Evaluating open government data initiatives: can a 5-star framework work?

How do you evaluate or compare open government data initiatives (ODI)? With initiatives like data.gov and data.gov.uk well established, and well over 100 national and city-level open data initiatives emerging across the globe, the question of evaluating these initiatives is coming up more and more.

As Jose Alonso of the World Wide Foundation has noted the elements of an evaluation framework are, as yet, few and far between. Linked Open Data (LOD) actors often turn to the ‘5-Stars of linked data’ to provide some metrics for evaluating particular datasets, and Jose has proposed that a 5-Star framework might be extended to provide a more general evaluative framework. Identifying six dimensions which should be taken into account in an open data initiative (political, legal, organizational, technical, social and economic), Jose suggests that:

5-star scale [Open Data Initiative] is one that is 5-star on every single of the six dimensions.

Exactly what it means to be a ‘5-star’ initiative is as yet unspecified, and World Wide Web Foundation are starting to explore how such a framework might be developed, and where it might connect with the development of a multi-dimensional composite World Wide Web Index to rank the impact of the web/open data on countries around the world.

In this post I’ll explore some of the challenges ahead in constructing a 5-star evaluation framework for open data initiatives, offering some remarks of possible routes to explore in the future.

Heading for an index? Reduction and ranking

Simple metrics and indexes are clearly very useful advocacy tools, encouraging behaviour change amongst government, civil society and business communities. ‘Official’ UN, OECD or World Bank Statistics on health or education can drive Ministerial focus in a desire for a country not to slip down the rankings; and civil society indexes like IFPRI’s Global Hunger Index, and Publish What You Funds’s recently released pilot Aid Transparency Index are useful tools in putting an issue on the public agenda. A well constructed index, based on open inputs, has the potential to balance the simplicity conventionally demanded by public advocacy, with the depth required to identify the complexity of creating change around a particular issue.

In outputting a single number and allowing ranking to be constructed, indexes can capture news attention as evaluated entities (often countries) look to see their relative positioning on the scale. But if an index is based on good open data, and this is also published clearly (as in the case of the excellent online interface to the PWYF Index), then the index also provides pointers for countries, companies or whichever institution was evaluated to identify areas where they should focus their efforts for change to get a higher ranking next time. In a good index, the input measures should each be linked directly to, or be proxies for, states and actions that have a proven connection with positive change in the overall domain the index is concerned with. For example, in an ideal context, PWYF should be able to account for how improving against each measured input of their Aid Transparency Index can support improvements in the ultimate effectiveness of a donors aid.

Finding the right inputs for an index is challenging: Indexes tend to rely not only on reducing the output to a single number, but also on reducing each of the inputs to the index to things that are easily quantifiable and comparable: and this can introduce significant national and cultural biases. For example, the Open Knowledge Foundation Open Economic’s group’s pilot Open Knowledge Index includes attempts to capture the existence of an ‘Open Knowledge Society’ by looking at indicators such as “Number of Wikipedia edits per 100.000 inhabitants”, not only prioritizing a particular technical platform and failing to take into account the complexity of comparing editing practices between different country (and their potentially diverse language communities), but also ignoring the likely double-counting of earlier index elements such as “Tertiary Education Rates” and “Fixed broadband Internet subscribers (per 100 people)” introduced by looking at edits of a written online resource per head of population. In fairness, the Open Knowledge index is just in it’s early stages, but it’s reliance upon existing comparable datasets highlights a key limitation of international index construction: it would be hard to use one input dataset for one region, and another in another region without finding some way of making these comparable.

The reductionism of indexes has a further problem: what exactly is to be compared? A number of the indexes above rank ‘countries’, but an open data initiative index might need to cover not only national government-driven open data schemes, but also local government, community and transnational projects. Putting these in a single ranking would obviously be fraught with difficulty – and it might be appropriate to weight elements differently depending on the type of initiative being evaluated. But event amongst national open government data initiatives: would it be right to plot all governments on the same axis? How would comparing Kenya, India, Moldova and the US on an index help develop open data practice in each?

Whilst the political attraction of an index might prove a strong one for open data advocates, and indexes are certainly in vogue, the reduction of indicators to an index needs careful and critical thought – and, if the driving force behind an evaluation framework, could lead to some potentially damaging distortions in it’s development.

5-Star scales; 6 domains; at least 2 sides

Jose’s proposal however isn’t yet for an index. Rather, the post suggests that in addition to the 5-Stars of open linked data in the technical domain, similar ‘scales’ are needed in the political, legal, organizational, social and economic domains. This raises a number of questions.

