OCDS – Notes on a standard

logo-open-contracting Today sees the launch of the first release of the Open Contracting Data Standard (OCDS). The standard, as I’ve written before, brings together concrete guidance on the kinds of documents and data that are needed for increased transparency in processes of public contracting, with a technical specification describing how to represent contract data and meta-data in common ways.

The video below provides a brief overview of how it works (or you can read the briefing note), and you can find full documentation at http://standard.open-contracting.org.

When I first jotted down a few notes on how to go forward from the rapid prototype I worked on with Sarah Bird in 2012, I didn’t realise we would actually end up with the opportunity to put some of those ideas into practice. However: we did – and so in this post I wanted to reflect on some aspects of the standard we’ve arrived at, some of the learning from the process, and a few of the ideas that have guided at least my inputs into the development process.

As, hopefully, others pick up and draw upon the initial work we’ve done (in addition to the great inputs we’ve had already), I’m certain there will be much more learning to capture.

(1) Foundations for ‘open by default’

Early open data advocacy called for ‘raw data now‘, asking for governments to essentially export and dump online existing datasets, with issues of structure and regular publishing processes to be sorted out later. Yet, as open data matures, the discussion is shifting to the idea of ‘open by default’, and taken seriously this means more than just data dumps that are created being openly licensed as the default position, but should mean that data is released from government systems as a matter of course in part of their day-to-day operation.

green_compilation.svgThe full OCDS model is designed to support this kind of ‘open by default’, allowing publishers to provide small releases of data every time some event occurs in the lifetime of a contracting process. A new tender is a release. An amendment to that tender is a release. The contract being awarded, or then signed, are each releases. These data releases are tied together by a common identifier, and can be combined into a summary record, providing a snapshot view of the state of a contracting process, and a history of how it has developed over time.

This releases and records model seeks to combine together different user needs: from the firm seeking information about tender opportunities, to the civil society organisation wishing to analyse across a wide range of contracting processes. And by allowing core stages in the business process of contracting to be published as they happen, and then joined up later, it is oriented towards the development of contracting systems that default to timely openness.

As I’ll be exploring in my talk at the Berkman Centre next week, the challenge ahead for open data is not just to find standards to make existing datasets line-up when they get dumped online, but is to envisage and co-design new infrastructures for everyday transparent, effective and accountable processes of government and governance.

(2) Not your minimum viable product

Different models of standard

Many open data standard projects adopt either a ‘Minimum Viable Product‘ approach, looking to capture only the few most common fields between publishers, or are developed through focussing on the concerns of a single publisher or users. Whilst MVP models may make sense for small building blocks designed to fit into other standardisation efforts, when it came to OCDS there was a clear user demand to link up data along the contracting process, and this required an overarching framework from into which simple component could be placed, or from which they could be extracted, rather than the creation of ad-hoc components, with the attempt to join them up made later on.

Whilst we didn’t quite achieve the full abstract model + idiomatic serialisations proposed in the initial technical architecture sketch, we have ended up with a core schema, and then suggested ways to represent this data in both structured and flat formats. This is already proving useful for example in exploring how data published as part of the UK Local Government Transparency Code might be mapped to OCDS from existing CSV schemas.

(3) The interop balancing act & keeping flex in the framework

OCDS is, ultimately, not a small standard. It seeks to describe the whole of a contracting process, from planning, through tender, to contract award, signed contract, and project implementation. And at each stage it provides space for capturing detailed information, linking to documents, tracking milestones and tracking values and line-items.

This shape of the specification is a direct consequence of the method adopted to develop it: looking at a diverse set of existing data, and spending time exploring the data that different users wanted, as well as looking at other existing standards and data specifications.

However, OCDS by not means covers all the things that publishers might want to state about contracting, nor all the things users may want to know. Instead, it focusses on achieving interoperability of data in a number of key areas, and then providing a framework into which extensions can be linked as the needs of different sub-communities of open data users arise.

We’re only in the early stages of thinking about how extensions to the standard will work, but I suspect they will turn out to be an important aspect: allowing different groups to come together to agree (or contest) the extra elements that are important to share in a particular country, sector or context. Over time, some may move into the core of the standard, and potentially elements that appear core right now might move into the realm of extensions, each able to have their own governance processes if appropriate.

As Urs Gasser and John Palfrey note in their work on Interop, the key in building towards interoperability is not to make everything standardised and interoperable, but is to work out the ways in which things should be made compatible, and the ways in which they should not. Forcing everything into a common mould removes the diversity of the real world, yet leaving everything underspecified means no possibility to connect data up. This is both a question of the standards, and the pressures that shape how they are adopted.

(4) Avoiding identity crisis

green_organisation.svgData describes things. To be described, those things need to be identified. When describing data on the web, it helps if those things can be unambiguously identified and distinguished from other things which might have the same names or identification numbers. This generally requires the use of globally unique identifiers (guid): some value which, in a universe of all available contracting data, for example, picks out a unique contracting process; or, in the universe of all organizations, uniquely identifies a specific organization. However, providing these identifiers can turn out to be both a politically and technically challenging process.

The Open Data Institute have recently published a report on the importance of identifiers that underlines how important identifiers are to processes of opening data. Yet, consistent identifiers often have key properties of public goods: everyone benefits from having them, but providing and maintaining them has some costs attached, which no individual identifier user has an incentive to cover. In some cases, such as goods and service identifiers, projects have emerged which take a proprietary approach to fund the maintenance of those identifiers, selling access to the lookup lists which match the codes for describing goods and services to their descriptions. This clearly raises challenges for an open standard, as when proprietary identifiers are incorporated into data, then users may face extra costs to interpret and make sense of data.

In OCDS we’ve sought to take as distributed an approach to identifiers as possible, only requiring globally unique identifiers where absolutely necessary (identifying contracts, organizations and goods and services), and deferring to existing registration agencies and identity providers, with OCDS maintaining, at most, code lists for referring to each identity ‘scheme’.

In some cases, we’ve split the ‘scheme’ out into a separate field: for example, an organization identifier consists of a scheme field with a value like ‘GB-COH’ to stand for UK Companies House, and then the identifier given in that scheme, like ‘5381958’. This approach allows people to store those identifiers in their existing systems without change (existing databases might hold national company numbers, with the field assumed to come from a particular register), whilst making explicit the scheme they come from in the OCDS. In other cases, however, we look to create new composite string identifiers, combining a prefix, and some identifier drawn from an organizations internal system. This is particularly the case for the Open Contracting ID (ocid). By doing this, the identifier can travel between systems more easily as a guid – and could even be incorporated in unstructured data as a key for locating documents and resources related to a given contracting process.

However, recent learning from the project is showing that many organisations are hesistant about the introduction of new IDs, and that adoption of an identifier schema may require as much advocacy as adoption of a standard. At a policy level, bringing some external convention for identifying things into a dataset appears to be seen as affecting the, for want of a better word, sovereignty of a specific dataset: even if in practice the prefix approach of the ocid means it only need to be hard coded in the systems that expose data to the world, not necessarily stored inside organizations databases. However, this is an area I suspect we will need to explore more, and keep tracking, as OCDS adoption moves forward.

(5) Bridging communities of practice

If you look closely you might in fact notice that the specification just launched in Costa Rica is actually labelled as a ‘release candidate‘. This points to another key element of learning in the project, concerning the different processes and timelines of policy and technical standardisation. In the world of funded projects and policy processes, deadlines are often fixed, and the project plan has to work backwards from there. In a technical standardisation process, there is no ‘standard’ until a specification is in use: and has been robustly tested. The processes for adopting a policy standard, and setting a technical one, differ – and whilst perhaps we should have spoken from the start of the project of an overall standard, embedding within it a technical specification, we were too far down the path towards the policy launch before this point. As a result, the Release Candidate designation is intended to suggest the specification is ready to draw upon, but that there is still a process to go (and future governance arrangements to be defined) before it can be adopted as a standard per-se.

