent ways. When we reuse existing standards, rather than creating bespoke versions, we can benefit from the work of thousands of different specialists across a variety of industries.

Sometimes though we do need to define a new standard, like the order of the columns in our specific type of CSV file. But where possible we should approach this by building on existing standards as much as possible.

Registers

To help us manage our network of weather stations it will be useful to record where each of them has been installed. It would also be helpful to record when they were installed. Then we can figure out when they might need to be re-calibrated or replaced and send some out to do the necessary work.

To do this, we can create a dataset, that lists the identifier, location, model and installation date of every weather station.

This type of dataset a register.

Registers are lists of important data. They have multiple uses, but are most frequently used to help us improve the quality of our data reporting.

For example we can use the above register to confirm that we’re regularly receiving data from every station on the network. When a station is installed it will need to get added to the register. We might give the company installing the station permission to do that, to help us maintain the register.

We can also use the register to determine if we have a good geographic spread of stations, to help us assess and improve the coverage and quality of the observations we’re collecting. The register is also useful for anyone using our global dataset so they can see how the dataset has been collected over time. Registers should be as open as possible.

There are other types of register that might be useful for governing our data infrastructure. For example we might create a register that lists all of the models of weather station that have been certified to comply with our preferred data standards.

We can use that register to help us make decisions about how to replace stations when they fail. A register can also help provide an incentive for the manufacturers of weather stations to conform to our chosen standards. If they’re not on the list, then we might not buy their products.

In Part 2 of this post we’ll look at others aspects of data infrastructure, including technology, organisations and policies. Thanks to Peter Wells and Jeni Tennison for feedback and suggestions that have helped me write these posts.

Creating better checklists, a short review of the Checklist Manifesto

I’ve just finished reading The Checklist Manifesto by Atul Gawande (Cancer Research UK affiliate link). It’s been on my reading list for a while. In my work I’ve written quite a few checklists to help capture best practice or to provide advice. So I was curious about whether I could learn something about creating better checklists.

I wanted to write down a few reflections whilst they’re still fresh in my mind.

The book explores the use of checklists in medicine, aviation and to a lesser extent in business. Checklists aren’t to-do lists. They are a tool to help reduce risk, uncertainty and failure. Gawande uses ample anecdotes, supported by evidence from real-world studies, to illustrate how effective a simple checklist can be. They routinely save people’s lives during surgeries and are a key contributor to the safety of modern aviation.

Gawande explains how checklists allow teams to perform better in complex situations. They protect against individual fallibility, and can help to transfer best practice and research into operational use. He explains that checklists aren’t a teaching tool. They are a means of imposing discipline on a team. Their goal is to improve outcomes.

He also explains why he thinks they’re not being used more widely. In particular, he highlights the tendency of professionals to feel like they’re being undermined or challenged when asked to use simple checklists. A “hero culture” contributes to this problem, something that is very evident in surgery. It’s also something that the tech industry struggles with as well.

Gawande explains that checklists help to address this by re-balancing power within teams. For example, by giving nurses the permission to halt a surgical procedure if a checklist hasn’t been completed to their satisfaction. A hero culture might otherwise silence the raising of concerns, or deter team members from pointing out problems as they see them.

The book highlights that a common pattern that occurs in many successful checklists are specific steps to encourage and make time for team communication. These range from simple introductions and a review of responsibilities, through to a walk-through of expected and possible outcomes. These all contribute towards making the team a more effective unit.

Throughout the book I was wondering how to transfer the insight into other areas. Gawande suggests that checklists are useful anywhere that we have multi-disciplinary teams working together on complex tasks. And specifically where those tasks might have complex outcomes that might have serious impacts.

I think there’s probably a lot of examples in the data and digital world where they might be useful.

What if teams working on data science and machine learning had “preflight checklists” that were used not just at the start of a project, but also at the time of launch and beyond? Would they help highlight problems, increase discipline and allow times for missteps or other concerns to be highlighted?

The ODI data ethics canvas, developed by Amanda Smith, Ellen Broad and Peter Wells is not quite a checklist. But it’s a similar type of tool, aiming to address some similar problems. Privacy impact assessments are another example. But perhaps there are other useful aids?

The book also raises wider questions about the approach we take in our societies towards ensuring safe outcomes of our work, research, etc. There is often too much focus on the use and application of exciting new research and technologies, and not enough on the discipline required to use them safely and effectively.

