Today I gave a brief presentation of the framework for measuring open data impact I created for UNDP Serbia last year, at the Open Belgium 2019 Conference.

The framework is meant to be relatable and usable for individual organisations by themselves, and based on how existing cases, papers and research in the past have tried to establish such impact.

Here are the slides.

This is the full transcript of my presentation:

Last Friday, when Pieter Colpaert tweeted the talks he intended to visit (Hi Pieter!), he said two things. First he said after the coffee it starts to get difficult, and that’s true. Measuring impact is a difficult topic. And he asked about measuring impact: How can you possibly do that? He’s right to be cautious.

Because our everyday perception of impact and how to detect it is often too simplistic. Where’s the next Google the EC asked years ago. but it’s the wrong question. We will only know in 20 years when it is the new tech giant. But today it is likely a small start-up of four people with laptops and one idea, in Lithuania or Bulgaria somewhere, and we are by definition not be able to recognize it, framed this way. Asking for the killer app for open data is a similarly wrong question.

When it comes to impact, we seem to want one straightforward big thing. Hundreds of billions of euro impact in the EU as a whole, made up of a handful of wildly successful things. But what does that actually mean for you, a local government? And while you’re looking for that big impact you are missing all the smaller craters in this same picture, and also the bigger ones if they don’t translate easily into money.

Over the years however, there have been a range of studies, cases and research papers documenting specific impacts and effects. Me and my colleagues started collecting those a long time ago. And I used them to help contextualise potential impacts. First for the Flemish government, and last year for the Serbian government. To show what observed impact in for instance a Spanish sector would mean in the corresponding Belgian context. How a global prediction correlates to the Serbian economy and government strategies.

The UNDP in Serbia, asked me to extend that with a proposal for indicators to measure impact as they move forward with new open data action plans in follow up of the national readiness assessment I did for them earlier. I took the existing studies and looked at what they had tried to measure, what the common patterns are, and what they had looked at precisely. I turned that into a framework for impact measurement.

In the following minutes I will address three things. First what makes measuring impact so hard. Second what the common patterns are across existing research. Third how, avoiding the pitfalls, and using the commonalities we can build a framework, that then in itself is an indicator.Let’s first talk about the things that make measuring impact hard.

Judging by the available studies and cases there are several issues that make any easy answers to the question of open data impact impossible.There are a range of reasons measurement is hard. I’ll highlight a few.
Number 3, context is key. If you don’t know what you’re looking at, or why, no measurement makes much sense. And you can only know that in specific contexts. But specifying contexts takes effort. It asks the question: Where do you WANT impact.

Another issue is showing the impact of many small increments. Like how every Dutch person looks at this most used open data app every morning, the rain radar. How often has it changed a decision from taking the car to taking a bike? What does it mean in terms of congestion reduction, or emission reduction? Can you meaningfully quantify that at all?

Also important is who is asking for measurement. In one of my first jobs, my employer didn’t have email for all yet, so I asked for it. In response the MD asked me to put together the business case for email. This is a classic response when you don’t want to change anything. Often asking for measurement is meant to block change. Because they know you cannot predict the future. Motives shape measurements. The contextualisation of impact elsewhere to Flanders and Serbia in part took place because of this. Use existing answers against such a tactic.

Maturity and completeness of both the provision side, government, as well as the demand side, re-users, determine in equal measures what is possible at all, in terms of open data impact. If there is no mature provision side, in the end nothing will happen. If provision is perfect but demand side isn’t mature, it still doesn’t matter. Impact demands similar levels of maturity on both sides. It demands acknowledging interdependencies. And where that maturity is lacking, tracking impact means looking at different sets of indicators.

Measurements often motivate people to game the system. Especially single measurements. When number of datasets was still a metric for national portals the French opened with over 350k datasets. But really it was just a few dozen, which they had split according to departments and municipalities. So a balance is needed, with multiple indicators that point in different directions.

Open data, especially open core government registers, can be seen as infrastructure. But we actually don’t know how infrastructure creates impact. We know that building roads usually has a certain impact (investment correlates to a certain % rise in GDP), but we don’t know how it does so. Seeing open data as infrastructure is a logical approach (the consensus seems that the potential impact is about 2% of GDP), but it doesn’t help us much to measure impact or see how it creates that.

Network effects exist, but they are very costly to track. First order, second order, third order, higher order effects. We’re doing case studies for ESA on how satellite data gets used. We can establish network effects for instance how ice breakers in the Botnian gulf use satellite data in ways that ultimately reduce super market prices, but doing 24 such cases is a multi year effort.

E puor si muove! Galileo said Yet still it moves. The same is true for open data. Most measurements are proxies. They show something moving, without necessarily showing the thing that is doing the moving. Open data often is a silent actor, or a long range one. Yet still it moves.

Yet still it moves. And if we look at the patterns of established studies, that is what we indeed see. There are communalities in what movement we see. In the list on the slide the last point, that open data is a policy instrument is key. We know publishing data enables other stakeholders to act. When you do that on purpose you turn open data into a policy instrument. The cheapest one you have next to regulation and financing.