(6) The schema is just the start of it

This leads to the most important point: that launching the schemas and specification is just one part of delivering the standard.

In a recent e-mail conversation with Greg Bloom about elements of standardisation, linked to the development of the Open Referral standard, Greg put forward a list of components that may be involved in delivering a sustainable standards project, including:

  • The specification – with its various components and subcomponents);
  • Tools that assesses compliance according to the spec (e.g. validation tools, and more advanced assessment tools);
  • Some means of visualizing a given set of data’s level of compliance;
  • Incentives of some kind (whether positive or negative) for attaining various levels of compliance;
  • Processes for governing all of the above;
  • and of course the community through which all of this emerges and sustains;

To this we might also add elements like documentation and tutorials, support for publishers, catalysing work with tool builders, guidance for users, and so-on.

Open government standards are not something to be published once, and then left, but require labour to develop and sustain, and involve many social processes as much as technical ones.

In many ways, although we’ve spent a year of small development iterations working towards this OCDS release, the work now is only just getting started, and there are many technical, community and capacity-building challenges ahead for the Open Contracting Partnership and others in the open contracting movement.

Upcoming talks: October/November 2014

[Summary: quick links to upcoming talks]

The next month is shaping up to be a busy one with project deadlines, and lots of interesting opportunities to share reflections on research projects from the last year. Below are details of a few talks and activities I’m involved in over the coming weeks:

29th October 2014: ICT for Transparency, Accountability and Anti-Corruption: Incentives and Key Features for Implementation (Webinar)

Tomorrow (29th October) at 2pm BST (10am EST) I’ll be sharing an outline of the paper I wrote with Silvana Fumega that was published earlier this year, questioning how the motivations of government in adopting open government ICTs may affect the way those ICTs are implemented and the effects they can have, as well as looking at the different factors that shape adoption and implemention of these technologies. The session will also include Savita Bailur, sharing brand new research into the mySociety Alavateli platform for FOI requests, and it’s use around the world.

The session will consist of short presentations, followed by an opportunity for discussion.

Registration to take part is open here.

25th November 2014: Unpacking open data: power, politics and the influence of infrastructures

I’ll be back at the Berkman Center to talk about some of my research from the last year, and to explore some of the new directions my work on open data is taking. Here’s the blurb for the talk:

“Countries, states & cities across the globe are embracing the idea of ‘open data’: establishing platforms, portals and projects to share government managed data online for re-use. Yet, right now, the anticipated civic impacts of open data rarely materialise, and the gap between the promise and the reality of open data remains wide. This talk, drawing on a series of empirical studies of open data around the world, will question the ways in which changing regimes around data can reconfigure power and politics, and will explore the limits of current practice. It will consider opportunities to re-imagine the open data project, not merely as one of placing datasets online, but as one that can positively reshape the knowledge infrastructures of civic life.”

The talk will be webcast, but if you happen to be in Cambridge, MA, you can also join in person at the Berkman Center over lunch. More details and in-person sign-up is here.

November 4th 2014: Sheffield iSchool Seminar

I’ll be joining Jo Bates and Danny Antrobus at the Sheffield iSchool for a seminar on open data theory of practice. Taking place at 1pm. More info should be up soon on the iSchool blog, and the blurb of what I’ll be talking on is below:

“Open data had rapidly become a global phenomena, driven both both top-down policy transfer, and bottom-up demands for greater access to vital information. Drawing on research from the Open Data in Developing Countries (ODDC) project, which has supported case-study research into open data use and impacts in 12 countries across the global South, this presentation will explore how far the models for open government data that are promoted through global institutions are aligned with the needs and realities of different communities around the world. By moving beyond a ‘narrow model’ of open data, focused on datasets, portals and apps, a richer picture of both the potential and the pitfalls of particular approaches to opening up data can be uncovered. “

November 18th 2014: Launch of the Open Contracting Data Standard

At the Open Government Partnership regional meeting in Costa Rica, I’ll be joining with the team who have been working on prototyping a data standard for public contracting to see the public release of the standard launched, and I hope to engage in conversation about how to keep developing it further in open and collaborative ways.

Creating the capacity building game…

Open Development Camp Logo[Summary: crowdsourcing contributions to a workshop at Open Development Camp]

There is a lot of talk of ‘capacity building’ in the open data world. As the first phase of the ODDC project found, there are many gaps between the potential of open data and it’s realisation: and many of these gaps can be described as capacity gaps – whether on the side of data suppliers, or potential data users.

But how does sustainable capacity for working with open data develop? At the Open Development Camp in a few weeks time I’ll be facilitating a workshop to explore this question, and to support participants to share learning about how different capacity building approaches fit in different settings.

The basic idea is that we’ll use a simple ‘cards and scenarios’ game (modelled, as ever, on the Social Media Game), where we identify a set of scenarios with capacity building needs, and then work in teams to design responses, based on combining a selection of different approaches, each of which will be listed one of the game cards.

But, rather than just work from the cards, I’m hoping that for many of these approaches there will be ‘champions’ on hand, able to make the case for that particular approach, and to provide expert insights to the team. So:

  • (1) I’ve put together a list of 24+ different capacity building approaches I’ve seen in the open data world – but I need your help to fill in the details of their strengths, weaknesses and examples of them in action.
  • (2) I’m looking for ‘champions’ for these approaches, either who will be at the Open Development Camp, or who could prepare a short video input in advance to make the case for their preferred capacity building approach;

If you could help with either, get in touch, or dive in direct on this Google Doc.

If all goes well, I’ll prepare a toolkit after the Open Development Camp for anyone to run their own version of the Capacity Building Game.

The list so far

Click each one to jump direct to the draft document

Two senses of standard

[Summary: technical standards play a role in both interoperability, and in target-setting for policy.]

I’ve been doing lots of thinking about standardisation recently, particularly as part of work on the Open Contracting Data Standard (feedback invited on the latest draft release…), and thanks to the opportunity to work with Samuel Goëta on a paper around data standards (hopefully out some time next year).

One of the themes I’ve been seeking to explore is how standards play both a technical and a political role, and how standards processes (at least at the level of content standards) can sensitively engage with this. Below is a repost of my earlier contribution to a GitHub thread discussing some of this in the context of Open Contracting.

Two senses of standard

In Open Contracting I believe we’re dealing with two different senses of ‘standard’, and two purposes which we need to keep in balance. Namely:

  • Standards as a basis for interoperability – as in *”their data complies with the standard, and can be used by standards-compliant tools.”
  • Standards as targets – as in, “they have achieved a high standard of disclosure”.

To unpack these a bit:

(Note: the arguments below are predominantly theoretical, and so some of the edge cases considered may not come up at all in practice in the Open Contracting Data Standard, but considering them is a useful exercise to test the intuitions and principles directing our action.)

Standards as interoperability

We’re interested in interoperability in two directions: vertical (can a single dataset be used by other actors and tools in a value-chain of re-use), and horizontal (can two datasets from different publishers be easily analysed alongside one another).

Where data is already published, then the goal should be to achieve the largest possible set of data publishers who can richly represent their data in the standard, and of data users who can draw on data in the standard to meet their needs. This supports the idea that for any element in the standard where (a) data already exists; and (b) use cases already exist; we should be looking for reference implementations to test that data can be rendered in the standard, and that users (or tools they create) can read, analyse and use that data effectively.