In short, are we taking care of one another in the best ways we know how?

Creating better checklists

There’s some great insight into creating checklists scattered throughout the Manifesto. But ironically, they’re not gathered together into a single list of suggestions.

So, for my own benefit I’ve jotted down some points to reflect on:

  • Checklists need to be focused. An exhaustive list of steps is not useful. Trust people know how to do their job, just ask them to confirm the most critical or important steps
  • Think about how the checklist will be used. There are READ-DO checklists (where you perform each item and check it off) and DO-CONFIRM checklists (where you carry out an activity, and then review what you have done)
  • Checklists can be used to help with both routine situations (pre-flight) and emergencies (engine failure)
  • Make the checklist easy to use. A good user experience can help embed them into routine practice
  • Consider who is leading use of the checklist. A checklist can help to balance power across a team
  • Include team communications. Teams perform better when they know each other and understand their roles. Ask them to explain what will happen and what the expected outcomes might be. This helps teams deal with items not on the checklist
  • Test and iterate the list
  • Let people customise it, so they can adapt to local use
  • Measuring success and impact (e.g. by measuring outcomes, or even just identifying where they have helped) can help encourage others to adopt it

 

When are open (geospatial) identifiers useful?

In a meeting today, I was discussing how and when open geospatial identifiers are useful. I thought this might make a good topic for a blog post in my continuing series of questions about data. So here goes.

An identifier provides an unambiguous reference for something about which we want to collect and publish data. That thing might be a road, a school, a parcel of land or a bus stop.

If we publish a dataset that contains some data about “Westminster” then, without some additional documentation, a user of that dataset won’t know whether the data is about a tube station, the Parliamentary Constituency, a company based in Hayes or a school.

If we have identifiers for all of those different things, then we can use the identifiers in our data. This lets us be confident that we are talking about the same things. Publishing data about “940GZZLUWSM” makes it pretty clear that we’re referring to a specific tube station.

If data publishers use the same sets of identifiers, then we can start to easily combine your dataset on the wheelchair accessibility of tube stations, with my dataset of tube station locations and Transport for London’s transit data. So we can build an application that will help people in wheelchairs make better decisions about how to move around London.

Helpful services

To help us publish datasets that use the same identifiers, there are a few things that we repeatedly need to do.

For example it’s common to have to lookup an identifier based on the name of the thing we’re describing. E.g. what’s the code for Westminster tube station? We often need to find information about an identifier we’ve found in a dataset. E.g. what’s the name of the tube station identified by 940GZZLUWSM? And where is it?

When we’re working with geospatial data we often need to find identifiers based on a physical location. For example, based on a latitude and longitude:

  • Where is the nearest tube station?
  • Or, what polling district am I in, so I can find out where I should go to vote?
  • Or, what is the identifier for the parcel of land that contains these co-ordinates?
  • …etc

It can be helpful if these repeated tasks are turned into specialised services (APIs) that make it easier to perform them on-demand. The alternative is that we all have to download and index the necessary datasets ourselves.

Network effects

Choosing which identifiers to use in a dataset is an important part of creating agreements around how we publish data. We call those agreements data standards.

The more datasets that use the same set of identifiers, the easier it becomes to combine those datasets together, in various combinations that will help to solve a range of problems. To put it another way, using common identifiers helps to generate network effects that make it easier for everyone to publish and use data.

I think it’s true to say that almost every problem that we might try and solve with better use of data requires the combination of several different datasets. Some of those datasets might come from the private sector. Some of them might come from the public sector. No single organisation always holds all of the data.

This makes it important to be able to share and reuse identifiers across different organisations. And that is why it is important that those identifiers are published under an open licence.

Open licensing

Open licences allow anyone to access, use and share data. Openly licensed identifiers can be used in both open datasets and those that are shared under more restrictive licences. They give data publishers the freedom to choose the correct licence for their dataset, so that it sits at the right point on the data spectrum.

Identifiers that are not published under an open licence remove that choice. Restricted licensing limits the ability of publishers to share their data in the way that makes sense for their business model or application. Restrictive licences cause friction that gets in the way of making data as open as possible.

Open identifiers create open ecosystems. They create opportunities for a variety of business models, products and services. For example intermediaries can create platforms that aggregate and distribute data that has been published by a variety of different organisations.

