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New report: Governing by rankings – How the Global Open Data Index helps advance the open data agenda

- November 29, 2017 in Featured, Global Open Data Index, godi, GODI16, open data survey, research

This blogpost was jointly written by Danny Lämmerhirt and Mária Žuffová (University of Strathclyde). We are pleased to announce our latest report Governing by rankings – How the Global Open Data Index helps advance the open data agenda. The Global Open Data Index (GODI) is one of the largest worldwide assessments of how well governments publish open data, coordinated by Open Knowledge International since 2013. Over the years we observed how GODI is used to monitor open data publication. But to date, less was known how​ ​GODI​ ​may​ ​translate​ ​into​ ​open​ ​data​ ​policies​ ​and publication​. How does GODI mobilise support for open data? Which actors are mobilised? Which aspects of GODI are useful, and which are not? Our latest report provides insights to these questions.

Why does this research matter?

Global governance indices like GODI enjoy great popularity due to their capacity to count, calculate, and compare what is otherwise hardly comparable. A wealth of research – from science and technology studies to sociology of quantification and international policy – shows that the effects of governance indicators are complex (our report provides an extensive reading list). Different audiences can take up indices to different (unintended) ends. It is therefore paramount to trace the effects of governance indicators to inform their future design. The report argues that there are multiple ways of looking for ‘impacts’ depending on different audiences, and how they put GODI into practice. Does a comparative open data ranking like GODI help mobilise high-level policy commitments? Does it incentivise individual government agencies to adjust and improve the publication of open data? Does it open up spaces for discussion and deliberation between government and civil society? This thinking builds on an earlier report by Open Knowledge International arguing that indicators have different audiences, with different lived experiences, needs, and agendas. While any form of measurement needs to align with these needs to become actionable (which affects how the impact of indicators will take shape), it also needs to retain comparability.

Our findings

We used Argentina, United Kingdom and Ukraine as case studies to represent different degrees of open data publication, economic development and political set-up. Our report, drawing from a series of twelve interviews and document analysis, suggests that GODI drives change primarily from within government. We assume this finding is partly due to our limited sample size. While key actors in the government are easy to identify, as open data publication is often one of their job responsibilities,  further research is needed to identify more civil society actors and how they engage with GODI. Below we describe nine ways how GODI influences open data policy and publication.
  1. Getting international visibility and achieving progress in country rankings or generally high ranking may incentivise and maintain high-level political support for open data, despite non-comparability of results across years.  
  2. In the absence of open data legislation, GODI has been used by Argentinian government as a soft policy tool to pressure other government agencies to publish data.
  3. Government agencies tasked with implementing open data used GODI to reward and point out progress made by other agencies, but also flag blockages to high-level politicians.  
  4. GODI sets standards what datasets to publish and sets a baseline for improvement. Outcomes are debatable around categories where the central government does not have easy political levers to publish data.
  5. GODI may be confounded with broader commitments to open government and used as an argument to reduce investment in other aspects of open government agenda. In the past, some high-level politicians presented  high ranking in GODI as evidence of government transparency and obsoletion of other ways of providing government information.  
  6. This effect may possibly be exacerbated by superficial media coverage that reports on the ranking without engaging with broader information and transparency policies. An analysis of Google News results suggests that journalists tend to reproduce (mostly politicians’) misconceptions and confound a good ranking in GODI with a high degree of government transparency and openness.
  7. Our findings suggest that individuals and organisations working around transparency and anti-corruption make little use of GODI due to a lack of detail and a misalignment with their specialised work tasks. For instance, Transparency International Ukraine uses the Transparent Public Procurement Rating to evaluate the legal framework, aside from the publication of open data.
  8. On the other hand, academics show interest to GODI to develop new governance indicators. They also often use country scores as a proxy for measuring open data availability.
  9. GODI has a potential for use in data journalism. Data journalism trainers may use it as a source of government data during their trainings.  