However, it is important that in this we look at both both horizontal and vertical interoperability in making this judgement. E.g. there could be a country as the sole publisher of a field that is used by 5 different users in their country. This should clearly not be a required field in a standard, but articulating how it is standardised is useful to this community of users (one way to accommodate such cases may be in extensions, although the judgement on whether or not to move something to an extension might come down to whether it is likely that other publishers could be providing this data in future).

In many cases, underlying data from different sources is not perfectly interoperable, or there is a mismatch between the requirements of users, and the requirements of data holders. In these cases, the way a standard is designed affects the distribution of labour between publishers and users with respect to rendering data interoperable. For example, a use case might involve ‘Identifying which different government agencies, each publishing data independently, have contracts with a particular firm’. In this case, a standard could require all publishers, who may store different identifiers in their systems, to map these to a common identifier, or a standard could allow publishers to use whatever identifier they hold, leaving the costs of reconciling these on the user. Making things interoperable then involves can involve then a process of negotiation, and this process may play out differently in different places at different times, leaving certain elements of a standard less stable than others. The concept of ‘designing for the tussle’ (PDF) may be relevant here, thinking about how we can modularise stable (or ‘neutral’) and unstable elements of a standard (this is what the proposed Organisation ID standard does, but having a common way to represent identifiers, but separating this off from the choice of identifier itself, and then allowing for the emergence of a set of third-party tools and validation routines to help manage the tussle).

In seeking to maximise the set of publishers and users interoperable through the standard we need to be critically aware of both short-term and long-term interoperability, as organisations modify their practices in order to be able to publish to, or draw upon, a common standard. We need to balance out a ‘Lowest Common Denominator’ (LCD) of ‘Minimum Viable Product’ (MVP) approach that means that the majority of publishers can achieve substantial coverage of the standard, with a richer standard that supports the greatest chance of different producer and consumer groups being able to exchange data through the standard.

initial-sketch-thinking-about-standards

(Initial attempt to sketch distinction between maximising set of common fields across publisher and users, and maximising set of publishers and users)

Standards as targets

Open Contracting is a political process. The Open Contracting Partnership have articulated a set of Global Principles which set out the sorts of information about contracting that governments and other parties should disclose, and they are working to secure government sign-up to these principles. In policy circles, a standard is often seen as a form of measure, qualitative or quantitative, against which process towards some policy goal is measured. Some targets might be based on ‘best practice’, others are based on ‘stretch goals’: things which perhaps no-one is yet doing particularly well, but which a community of actors agree are worth aiming for. A standard, whether specified in terms of indicators and measures, or in terms of fields and formats, provides a means of agreeing what meeting the target will look like.

The Open Contracting Principles call for a lot of things which no governments appear to yet be publishing in machine-readable forms. In many cases we’ve not touched the standardisation of these right now (e.g. “Risk assessments, including environmental and social impact assessments”) recognising that standards for these will either exist in different domains that can be linked or embedded into our standard, or, recognising that interoperability of such information is hard to achieve and ultimately what is needed for most use cases may be legal text or plain language documents, rather than structured data. However, there may be cases where something is a strong candidate for standardisation, having both the potential to be published (i.e. this is something which evidence suggests governments either do, or could, capture in their existing information systems), and for which clearly articulated use cases exist. In these cases a proposed field-level standard can act as an important target for those seeking to provide this data to move towards. It also acts to challenge unwarranted ‘first mover advantage’ where the first person to publish, even if publishing less than an idea target would require, gets to set the standard, and instead makes the ‘target’ subject to community discussion.

Clearly any ‘aspirational’ elements of a standard should not predominate or make up the majority of a standard if it seeks to effectively support interoperability, but in standards that play a part in policy and political processes (as, in practice, all standards do to some extent (c.f. Lessig).

Implications for Open Contracting Data Standard

There are a number of ways we might respond to a recognition of the dual role that standardisation plays in Open Contracting.

Purposes and validation sets

One approach, suggested in the early technical scoping is to identify different sets of users, or ‘purposes’ for the standard, and for each of these to identify the kinds of fields (subset of the data) these purposes require. As Jeni Tennison’s work on the scoping describes “…each purpose can have a status (eg proposed vs implemented) and … purposes are only marked as implemented when there are implementations that use the given subset of data for the specified purpose”.

If their are neither purposes requiring a field, nor datasets providing a field, then it would not be suitable for inclusion in a standard. And if a purpose either went unimplemented for a long period, or required a field that no supplier could publish, then careful evaluation would be needed of whether to remove that purpose (or remove that field from the purpose) against which elements of the standard could be evaluated for relevance to remain in the model.

Purposes could also be used to validate datasets, identifying how many datasets are fit for which purpose.

Stable, ordinary and target elements

We could maintain a distinction in how the standard is described between fields and elements which are ‘stable’ (and thus very unlikely to change), ‘ordinary’ elements (which may have reference implementations, but could change if there was some majority interest amongst those governing a standard in seeing changes), and ‘target’ elements, which may lack any reference implementations, but which are considered useful to help publishers moving towards implementing a political commitment to publish.

Q: Could we build this information into the schema meta-data somehow?

We might need to have quite a long time horizon for keeping target elements provisionally in the standard, and to only remove them if there is agreement that no-one is likely to publish to them. However, being able to represent them visually as distinct in the schema, and clearly documenting the distinction may be valuable.

Extensions

Some ‘target’ elements may best belong in extensions, with some process for merging extensions into the core standard if they are widely enough adopted.

Regular implementation monitoring

The IATI Team run a dashboard which tracks use of particular fields in the data. Doing similar for Open Contracting would be valuable, and it may even be useful to feed such information into the display of the schema or documentation (or at least to make it easy for publishers and users to look up who is implementing a given property)

Implementation schedules

Another approach IATI uses for ‘target elements’ is to ask publishers to prepare ‘Implementation Schedules‘ which outline which fields they expect to be able to publish by when. This allows an indication of whether there is political will to reach some of the ‘stretch targets’ that might be involved in a standard, and holds out the potential to convene together to define and refine target standardisations those who are most likely to publish that data in the near to medium term.

Discussion

What theoretical writing on standardisation could I be drawing on here?

What experience from other standards could we be drawing upon in Open Contracting and in other standard processes?

Exploring Wikidata

WikiData[Summary: thinking aloud – brief notes on learning about the wikidata project, and how it might help addressing the organisational identifiers problem]

I’ve spent a fascinating day today at the Wikimania Conference at the Barbican in London, mostly following the programmes ‘data’ track in order to understand in more depth the Wikidata project. This post shares some thinking aloud to capture some learning, reflections and exploration from the day.

As the Wikidata project manager, Lydia Pintscher, framed it, right now access to knowledge on wikipedia is highly skewed by language. The topics of articles you have access to, the depth of meta-data about them (such as the locations they describe), and the detail of those articles, and their liklihood of being up to date, is greatly affected by the language you speak. Italian or Greek wikipedia may have great coverage of places in Italy or Greece, but go wider and their coverage drops off. In terms of seeking more equal access to knowledge, this is a problem. However, whilst the encyclopedic narrative of a French, Spanish of Catalan page about the Barbican Center in London will need to be written by someone in command of that language, many of the basic facts that go into an article are language-neutral, or translatable as small units of content, rather than sentences and paragraphs. The date the building was built, the name of the architect, the current capacity of the building – all the kinds of things which might appear in infoboxes – are all things that could be made available to bootstrap new articles, or that, when changed, could have their changes cascaded across all the different language pages that draw upon them.

That is one of the motivating cases for Wikidata: separating out ‘items’ and their ‘properties’ that might belong in Wikipedia from the pages, making this data re-usable, and using it to build a better encyclopedia.