Firstly, to what extent is the existing 5-Stars of Linked Data model truly a ‘scale’. I’ve commented before on the importance of seeing the stars as incremental and cumulative actions to be taken: as a checklist to work through in order, rather than as a ‘score’ where leaping to the top score without moving through the stages before is desirable. The 5-Stars might better be conceived of as a set of ‘indicators’, with early stars setting out the foundations that future steps should build upon. In the Hear by Right framework (PDF) for Organisational Change co-authored by Bill, my colleague at Practical Participation, 49 indicators are organised around a 7-S organisational change model, and divided into ‘Emerging’, ‘Established’ and ‘Advanced’ levels – highlighting that it’s important to move through ‘Emerging’ practice, to become ‘Established’ and to aspire to ‘Advanced’ forms of practice. It might be possible to maintain a ‘5-star’ model to indicate the movement through from emerging, to established and then advanced practice, but ensuring the design of indicators is not ambiguous about their cumulative nature will be important.

Secondly, we should ask to what extent each dimension (technical, political, legal, organizational, social and economic) can have a single set of cumulative indicators, or to what extent we might identify multiple sets of indicators in each. For example, the five-stars of linked open data (which Jose’s post might imply could simply be adopted as the indicator set for the ‘technical’ domain) only focusses on one set of technical issues in open data publishing: the format and publishing platform (i.e. non-proprietary / linked data; and the web). However, in looking at the use of open data in practice, we find there are important further technical elements to open data initiatives – including providing tools for data discovery (catalogues), providing open source code and tools for working with data, and ensuring technical platforms can cope with demand. Similarly, there might not be one simple sequential set of indicators for the economic or political domain (for example), but rather a parallel set of states that are good to get to, including having political leadership for open data; having open data about politics available; and having open data used in political decision making.

Which leads to the third question, and perhaps one of the most fundamental for an evaluation framework: what exactly are we evaluating? The current 5-Stars of Linked Open Data is primarily a supply-side evaluation: is data being provided. But we might look at the demand or use-side of each of the dimensions Jose points to, asking not only is this domain contributing to the availability of open data, but is open data being effectively used in this domain (which is also different from asking ‘is data about this domain being used effectively).

Jose has already noted the further challenge with the 5-star scale in terms of working out when a star is reached. Is an open data initiative only 5-star when all the data within it’s ambit is published according to Linked Open Data standards, when all public organisations are fully equipped to share and work with open data, and when the whole enterprise sector is engaged with open data and using it to create new jobs? Or is there some threshold of 10% of datasets; and 50% of organisations? Or is that threshold based on ‘valuable datasets’ , which, as Jose notes, raises the question “What does “most valuable” mean? For whom?”

A six-domain five-star model quickly loses it’s potential simplicity when we find the need to focus on both input and impact sides of the equation.

An refined proposal: creating organisational change, measuring social change

So where does this leave us? Again, turning back to learning from Hear by Right, it may be useful to draw a clear distinction between a framework for organisational change, and measuring the impacts of open data.

An organisational change framework for open data initiatives would draw upon the 5-stars already put forward as indicators for mapping and planning: organisations can map their own performance against these indicators (with the possibility of some external assessment and audit too) and can identify actions to move towards a higher level of indicator. Each indicator would identify a set of states or actions an organisation can take to effectively run an open data initiative. Each indicator should be based on a hypothesis about how that state or action increases the impact of open data, but the measurement should simply be based on whether or not the initiative has achieved that state, or taken that action. For example, in the economic dimension, an organisational change framework might include indicators for: ‘The initiative supports the development of a marketplace connecting potential infomediaries with possible sustainable sources of revenue for their services’, and would measure this on the basis of whether the initiative self-assesses (or others judge) that this in place. The organisational change framework would not include any metrics about impact, although if, over time, it became clear an indicator did not lead to the sorts of changes it was hypothesized to support, then it might be removed or amended.

An impact framework would identify key dimensions of change which could take the form of statements about the sorts of impacts an open data strategy might have. For example, “Open data supports economic growth” in the economic dimension; or “Open data is actively used by citizens in policy making processes”. These might have ‘suggested evidence’ requirements, but it’s unlikely these will be reducible to a single number in most cases. Both organisational change and social change are, to a significant extend, subjective. Whilst we can measure certain ‘states’ (existence of organisational policies and practices; performance statistics; etc.) any measurement of organisational and social change needs also to include a narrative component – highlighting experiences of change, and how the benefits of change are distributed, as well as look at aggregate measures of change.

In this approach, we disentangle ‘best practices’ and ‘impacts’ – and allow them to each be evaluated on their own terms. Both are still needed: pursuing organisational change without asking ‘what difference does this make?’ isn’t helpful. And equally, measuring impacts without hypothesizing about how to further them, and planning concrete steps to do so, creates massive missed opportunities.

It might even be possible to fit this approach with the elegance of a 5-star formulation.


  1. Bill Roberts

    Hi Tim

    All interesting stuff, but my feeling is that it is a bit premature, because our ability to measure impact of open data initiatives is still very limited and mostly at the anecdotal level. I suspect any quantitative rating would be giving a false impression of precision.