So, the best identifiers are those that are

  • published under an open licence that allows anyone to access, use and share them
  • published alongside some basic metadata (a label, a location or other geospatial data, a type)
  • and, are accessible via services that allow them to be easily used

Who provides that infrastructure?

Whenever there is friction around the use of data, application developers are left with a difficult choice. They either have to invest time and effort in working around that friction, or compromise their plans in some way. The need to quickly bring products to market may lead to choices which are not ideal.

For example, developers may choose to build applications against Google’s mapping services. These services are easily and immediately available for anyone developer wanting to display a map or recommend a route to a user. But these platforms usually have restricted licensing that means it is usually the platform provider that reaps the most benefits. In the absence of open licences, network effects can lead to data monopolies.

So who should provide these open identifiers, and the metadata and services that support them?

This is the role of national mapping agencies. These agencies will already have identifiers for important geospatial features. The Ordnance Survey has an identifier called a TOID which is assigned to every feature in Great Britain. But there are other identifiers in use too. Some are designed to support publication of specific types of data, e.g. UPRNs.

These identifiers are national assets. They should be managed as data infrastructure and not be tied up in commercial data products.

Publishing these identifiers under an open licence, in the ways that have been outlined here, will provide a framework to support the collection and curation of geospatial data by many  different organisations, across the public and private sector. That infrastructure will allow value to be created from that geospatial data in a variety of new ways.

Provision of this type of infrastructure is also in-line with what we can see happening across other parts of government. For example the work of the GDS team to develop registers of important data. Identifiers, registers and standards are important building blocks of our local, national and global data infrastructure.

If you’re interested in reading more about the benefits of open identifiers, then you might be interested in this white paper that I wrote with colleagues from the Open Data Institute and Thomson Reuters: “Creating value from identifiers in an open data world

Data assets and data products

A lot of the work that we’ve done at the ODI over the last few years has involved helping organisations to recognise their data assets.

Many organisations will have their IT equipment and maybe even their desks and chairs asset tagged. They know who is using them, where they are, and have some kind of plan to make sure that they only invest in maintaining the assets they really need. But few will be treating data in the same way.

That’s a change that is only just beginning. Part of the shift is in understanding how those assets can be used to solve problems. Or help them, their partners and customers to make more informed decisions.

Often that means sharing or opening that data so that others can use it. Making sure that data is at the right point of the data spectrum helps to unlock its value.

A sticking point for many organisations is that they begin to question why they should share or open those data assets, and whether others should contribute to their maintenance. There are many commons questions around the value of sharing, respecting privacy, logistics, etc.

I think a useful framing for this type of discussion might be to distinguish between data assets and data products.

A data asset is what an organisation is managing internally. It may be shared with a limited audience.

A data product is what you share with or open to a wider audience. Its created from one or more data assets. A data product may not contain all of the same data as the data assets it’s based on. Personal data might need to be removed or anonymised for example. This means a data product might sit at a different point in the data spectrum. It can be more open. I’m using data product here to refer to specific types of datasets, not “applications that have been made using data”

An asset is something you manage and invest in. A product is intended to address some specific needs. It may need some support or documentation to make sure it’s useful. It may also need to evolve based on changing needs.

In some cases a data asset could also be a data product. The complete dataset might be published in its entirety. In my experience this is often rarely the case though. There’s usually additional information, e.g governance and version history, that might not be useful to reusers.

In others cases data assets are collaboratively maintained, often in the open. Wikidata and OpenStreetMap are global data assets that are maintained in this way. There are many organisations that are using those assets to create more tailored data products that help to meet specific needs. Over time I expect more data assets will be managed in collaborative ways.

Obviously not every open data release needs to be a fully supported “product”. To meet transparency goals we often just need to get data published as soon as possible, with a minimum of friction for both publishers and users.

But when we are using data as tool to create other types of impact, more work is sometimes needed. There are often a number of social, legal and technical issues to consider in making data accessible in a sustainable way.

By injecting some product thinking into how we share and open data it might be helpful in addressing the types of problems that can contribute to data releases not having the desired impact: Why are we opening this data? Who will use it? How can we help them be more effective? Does releasing the data provide ways in which the data asset might be more collaboratively maintained?