However, wikidata is also generating much wider interest – not least because it is taking on a number of problems that many people want to see addressed. These include:

  • Somewhere ‘institutional’ and well governed on the web to put data – and where each data item also gains the advantage of a discussion page.
  • The long-term preservation, and versioning, of data;
  • Providing common identifiers on the web for arbitrary things – and providing URIs for these things that can be looked up (building on the idea of DBPedia as a crystalisation point for the web of linked data);
  • Providing a data model that can cope with change over time, and with data from heterogenous sources – all of the properties in wikidata can have qualifiers, such as when the statement is true from, or until, source information, and other provenance data.

Wikidata could help address these issues on two levels:

  • By allowing anyone to add items and properties to the central wikidata instance, and making these available for re-use;
  • By providing an open source software platform for anyone to use in managing their own corpus of wikified, versioned data*;

A particular use case I’m interested in is whether it might help in addressing the perenial Organisational Identifiers problem faced by data standards such as IATI and Open Contracting, where it turns out that having shared identifiers for government agencies, and lots of existing, but non-registered, entities like charities and associations that give and recieve funds, is really difficult. Others at Wikimania spoke of potential use cases around maintaining national statistics, and archiving the datasets underlying scientific publications.

However, in thinking about the use cases wikidata might have, its important to keep in mind it’s current scope:

  • It is a store of ‘items’ and then ‘statements’ about them (essentially a graph store). This is different from being a place to store datasets (as you might want to do with the archival of the dataset used in a scientific paper), and it means that, once created, items are the first class entities of wikidata, able to exist in multiple collection.
  • It currently inherits Wikipedia’s notability criteria for items. That is, the basic building blocks of wikidata – the items that can be identified and described, such as the Barbican, Cheese or Government of Grenada – can only be included in the main wikidata instance if they have a corresponding wikipedia page in some language wikipedia (or similar: this requirement is a little more complex).
  • It can be edited by anyone, at any time. That is, systems that rely on the data need to consider what levels of consistence they need. Of course, as wikipedia has shown, editability is often a great strength – and as Rufus Pollock noted in the ‘data roundtable’ session, updating and versioning of open data are currently big missing parts of our data infrastructures.

Unlike the entirely distributed open world assumption on the web of data, where the AAA assumption holds (Anyone can say Anything about Anything), wikidata brings both a layer of regulation to the statements that can be made, and the potential of community driven editorial control. It sits somewhere between the controlled description sets of Schema.org, and an entirely open proliferation of items and ontologies to describe them.

Can it help the organisational identifiers problem?

I’ve started to carry out some quick tests to see how far wikidata might be a resource to help with the aforementioned organisational identifiers problem.

Using Kasper Brandt‘s fantastically useful linked data rendering of IATI, I queried for the names of a selection of government and non-government organisations occurring in the International Aid Transparency Initiative data. I then used Open Refine to look up a selection of these on the DBPedia endpoint (which it seems now incorporates wikidata info as well). This was very rough-and-ready (just searching for full name matches), but by cross-checking negative results (where there were no matches) by searching wikipedia manually, it’s possible to get a sense of how many organisations might be identifiable within Wikipedia.

So far I’ve only tested the method, and haven’t run a large scale test – but I found around 1/2 the organisations I checked had a Wikipedia entry of some form, and thus would currently be eligible to be Wikidata items right away. For others, Wikipedia pages would need to be created, and whether or not all the small voluntary organisations that might occur in an IATI or Open Contracting dataset would be notable for inclusion is something that would need to be explored more.

Exploring the Wikidata pages for some of the organisations I did find threw up some interesting additional possibilities to help with organisation identifiers. A number of pages were linked to identifiers from Library Authority Files, including VIAF identifiers such as this set of examples returned for a search on Malawi Ministry of Finance. Library Authority Files would tend to only include entries when a government agency has a publication of some form in that library, but at a quick glance coverage seems pretty good.

Now, as Chris Taggart would be quick to point out, neither wikipedia pages, nor library authority file identifiers, act as a registry of legal entities. They pick out everyday concepts of an organisation, rather than the legally accountably body which enters into contracts. Yet, as they become increasingly backed by data, these identifiers do provide access to look up lots of contextual information that might help in understanding issues like organisational change over time. For example, the Wikipedia page for the UK’s Department for Education includes details on the departments that preceeded it. In wikidata form, a statement like this could even be qualified to say if that relationship of being a preceeding department is one that passes legal obligations from one to the other.

I’ve still got to think about this a lot more, but it seems that:

  • There are many things it might be useful to know about organisations, but which are not going to be captured in official registries anytime soon. Some of these things will need to be subject of discussion, and open to agreement through dialogue. Wikidata, as a trusted shared space with good community governance practices might be a good place to keep these things, albeit recognising that in its current phase it has no goal of being a comprehensive repository of records about all organisations in the world (and other spaces such as Open Corporates are already solving the comprehensive coverage problem for particular classes of organiastion).

  • There are some organisations for which, in many countries, no official registry exists (particularly Government Departments and Agencies). Many of these things are notable (Government Departments for example), and so even if no Wikipedia entry yet exists, one could and should. A project to manage and maintain government agency records and identifiers in Wikidata may be worth exploring.

Whether a shift from seeking to solve some aspects of the organisational identifiers problem through finding some authority to provide master lists, to developing a distributed best-efforts community approach is one that would make sense to the open government community is something yet to be explored.

Notes

*I here acknowledge SJ Klein‘s counsel was that this (encouraging multiple domain specific instances of a wikidata platform) is potentially a very bad idea, as the ‘forking’ of wiki-projects has rarely been a successful journey: particularly with respect to the sustainability of forked content. As SJ outlined, even though there may be technical and social challenges to a mega graph store, these could be compared to the apparant challenges of making the first encyclopedias (the idea of 50,000 page book must have seemed crazy at first), or the social challenges envisioned to Wikipedia at its genesis (‘how could non-experts possible edit an enecylopedia?’). On this view, it is only by setting the ambition of a comprehensive shared store of the worlds propositional data (with the qualifiers that Wikidata supports to make this possible without a closed world assumption) that such limits might be overcome. Perhaps with data there is a greater possibility to support forking, and remerging, of wikidata instances, permitting short-term pragmatic creation of datasets outside the core wikidata project, which can later be brought back in if they are considered, as a set, notable (although this still carries risks that forked projects diverge in their values, governance and structure so far that re-connecting later is made prohibitively difficult).

A Data Sharing Disclosure Standard?

DataSharing[Summary: Iterations on a proposal for a public register of government data sharing arrangements, setting out options for a Data Sharing Disclosure Standard to be used whenever government shares personal data. Draft for interactive comments here (and PDF for those in govt without access to Google Docs )] 

At the instigation of the UK Cabinet Office, an open policy making process is currently underway to propose new arrangements for data sharing in government. Data sharing arrangements are distinct from open data, as they may involve the limited exchange of personal and private data between government departments, or outside of government, with specific purpose of data use in mind.

The idea that new measures are needed is based on a perception that many opportunities to make better use of data for research, addressing debt and fraud, or tailoring the design of public services, are missed because either because of legal or practical barriers to data moving being exchanged or joined up between government departments. Some departments in particular, such as HMRC, require explicit legal permissions to share data, where in other department and public bodies, a range of existing ‘legal gateways’ and powers support exchange of data.

I’ve been following the process from afar, but on Monday last week I had the chance to attend one of the open full-day workshops that Involve are facilitating as part of the open policy making process. This brought together representatives of a range of public bodies, including central government departments and local authorities, with members of the Cabinet Office team leading on data sharing reforms, and a small number of civil society organisations and individuals. Monday’s discussion were centered on the introduction of new ‘permissive powers’ for data sharing to support tailored public services. For example, powers that would make it easier for local government to request and obtain HMRC data on 16 – 19 year olds in order to identify which young people in their area were already in employment or training, and so to target their resources on contacting those young people outside employment or training who they have a statutory obligation to support.