    There seems to be still quite a bit to do in terms of identifying and documenting positive impacts of open data – to spread some success stories and models that people can follow.

    (By the way I am trying to compile some references on open data success stories – got quite a few, but would be glad of pointers to more!)



  2. Bill Roberts

    OK fair point! And I agree with your idea of systematic collection of evidence.

  3. Jury Konga

    Hi Tim,

    Great post – lots to consider. I’ve been pushing the work of some orgs that maintain “Wellness” indicators that speak to individual and community WellBeing. There are many factors that yield the index value and the state of Open Gov – Open Data et al should be included as factors – not currently the case. Until we get some metrics around how Open Data directly impacts positively on citizens and society at large – we’ll collectively have limited success … by that I mean not optimal societal ROI. Talk soon

    Cheers Jury

  4. Julian Tait

    Hi Tim,

    I agree with what you are saying transposing something that is empirical ‘5 stars of Open Data’ on to something that is more complex and human is difficult. I would also suggest that there has to be consideration as to relevance and achievability. As you have said different countries, communities, cultures do things in different ways. I think the ‘one size fits all’ and establishment of a gold standard might sound good but could also be alienating and unattainable at the same time.

  5. Laurence Millar

    It is a good time to start this discussion, and thank you Tim for an excellent post.

    The first set of measures are from the inside – as well as the 5star metric which indicates how open the data is, there is also a measure of how much data is open, and how important it is. I was excited to see the map of global initiatives at Open@CITC, but when I clicked on a number of the countries with which I am familiar, I saw some serious over-assessment. National data catalogues are assessed as Status 3 (Open formats), when most of the data referenced in the catalogues is unstructured, and often non-machine readable. Similarly, most open data catalogues are still dominated by datasetsthat are easy to publish rather than things that are important.

    So there is a long way to go to even get the basic measures of “how open is the data of this government” – which needs to be made up of the 5-star grade, plus an assessment of how much government data is rated in each of the 5 stars.

    As for the impact of Open Data, I think that suffers from the attribution problems of any outcome-based evaluation, as well as the challenges of many diverse political, social and cultural systems in different countries. The OECD has wrestled with this challenge for years, and made a first step with the “Government at a Glance” publication a couple of years ago – which acknowledged the fundamental problems of comparing the impact of government performance in different countries.

    So my view is that attention is best channelled onto the measurement of “how open is this government” and more pointedly “how much more open has this government become over the last 12 months”

  6. Mark Golledge

    A really interesting post Tim and something I think we’ll be hearing much more of as we go forward.

    It’s actually something I’m touching on in my Masters dissertation (yes I did get around to doing it albeit a little later than planned!). I think it is right to be considering three factors here (similar to Laurence’s post):

    1. How open is an organisation (or departments within organisations)? I’ve tried to summarise this into some sort of framework (albeit still in development) which compares the rhetoric (i.e. what organisations say) with reality (what organisations do):

    a). Voluntary Transparency – an organisations says it is open and makes every effort to publish information in a transparent way
    b). Lip Service – an organisation says it is open but in reality very little is open and transparent
    c). Enforced Transparency – an organisation says it is ‘closed’ but information is published: at one end of this will be FoI’s (i.e. an organisation publishes what it is told to publish) at the other end will be things like Wikileaks – if you don’t publish it someone may publish it for you
    d). Secrecy – what many academics define as the default mindset of bureaucratic organisations

    In reality I know that there is a spectrum and merging of these areas – when we say voluntary transparency in reality there will be different elements to this with organisations publishing different types of information (and no doubt still holding information back).

    So the first I think is to consider how open an organisation is.

    Secondly, I think you are right, that for the information is published we need to consider how linked and readable the information is (and I think this is where the 5* comes in). This does cross-over with the above. It is all very well to say an organisation is publishing information voluntarily but I’m sure there are cases where they are doing so in a way which is making it very difficult to find. This I think would best be done on a dataset by dataset basis.

    Finally, there is the impact that open data is having and I think this will probably become a bigger factor going forward. We’ve seen and heard much about armchair auditors but certainly in the case of Local Government I don’t believe there has yet been much interest (at least not what was expected) – and studies around the £500 spending information seem to confirm this. This is of course not to say it’s not valuable but rather sometimes that some information (without context) can be less valuable than others (I’m not sure myself that starting with £500 spend information was right). The focus (at least from Government) seems to have shifted to economic value of open data – and the stimulus this might be able to provide to SME organisations.

    Whether there are metrics to determine the above I’m less convinced. However, I do wonder whether we are likely to be seeing less of a focus on voluntary transparency and more on enforced transparency going forward. If we do, that will be a real shame – I think this is an emerging agenda and I would like to see organisations voluntarily move forward (carrot rather than stick!).

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Open Data in Developing Countries

The focus of my work is currently on the Exploring the Emerging Impacts of Open Data in Developing Countries (ODDC) project with the Web Foundation.

MSc – Open Data & Democracy

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