When governments are publishing data that should be part of a national data infrastructure, more value will be unlocked if more of the underlying data assets are available for anyone to access, use and share. Releasing a “data product” that is too closely targeted might limit its utility.  So I also think this “data asset” vs “data product” distinction can help us to challenge the types data that are being released. Are we getting access to the most valuable data assets or useful subsets of them. Or are we just being given a data product that has much more limited applications, regardless of how well it is being published?

We CAN get there from here

On Wednesday, as part of the Autumn Budget, the Chancellor announced that the government will be creating a Geospatial Commission “to establish how to open up freely the OS MasterMap data to UK-based small businesses”. It will be supported by new funding of £80 million over two years. The Commission will be looking at a range of things including:

  • improving the access to, links between, and quality of their data
  • looking at making more geospatial data available for free and without restriction
  • setting regulation and policy in relation to geospatial data created by the public sector
  • holding individual bodies to account for delivery against the geospatial strategy
  • providing strategic oversight and direction across Whitehall and public bodies who operate in this area

That’s a big pot of money to get something done and a remit that ticks all of the right boxes. As the ODI blog post notes, it creates “the opportunity for national mapping agencies to adapt to a future where they become stewards for national mapping data infrastructure, making sure that data is available to meet the needs of everyone in the country”.

So, I’m really surprised that the many of the reactions from the open data community have been fairly negative. I understand the concerns that the end result might not be a completely open Mastermap. There are many, many ways in which this could end up with little or no change to the status quo. That’s certainly true if we ignore the opportunity to embed some change.

From my perspective, this is the biggest step towards a more open future for UK geospatial data since the first OS Open Data release in 2010. (I remember excitedly hitting the publish button to make their first Linked Data release publicly accessible)

Anyone who has been involved with open data in the UK will have encountered the Ordnance Survey licensing issues that are massively inhibiting both the release and use of open data in the UK. It’s a frustration of mine that these issues aren’t manifest in the various open data indexes.

In my opinion, anything that moves us forward from the current licensing position is to be welcomed. Yes, we all want a completely open MasterMap. That’s our shared goal. But how do we get there?

We’ve just seen the government task and resource itself to do something that can help us achieve that goal. It’s taken concerted effort by a number of people to get to this point. We should be focusing on what we all can do, right now, to help this process stay on track. Dismissing it as an already failed attempt isn’t helpful.

I think there’s a great deal that the community could do to engage with and support this process.

Here’s a few ideas of things of ways that we could inject some useful thinking into the process:

  • Can we pull together examples of where existing licensing restrictions are causing friction for UK businesses? Those of who us have been involved with open data have internalised many of these issues already, but we need to make sure they’re clearly understood by a wider audience
  • Can we do the same for local government data and services? There are loads of these too. Particularly compelling examples will be those that highlight where more open licensing can help improve local service delivery
  • Where could greater clarity around existing licensing arrangements help UK businesses, public sector and civil society organisations achieve greater impact? It often seems like some projects and local areas are able to achieve releases where others can’t.
  • Even if all of MasterMap were open tomorrow, it might still be difficult to access. No-one likes the current shopping cart model for accessing OS open data. What services would we expect from the OS and others that would make this data useful? I suspect this would go beyond “let me download some shapefiles”. We built some of these ideas into the OS Linked Data site. It still baffles me that you can’t find much OS data on the OS website.
  • If all of MasterMap isn’t made open, then which elements of it would unlock the most value? Are there specific layers or data types that could reduce friction in important application areas?
  • Similarly, how could the existing OS open data be improved to make it more useful? Hint: currently all of the data is generalised and doesn’t have any stable identifiers at all.
  • What could the OS and others do to support the rest of us in annotating and improving their data assets? The OS switched off its TOID lookup service because no-one was using it. It wasn’t very good. So what would we expect that type of identifier service to do?
  • If there is more openly licensed data available, then how could it be usefully added to OpenStreetMap and used by the ecosystem of open geospatial tools that it is supporting?
  • We all want access to MasterMap because its a rich resource. What are the options available to ensure that the Ordnance Survey stays resourced to a level where we can retain it as a national asset? Are there reasonable compromises to be made between opening all the data and them offering some commercial services around it?
  • …etc, etc, etc.

Personally, I’m choosing to be optimistic. Let’s get to work to create the result we want to see.