We all know the story of the drunk that lost his keys. He was searching under the light of a street lamp. Someone who helped him else asked if he lost the keys there. No, the drunk said, but at least there is light here. The same is true for open data. If you know what you published it for, at least you will be able to recognise relevant impact, if not all the impact it creates. Using it as policy instrument is like switching on the lights.

Dealing with lack of maturity means having different indicators for every step of the way. Not just seeing if impact occurs, but also if the right things are being done to make impact possible: Lead and lag indicators

The framework then is built from what has been used to establish impact in the past, and what we see in our projects as useful approaches. The point here is that we are not overly simplifying measurement, but adapt it to whatever is the context of a data provider or user. Also there’s never just one measurement, so a balanced approach is possible. You can’t game the system. It covers various levels of maturity from your first open dataset all the way to network effects. And you see that indicators that by themselves are too simple, still can be used.

Additionally the framework itself is a large scale sensor. If one indicator moves, you should see movement in other indicators over time as well. If you throw a stone in the pond, you should see ripples propagate. This means that if you start with data provision indicators only, you should see other measurements in other phases pick up. This allows you to both use a set of indicators across all phases, as well as move to more progressive ones when you outgrow the initial ones.finally some recommendations.

Some final thoughts. If you publish by default as integral part of processes, measuring impact, or building a business case is not needed as such. But measurement is very helpful in the transition to that end game. Core data and core policy elements, and their stakeholders are key. Measurement needs to be designed up front. Using open data as policy instrument lets you define the impact you are looking for at the least. The framework is the measurement: Only micro-economic studies really establish specific economic impact, but they only work in mature situations and cost a lot of effort, so you need to know when you are ready for them. But measurement can start wherever you are, with indicators that reflect the overall open data maturity level you are at, while looking both back and forwards. And because measurement can be done, as a data holder you should be doing it.

29 reactions on “Measuring Open Data Impact – #OpenBelgium19

  1. There were several points made in the conversation after my presentation yesterday at Open Belgium 2019. This is a brief overview to capture them here.
    1) One remark was about the balance between privacy and openness, and asking about (negative) privacy impacts.
    The framework assumes government as the party being interested in measurement (given that that was the assignment for which it was created). Government held open data is by default not personal data as re-use rules are based on access regimes which in turn all exclude personal data (with a few separately regulated exceptions). What I took away from the remark is that, as we know new privacy and other ethical issues may arise from working with data combinations, it might be of interest if we can formulate indicators that try to track negative outcomes or spot unintended consequences, in the same way as we are trying to track positive signals.
    2) One question was about if I had included all economic modelling work in academia etc.
    I didn’t. This isn’t academic research either. It seeks to apply lessons already learned. What was included were existing documented cases, studies and research papers looking at various aspects of open data impact. Some of those are academic publications, some aren’t. What I took from those studies is two things: what exactly did they look at (and what did they find), and how did they assess a specific impact? The ‘what’ was used as potential indicator, the ‘how’ as the method. It is of interest to keep tracking new research as it gets published, to augment the framework.
    3) Is this academic research?
    No, its primary aim is as a practical instrument for data holders as well as national open data policy makers. It’s is not meant to establish scientific truth, and completely quantify impact once and for all. It’s meant to establish if there are signs the right steps are taken, and if that results in visible impact. The aim, and this connects to the previous question as well, is to avoid extensive modelling techniques, and favor indicators we know work, where the methods are straightforward. This to ensure that government data holders are capable to do these measurements themselves, and use it actively as an instrument.
    4) Does it include citizen science (open data) efforts?
    This is an interesting one (asked by Lukas of Luftdaten.info). The framework currently does include in a way the existence and emergence of citizen science projects, as that would come up in any stakeholder mapping attempts and in any emerging ecosystem tracking, and as examples of using government open data (as context and background for citizen science measurements). But the framework doesn’t look at the impact of such efforts, not in terms of socio-economic impact and not in terms of government being a potential user of citizen science data. Again the framework is to make visible the impact of government opening up data. But I think it’s not very difficult to adapt the framework to track citizen science project’s impact. Adding citizen science projects in a more direct way, as indicators for the framework itself is harder I think, as it needs more clarification of how it ties into the impact of open government data.
    5) Is this based only on papers, or also on approaching groups, and people ‘feeling’ the impact?
    This was connected to the citizen science bit. Yes, the framework is based on existing documented material only. And although a range of those base themselves on interviewing or surveying various stakeholders, that is not a default or deliberate part of how the framework was created. I do however recognise the value of for instance participatory narrative inquiry that makes the real experiences of people visible, and the patterns across those experiences. Including that sort of measurements would be useful especially on the social and societal impacts of open data. But currently none of the studies that were re-used in the framework took that approach. It does make me think about how one could set-up something like that to monitor impact e.g. of local government open data initiatives.

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