The exact wording of such a power, and the safeguards that need to be in place to ensure it is neither too broad, nor open to abuse, are being developed through the open policy making process. One safeguard I believe is important comes from introducing greater transparency into government data sharing arrangements.

A few months back, working with Reuben Binns, I put together a short note on a possible model for an ‘Open Register of Data Sharing‘. In Monday’s open policy making meeting, the topic of transparency as an important aspect of tailored public service data sharing came up, and provided an opportunity to discuss many of the ideas that the draft proposal had contained. Through the discussions, however, it became clear that there were a number of extra considerations needed to develop the proposal further, in particular:

  • Noting that public disclosure of planned data sharing was not only beneficial for transparency and scrutiny, but also for efficiency, coordination and consistency of data sharing: by allowing public bodies to pool data sharing arrangements, and to easily replicate approved shares, rather than starting from scratch with every plan and business case.
  • Recognising the concerns of local authorities and other public bodies about a centralised register, and the need to accommodate shares that might take place between public bodies at a local level only, without involvement of central government.
  • Recognising the need for both human and machine-readable information on data sharing arrangements, so that groups with a specific interest in particular data (e.g. associations looking out for the rights of homeless people) could track proposed or enacted arrangements without needing substantial technical know-how.
  • Recognising the importance of documents like Privacy Impact Assessments and Business Cases, but also noting that mandatory publication of these during their drafting could distort the drafting process (with the risk they become more PR documents making the case for a share, than genuine critical assessments), suggesting a mix of proactive and reactive transparency may be needed in practice.

As a result of the discussions with local authorities, government departments and others, I took away a number of ideas about how the proposal could be refined, and so this Friday, at the University of Southampton Web and Internet Science group annual gathering and weekend of projects (known locally as WAISFest) I worked in a stream on personal data, and spend a morning updating the proposals. The result is a reframed draft that, rather than focusing on the Register, focuses on a Data Sharing Disclosure Standard emphasising the key information that needs to be disclosed about each data share, and discussing when disclosure should take place, whilst leaving open a range of options for how this might be technically implemented.

You can find the updated document here, as a Google Doc open to comments. I would really welcome comments and suggestion for how this could be refined further over the coming weeks. If you do leave a comment and want to be credited / want to join in future discussion of this proposal, please also include your name / contact details.

The Gazette provides semantically enriched public notices: readable by humans and machines.
The Gazette provides semantically enriched public notices: readable by humans and machines.

A couple of things of particular note in the draft:

  • It is useful to identify (a) data controllers; (b) dataset; (c) legislation authorising data shares. Right now the Register of Data Controllers seems to provide a good resource for (a), and thanks to recent efforts at building out the digital information infrastructure of the UK, it turns out there are often good URLs that can be used as identifiers for datasets (data.gov.uk lists unpublished datasets from many central government departments) and legislation (through the data-all-the-way down approach of legislation.gov.uk).
  • It considers how the Gazette might be used as a publication route for Data Sharing Disclosures. The Gazette is an official paper of record, established since 1665 but recently re-envisioned with a semantic publishing platform. Using such a route to publish notices of data sharing has the advantage that it combines the long-term archival of information in a robust source, with making enriched openly licensed data available for re-use. This potentially offers a more robust route to disclosures, in which the data version is a progressive enhancement on top of an information disclosure.
  • Based on feedback from Javier Ruiz, it highlights the importance of flagging when shared data is going to be processed using algorithms that will determine individuals eligibility for services/trigger interventions affecting citizens, and raises of the question of whether the algorithms themselves should be disclosed as a mater of course.

I’ll be sharing a copy of the draft with the Data Sharing open policy process mailing list, and with the Cabinet Office team working on the data sharing brief. They are working to draft an updated paper on policy options by early September, with a view to a possible White Paper – so comments over the next few weeks are particularly valued.

Fifteen open data insights

ODDC Phase 1 Report - Cover[Summary: blogging the three-page version of Open Data in Developing Countries – Emerging Insights from Phase I paper, with some preamble]

I’m back living in Oxford after my almost-year in the USA at the Berkman Center. Before we returned, Rachel and I took a month to travel around the US – by Amtrak. The delightfully ponderous pace of US trains gave me plenty of time for reading, which was just as well, given June was the month when most of the partners in the Open Data in Developing Countries project I coordinate were producing their final reports. So, in-between time staring at the stunning scenery as we climbed through the Rockies, or watching amazing lightening storms from the viewing car, I was digging through in-depth reports into open data in the global south, and trying to pick out common themes and issues. A combination of post-it notes and scrivener index cards later, and finally back at my desk in Oxford, the result was a report, released alongside the ODDC Research Sharing Event in Berlin last week, that seeks to snapshot 15 insights or provocations for policy-makers and practitioners drawn out from the ODDC case study reports.

These are just the first stage of the synthesis work to be carried out in the ODDC project. In the network meeting also hosted in Berlin last week, we worked on mapping these and other findings from projects onto the original conceptual framework of the project, and looked at identifying further cross-cutting write-ups required. But, for now, below are the 15 points from the three-page briefing version, and you can find a full write-up of these points for download. You can also find reports from all the individual project partners, including a collection of quick-read research posters over on the Open Data Research Network website.

15 insights into open data supply, use and impacts

(1) There are many gaps to overcome before open data availability, can lead to widespread effective use and impact. Open data can lead to change through a ‘domino effect’, or by creating ripples of change that gradually spread out. However, often many of the key ‘domino pieces’ are missing, and local political contexts limit the reach of ripples. Poor data quality, low connectivity, scarce technical skills, weak legal frameworks and political barriers may all prevent open data triggering sustainable change. Attentiveness to all the components of open data impact is needed when designing interventions.

(2) There is a frequent mismatch between open data supply and demand in developing countries. Counting datasets is a poor way of assessing the quality of an open data initiative. The datasets published on portals are often the datasets that are easiest to publish, not the datasets most in demand. Politically sensitive datasets are particularly unlikely to be published without civil society pressure. Sometimes the gap is on the demand side – as potential open data users often do not articulate demands for key datasets.

(3) Open data initiatives can create new spaces for civil society to pursue government accountability and effectiveness. The conversation around transparency and accountability that ideas of open data can support is as important as the datasets in some developing countries.

(4) Working on open data projects can change how government creates, prepares and uses its own data. The motivations behind an open data initiative shape how government uses the data itself. Civil society and entrepreneurs interacting with government through open data projects can help shape government data practices. This makes it important to consider which intermediaries gain insider roles shaping data supply.

(5) Intermediaries are vital to both the supply and the use of open data. Not all data needed for governance in developing countries comes from government. Intermediaries can create data, articulate demands for data, and help translate open data visions from political leaders into effective implementations. Traditional local intermediaries are an important source of information, in particular because they are trusted parties.

(6) Digital divides create data divides in both the supply and use of data. In some developing countries key data is not digitised, or a lack of technical staff has left data management patchy and inconsistent. Where Internet access is scarce, few citizens can have direct access to data or services built with it. Full access is needed for full empowerment, but offline intermediaries, including journalists and community radio stations, also play a vital role in bridging the gaps between data and citizens.

(7) Where information is already available and used, the shift to open data involves data evolution rather than data revolution. Many NGOs and intermediaries already access the information which is now becoming available as data. Capacity building should start from existing information and data practices in organisations, and should look for the step-by-step gains to be made from a data-driven approach.

(8) Officials’ fears about the integrity of data are a barrier to more machine-readable data being made available. The publication of data as PDF or in scanned copies is often down to a misunderstanding of how open data works. Only copies can be changed, and originals can be kept authoritative. Helping officials understand this may help increase the supply of data.

(9) Very few datasets are clearly openly licensed, and there is low understanding of what open licenses entail. There are mixed opinions on the importance of a focus on licensing in different contexts. Clear licenses are important to building a global commons of interoperable data, but may be less relevant to particular uses of data on the ground. In many countries wider conversation about licensing are yet to take place.

(10) Privacy issues are not on the radar of most developing country open data projects, although commercial confidentiality does arise as a reason preventing greater data transparency. Much state held data is collected either from citizens or from companies. Few countries in the ODDC study have weak or absent privacy laws and frameworks, yet participants in the studies raised few personal privacy considerations. By contrast, a lack of clarity, and officials’ concerns, about potential breaches of commercial confidentiality when sharing data gathered from firms was a barrier to opening data.

(11) There is more to open data than policies and portals. Whilst central open data portals act as a visible symbol of open data initiatives, a focus on portal building can distract attention from wider reforms. Open data elements can also be built on existing data sharing practices, and data made available through the locations where citizens, NGOs are businesses already go to access information.

(12) Open data advocacy should be aware of, and build upon, existing policy foundations in specific countries and sectors. Sectoral transparency policies for local government, budget and energy industry regulation, amongst others, could all have open data requirements and standards attached, drawing on existing mechanisms to secure sustainable supplies of relevant open data in developing countries. In addition, open data conversations could help make existing data collection and disclosure requirements fit better with the information and data demands of citizens.

(13) Open data is not just a central government issue: local government data, city data, and data from the judicial and legislative branches are all important. Many open data projects focus on the national level, and only on the executive branch. However, local government is closer to citizens, urban areas bring together many of the key ingredients for successful open data initiatives, and transparency in other branches of government is important to secure citizens democratic rights.

(14) Flexibility is needed in the application of definitions of open data to allow locally relevant and effective open data debates and advocacy to emerge. Open data is made up of various elements, including proactive publication, machine-readability and permissions to re-use. Countries at different stages of open data development may choose to focus on one or more of these, but recognising that adopting all elements at once could hinder progress. It is important to find ways to both define open data clearly, and to avoid a reductive debate that does not recognise progressive steps towards greater openness.

(15) There are many different models for an open data initiative: including top-down, bottom-up and sector-specific. Initiatives may also be state-led, civil society-led and entrepreneur-led in their goals and how they are implemented – with consequences for the resources and models required to make them sustainable. There is no one-size-fits-all approach to open data. More experimentation, evaluation and shared learning on the components, partners and processes for putting open data ideas into practice must be a priority for all who want to see a world where open-by-default data drives real social, political and economic change.

You can read more about each of these points in the full report.

New Paper – Mixed incentives: Adopting ICT innovations for transparency, accountability, and anti-corruption

7353-U4Issue-2014-03-04-WEB

[Summary: critical questions to ask when planning, funding or working on ICTs for transparency and accountability]

Last year I posted some drafts of a paper I’ve been writing with Silvana Fumega at the invitation of the U4 Anti-Corruption Center, looking at the incentives for, and dynamics of, adoption of ICTs as anti-corruption tools. Last week the final paper was published in the U4 Issue series, and you can find it for download here.

In the final iteration of the paper we have sought to capture the core of the analysis in the form of a series of critical questions that funders, planners and implementers of anti-corruption ICTs can ask. These are included in the executive summary below, and elaborated more in the full paper.

Adopting ICT innovations for transparency, accountability, and anti-corruption – Executive Summary

Initiatives facilitated by information and communication technology (ICT) are playing an increasingly central role in discourses of transparency, accountability, and anti-corruption. Both advocacy and funding are being mobilised to encourage governments to adopt new technologies aimed at combating corruption. Advocates and funders need to ask critical questions about how innovations from one setting might be transferred to another, assessing how ICTs affect the flow of information, how incentives for their adoption shape implementation, and how citizen engagement and the local context affect the potential impacts of their use.

ICTs can be applied to anti-corruption efforts in many different ways. These technologies change the flow of information between governments and citizens, as well as between different actors within governments and within civil society. E?government ICTs often seek to address corruption by automating processes and restricting discretion of officials. However, many contemporary uses of ICTs place more emphasis on the concept of transparency as a key mechanism to address corruption. Here, a distinction can be made between technologies that support “upward transparency,” where the state gains greater ability to observe and hear from its citizens, or higher-up actors in the state gain greater ability to observe their subordinates, and “downward transparency,” in which “the ‘ruled’ can observe the conduct, behaviour, and/or ‘results’ of their ‘rulers’” (Heald 2006). Streamlined systems that citizens can use to report issues to government fall into the former category, while transparency portals and open data portals are examples of the latter. Transparency alone can only be a starting point for addressing corruption, however: change requires individuals, groups, and institutions who can access and respond to the information.

In any particular application of technology with anti-corruption potential, it is important to ask:

  • What is the direction of the information flow: from whom and to whom?
  • Who controls the flow of information, and at what stages?
  • Who needs to act on the information in order to address corruption?

Different incentives can drive government adoption of ICTs. The current wave of interest in ICT for anti-corruption is relatively new, and limited evidence exists to quantify the benefits that particular technologies can bring in a given context. However, this is not limiting enthusiasm for the idea that governments, particularly developing country governments, can adopt new technologies as part of open government and anti-corruption efforts. Many technologies are “sold” on the basis of multiple promised benefits, and governments respond to a range of different incentives. For example, governments may use ICTs to:

  • Improve information flow and government efficiency, creating more responsive public institutions, supporting coordination.
  • Provide open access to data to enable innovation and economic growth, responding to claims about the economic value of open data and its role as a resource for private enterprise.
  • Address principal-agent problems, allowing progressive and reformist actors within the state to better manage and regulate other parts of the state by detecting and addressing corruption through upward and downward transparency.
  • Respond to international pressure, following the trends in global conversations and pressure from donors and businesses, as well as the availability of funding for pilots and projects.
  • Respond to bottom-up pressure, both from established civil society and from an emerging global network of technology-focussed civil society actors. Governments may do this either as genuine engagement or to “domesticate” what might otherwise be seen as disruptive innovations.

In supporting ICTs for anti-corruption, advocates and donors should consider several key questions related to incentives:

  • What are the stated motivations of government for engaging with this ICT?
  • What other incentives and motivations may be underlying interest in this ICT?
  • Which incentives are strongest? Are any of the incentives in conflict?
  • Which incentives are important to securing anti-corruption outcomes from this ICT?
  • Who may be motivated to oppose or inhibit the anti-corruption applications of this ICT?

The impact of ICTs for anti-corruption is shaped by citizen engagement in a local context. Whether aimed at upward or downward transparency, the successful anti-corruption application of an ICT relies upon citizen engagement. Many factors affect which citizens can engage through technology to share reports with government or act upon information provided by government. ICTs that worked in one context might not achieve the same results in a different setting (McGee and Gaventa 2010). The following questions draw attention to key aspects of context:

  • Who has access to the relevant technologies? What barriers of connectivity, literacy, language, or culture might prevent a certain part of the population from engaging with an ICT innovation?
  • What alternative channels (SMS, offline outreach) might be required to increase the reach of this innovation?
  • How will the initiative close the feedback loop? Will citizens see visible outcomes over the short or long term that build rather than undermine trust?
  • Who are the potential intermediary groups and centralised users for ICTs that provide upward or downward transparency? Are both technical and social intermediaries present? Are they able to work together?

Towards sustainable and effective anti-corruption use of ICTs. As Strand (2010) argues, “While ICT is not a magic bullet when it comes to ensuring greater transparency and less corruption . . . it has a significant role to play as a tool in a number of important areas.” Although taking advantage of the multiple potential benefits of open data, transparency portals, or digitised communication with government can make it easier to start a project, funders and advocates should consider the incentives for ICT adoption and their likely impact on how the technology will be applied in practice. Each of the questions above is important to understanding the role a particular technology might play and the factors that affect how it is implemented and utilised in a particular country.

 

You can read the full paper here.

Data, information, knowledge and power – exploring Open Knowledge’s new core purpose

[Summary: a contribution to debate about the development of open knowledge movements]

New 'Open Knowledge' data-earth logo.
New ‘Open Knowledge Foundation’ name and ‘data earth’ branding.

The Open Knowledge Foundation (re-named as as ‘Open Knowledge’) are soft-launching a new brand over the coming months.

Alongside the new logo, and details of how the new brand was developed, posted on the OK Wiki, appear a set of statements about the motivations, core purpose and tag-line of the organisation. In this post I want to offer an initial critical reading of this particular process and, more importantly, text.

Preliminary notes

Before going further, I want to offer a number of background points that frame the spirit in which the critique is offered.

  1. I have nothing but respect for the work of the leaders, staff team, volunteers and wider community of the Open Knowledge Foundation – and have been greatly inspired by the dedication I’ve seen to changing defaults and practices around how we handle data, information and knowledge. There are so many great projects, and so much political progress on openness, which OKFN as a whole can rightly take credit for.
  2. I recognise that there are massive challenges involved in founding, running and scaling up organisations. These challenges are magnified many times in community based and open organisations.
  3. Organisations with a commitment to openness, or democracy, whether the co-operative movement, open source communities like Mozilla, communities such as Creative Commons and indeed, the Open Knowledge Foundation – are generally held to much higher standards and face much more complex pressures from engaging their communities in what they do – than do closed and conventional organisations. And, as the other examples show, the path is not always an easy one. There are inevitably growing pains and challenges.
  4. It is generally better to raise concerns and critiques and talk about them, than leave things unsaid. A critique is about getting into the details. Details matter.
  5. See (1).

(Disclosure: I have previously worked as a voluntary coordinator for the open-development working group of OKF (with support from AidInfo), and have participated in many community activities. I have never carried out paid work for OKF, and have no current formal affiliation.)

The text

Here’s the three statements in the OK Branding notes that caught my attention and sparked some reflections:

About our brand and what motivates us:
A revolution in technology is happening and it’s changing everything we do. Never before has so much data been collected and analysed. Never before have so many people had the ability to freely, easily and quickly share information across the globe. Governments and corporations are using this data to create knowledge about our world, and make decisions about our future. But who should control this data and the ability to find insights and make decisions? The many, or the few? This is a choice that we get to make. The future is up for grabs. Do we want to live in a world where access to knowledge is “closed”, and the power and understanding it brings is controlled by the few? Or, do we choose a world where knowledge is “open” and we are all empowered to make informed choices about our future? We believe that knowledge should be open, and that everyone – from citizens to scientists, from enterprises to entrepreneurs, – should have access to the information they need to understand and shape the world around them.

Our core purpose:

  • A world where knowledge creates power for the many, not the few.
  • A world where data frees us – to make informed choices about how we live, what we buy and who gets our vote.
  • A world where information and insights are accessible – and apparent – to everyone.
  • This is the world we choose.

Our tagline:
See how data can change the world

The critique

My concerns are not about the new logo or name. I understand (all too well) the way that having ‘Foundation’ in a non-profits name can mean different things in different contexts (not least people expecting you to have an endowment and funds to distribute), and so the move to Open Knowledge as a name has a good rationale. Rather, I wanted to raise four concerns:

(1) Process and representativeness

Tag Cloud from Open Knowledge Foundation Survey. See http://blog.okfn.org/2014/02/12/who-are-you-community-survey-results-part-1/ for details.
Tag Cloud from Open Knowledge Foundation Survey. See blog post for details.

The message introducing the new brand to OKF-Discuss notes that “The network has been involved in the brand development process especially in the early stages as we explored what open knowledge meant to us all” referring primarily to the Community Survey run at the end of 2013 and written up here and here. However, the later parts of developing the brand appear to have been outsourced to a commercial brand consultancy consulting with a limited set of staff and stakeholders, and what is now presented appears to be being offered as given, rather than for consultation. The result has been a narrow focus on the ‘data’ aspects of OKF.

Looking back over the feedback from the 2013 survey, that data-centricity fails to represent the breadth of interests in the OKF community (particularly when looking beyond the quantitative survey questions which had an in-built bias towards data in the original survey design). Qualitative responses to the Survey talk of addressing specific global challenges, holding governments accountable, seeking diversity, and going beyond open data to develop broader critiques around intellectual property regimes. Yet none of this surfaces in the motivation statement, or visibly in the core purpose.

OKF has not yet grappled in full with idea of internal democracy and governance – yet as a network made up of many working groups, local chapters and more, for a ‘core purpose’ statement to emerge without wider consultation seem problematic. There is a big missed opportunity here for deeper discussion about ideas and ideals, and for the conceptualisation of a much richer vision of open knowledge. The result is, I think, a core purpose statement that fails to represent the diversity of the community OKF has been able to bring together, and that may threaten it’s ability to bring together those communities in shared space in future.

Process points aside however (see growing pains point above), there are three more substantive issues to be raised.

(2) Data and tech-centricity

A selection of OKF Working Groups

The Open Knowledge movement I’ve met at OKFestival and other events, and that is evident through the pages of the working groups is one committed to many forms of openness – education, hardware, sustainability, economics, political processes and development amongst others. It is a community that has been discussing diversity and building a global movement. Data may be an element of varying importance across the working groups and interest areas of OKF. And technology may be an enabler of action for each. But a lot are not fundamentally about data, or even technology, as their core focus. As we found when we explored how different members of the Open Development working group understood the concept of open development in 2012, many members focussed more upon open processes than on data and tech. Yet, for all this diversity of focus – the new OK tagline emphasises data alone.

I work on issues of open data everyday. I think it’s an important area. But it’s not the only element of open knowledge that should matter in the broad movement.

Whilst the Open Knowledge Foundation has rarely articulated the kinds of broad political critique of intellectual property regimes that might be found in prior Access to Knowledge movements, developing a concrete motivation and purpose statement gave the OKF chance to deepen it’s vision rather than narrow it. The risk Jo Bates has written about, of intellectual of the ‘open’ movement being co-opted into dominant narratives of neoliberalism, appears to be a very real one. In the motivation statement above, government and big corporates are cast as the problem, and technology and data in the hands of ‘citizens’, ‘scientists’, ‘entrepreneurs’ and (perhaps contradictorily) ‘enterprises’, as the solution. Alternative approaches to improving processes of government and governance through opening more spaces for participation is off the table here, as are any specific normative goals for opening knowledge. Data-centricity displaces all of these.

Now – it might be argued that although the motivation statement takes data as a starting point – is is really at its core about the balance of power: asking who should control data, information and knowledge. Yet – the analysis appears to entirely conflate the terms ‘data’, ‘information’ and ‘knowledge’ – which clouds this substantially.

(3) Data, Information and Knowledge

Data, Information, Knowledge ,Wisdom

The DIKW pyramid offers a useful way of thinking about the relationship between Data, Information, Knowledge (and Wisdom). This has sometimes been described as a hierarchy from ‘know nothing’ (data is symbols and signs encoding things about the world, but useless without interpretation), ‘know what’, ‘know how’ and ‘know why’.

Data is not the same as information, nor the same as knowledge. Converting data into information requires the addition of context. Converting information into knowledge requires skill and experience, obtained through practice and dialogue.

Data and information can be treated as artefacts/thigns. I can e-mail you some data or some information. But knowledge involves a process – sharing it involves more than just sending a file.

OKF has historically worked very much on the transition from data to information, and information to knowledge, through providing training, tools and capacity building, yet this is not captured at all in the core purpose. Knowledge, not data, has the potential to free, bringing greater autonomy. And it is arguably proprietary control of data and information that is at the basis of the power of the few, not any superior access to knowledge that they possess. And if we recognise that turning data into information and into knowledge involves contextualisation and subjectivity, then ‘information and insights’ cannot be by simultaneously ‘apparent’ to everyone, if this is taken to represent some consensus on ‘truths’, rather than recognising that insights are generated, and contested, through processes of dialogue.

It feels like there is a strong implicit positivism within the current core purpose: which stands to raise particular problems for broadening the diversity of Open Knowledge beyond a few countries and communities.

(4) Power, individualism and collective action

I’ve already touched upon issues of power. Addressing “global challenges like justice, climate changes, cultural matters” (from survey responses) will not come from empowering individuals alone – but will have to involve new forms of co-ordination and collective action. Yet power in the ‘core purpose’ statement appears to be primarily conceptualised in terms of individual “informed choices about how we live, what we buy and who gets our vote”, suggesting change is purely the result of aggregating ‘choice’, yet failing to explore how knowledge needs to be used to also challenge the frameworks in which choices are presented to us.

The ideas that ‘everyone’ can be empowered, and that when “knowledge is ‘open’ […] we are all empowered to make informed choices about our future” fails to take account of the wider constraints to action and choice that many around the world face, and that some of the global struggles that motivate many to pursue greater openness are not always win-win situations. Those other constraints and wider contexts might not be directly within the power of an open knowledge movement to address, or the core preserve of open knowledge, but they need to be recognised and taken into account in the theories of change developed.

In summary

I’ve tried to deal with the Motivation, Core Purpose and Tag-line statements with as carefully as limited free time allows – but inevitably there is much more to dig into – and there will be other ways of reading these statements. More optimistic readings are possible – and I certainly hope might turn out to be more realistic – but in the interest of dialogue I hope that a critical reading is a more useful contribution to the debate, and I would re-iterate my preliminary notes 1 – 5 above.

To recap the critique:

  • Developing a brand and statement of core purpose is an opportunity for dialogue and discussion, yet right now this opportunity appears to have be mostly missed;
  • The motivation, core purpose and tagline are more tech-centric and data-centric than the OKF community, risking sidelining other aspects of the open knowledge community;
  • There need to be a recognition of the distinction of data, information and knowledge, to develop a coherent theory of change and purpose;
  • There appears to be an implicit libertarian individualism in current theories of change, and it is not clear that this is compatible with working to address the shared global challenges that have brought many people into the open knowledge community.

Updates:

There is some discussion of these issues taking place on the OKFN-Discuss list, and the Wiki page has been updated from that I was initially writing about, to re-frame what was termed ‘core purpose’ as ‘brand core purpose’.

Five critical questions for constructing data standards

I’ve been spending a lot of time thinking about processes of standardisation recently (building on the recent IATI Technical Advisory Group meeting, working on two new standards projects, and conversations at today’s MIT Center for Civic Media & Berkman Center meet-up). One of the key strands in that thinking is around how pragmatics and ethics of standards collide. Building a good standard involves practical choices based on the data that is available, the technologies that might use that data and what they expect, and the feasibility of encouraging parties who might communicate using that standard to adapt their practices (more or less minimally) in order to adopt it. But a standard also has ethical and political consequences, whether it is a standard deep in the Internet stack (as John Morris and Alan Davidson discuss in this paper from 2003[1]), or a standard at the content level, supporting exchange of information in some specific domain.

The five questions below seek to (in a very provisional sense) capture some of the considerations that might go into an exploration of the ethical dimensions of standard construction[2].

(Thanks to Rodrigo Davies, Catherine D’Ignazio and Willow Brugh for the conversations leading to this post)

For any standard, ask:

Who can use it?

Practically I mean. Who, if data in this standard format was placed in front of them, would be able to do something meaningful with it. Who might want to use it? Are people who could benefit from this data excluded from using it by it’s complexity?

Many data standards assume that ‘end users’ will access the data through intermediaries (i.e. a non-technical user can only do anything with the data after it has been processed by some intermediary individual or tool) – but not everyone has access to intermediaries, or intermediaries may have their own agendas or understandings of the world that don’t fit with those of the data user.

I’ve recently been exploring whether it’s possible to turn this assumption around, and make simple versions of a data standard the default, with more expressive data models available to those with the skills to transform data into these more structured forms. For example, the Three Sixty Giving standard (warning: very draft/provisional technical docs) is based around the idea of a rich data model, but a simple flat-as-possible serialisation that means most of the common forms of analysis someone might want to do with the data can be done in a spreadsheet, and for 90%+ of cases, data can be exchanged in flat(ish) forms, with richer structures only used where needed.

What can be expressed?

Standards make choices about what can be expressed usually at two levels:

  • Field choice
  • Taxonomies / codelists

Both involve making choices about how the world is sliced up, and what sorts of things can be represented and expressed.

A thought experiment: If I asked people in different social situations an open question inviting them to tell me about the things a standard is intended to be about (e.g. “Tell me about this contract?”) how much of what they report can be captured in the standard? Is it better at capturing the information seen as important to people in certain social positions? Are there ways it could capture information from those in other positions?

What social processes might it replace or disrupt?

Over the short-term, many data standards end up being fed by existing information systems – with data exported and transformed into the standard. However, over time, standards can lead to systems being re-engineered around them. And in shifting the flow of information inside and outside of organisations, standards processes can disrupt and shift patterns of autonomy and power.

Sometimes the ‘inefficient’ processes of information exchange, which open data standards seek to rationalise, can be full of all sorts of tacit information exchange, relationship building etc. which the introduction of a standard could affect. Thinking about how the technical choices in a standard affect it’s adoption, and how far they allow for distributed patterns of data generation and management may be important. (For example, which identifiers in a standard have to be maintained centrally, thus placing a pressure for centralised information systems to maintain the integrity of data – and which can be managed locally – making it easier to create more distributed architectures. It’s not simply a case of what kinds of architectures a standard does or doesn’t allow, but which it makes easier or trickier, as in budget constrained environments implementations will often go down the path of least resistance, even if it’s theoretically possible to build out implementation of standard-using tools in ways that better respect the exiting structures of an organisation.)

Which fields are descriptive? Which fields are normative?

There has recently been discussion of the introduction on Facebook of a wide range of options for describing Gender, with Jane Fae arguing in the Guardian that, rather than provide a restricted list of fields, the field should simply be dropped altogether. Fae’s argument is about the way in which gender categories are used to target ads, and that it has little value as a category otherwise.

Is it possible to look at a data standard and consider which proposed fields import strong normative worldviews with them? And then to consider omitting these fields?

It may be that for some fields, silence is the better option that forcing people, organisations or events (or whatever it is that the standard describes) into boxes that don’t make sense for all the individuals/cases covered…

Does it permit dissent?

Catherine D’Ignazio suggested this question. How far does a standard allow itself to be disputed? What consequences are there to breaking the rules of a standard or remixing it to express ideas not envisaged by the original architects? What forms of tussle can the standard accommodate?

This is perhaps even more a question of the ecosystem of tools, validators and other resources around the standard than a standard specification itself, but these are interelated.

Footnotes

[1]: I’ve been looking for more recent work on ‘public interest’ and politics of standard creation. Academically I spend a lot of time going back to Bowker and Star’s work on ‘infrastructure’, but I’m on the look out for other works I should be drawing upon in thinking about this.

[2]: I’m talking particularly about open data standards, and standards at the content level, like IATI, Open 311, GTFS etc.