What we learned and the road ahead

Our research suggests that governments in all analysed countries pay attention to GODI.  With a few exceptions, they use it mostly to support open data publication and pave the way for new open data policies. While this is a promising finding, it has important implications for GODI and its design. If GODI sets standards in open data publication, as some interviewees from the government suggest, it needs to make sure to represent different data demands in the assessment and to encourage the implementation of sound policies. The challenge is to support policy development, which is often a lengthy process as opposed to short-lived rank-seeking. Some interviewees suggested valuable avenues for GODI’s design. For instance, assessing progress in open data publication perpetually rather than once a year over a limited timespan would require a long-term commitment to open data publication and better opportunities for civic engagement, as it would prevent governments from updating datasets once a year before GODI’s deadline only. Another route forward is discussed in another recent research by OKI, highlighting the potential to adjust an open data index to align it more closely to specific needs of topical expert organisations. Beyond engaging via GODI, civil society and academia might also participate in the development of new data monitoring instruments such as the Open Data Survey, that are relevant for their mission.    

How do open data measurements help water advocates to advance their mission?

- November 23, 2017 in Global Open Data Index, godi, GODI16, open data survey, WASH, water quality

This blogpost was jointly written by Danny Lämmerhirt and Nisha Thompson (DataMeet). Since its creation, the open data community has been at the heart of the Global Open Data Index (GODI). By teaming up with expert civil society organisations we define key datasets that should be opened by government to align with civil society’s priorities. We assumed that GODI also teaches our community to become more literate about government institutions, regulatory systems and management procedures that create data in the first place – making GODI an engagement tool with government.

Tracing the publics of water data

Over the past few months we have reevaluated these assumptions. How do different members of civil society perceive the data assessed by GODI? Is the data usable to advance their mission? How can GODI be improved to accommodate and reflect the needs of civil society? How would we go about developing user-centric open data measurements and would it be worth to run more local and contextual assessments? As part of this user research, OKI and DataMeet (a community of data science and open data enthusiasts in India) teamed up to investigate the needs of civic organisations in the water, sanitation and health (WASH) sector. GODI assesses whether governments release information on water quality, that is pollution levels, per water source. In detail this means that we check whether water data is available at potentially a household level or  at each untreated public water source such as a lake or river. The research was conducted by DataMeet and supervised by OKI, and included interviews and workshops with fifteen different organisations. In this blogpost we share insights on how law firms, NGOs, academic institutions, funding and research organisations perceive the usefulness of GODI for their work. Our research focussed on the South Asian countries India, Pakistan, Nepal, and Bangladesh. All countries face similar issues with ensuring safe water to their populations because of an over-reliance on groundwater, geogenic pollutants like arsenic, and high pollutants from industry, urbanisation, farming, and poor sanitation.

According to the latest GODI results, openness of water quality data remains low worldwide.

What kinds of water data matter to organisations in the water sector?

Whilst all interviewed organisations have a stake in access to clean water for citizens, they have very different motivations to use water quality data. Governmental water quality data is needed to
  1. Monitor government activities and highlight general issues with water management (for advocacy groups).
  2. Create a baseline to compare against civil society data (for organisations implementing water management systems)
  3. Detect geographic target areas of under-provision as well as specific water management problems to guide investment choices (for funding agencies and decision-makers)
Each use case requires data with different quality. Some advocacy interviewees told us that government data, despite a potential poor reliability, is enough to make the case that water quality is severely affected across their country. In contrast, researchers have a need for data that is provided continuously and at short updating cycles. Such data may not be provided by government. Government data is seen as support for their background research, but not a primary source of information. Funders and other decision-makers use water quality data largely for monitoring and evaluation – mostly to make sure their money is being used and is impactful. They will sometimes use their own water quality data to make the point that government data is not adequate. Funders push for data collection at a project level not continuous monitoring which can lead to gaps in understanding. GODI’s definition of water quality data is output-oriented and of general usefulness. It enables finding the answer to whether the water that people can access is clean or not. Yet, organisations on the ground need other data – some of which is process-oriented – to understand how water management services are regulated and governed or what laboratory is tasked to collect data. A major issue for meaningful engagement with water-related data is the complexity of water management systems. In the context of South Asia, managing, tracking, and safeguarding water resources for use today and in the future is complex. Water management systems, from domestic to industrial to agricultural ones, are diverse and hard to examine and keep accountable. Where water is coming from, how much of it is being used and for what, and then how waste is being disposed of are all crucial questions to these systems. Yet there is very little data available to address all these questions.

How do organisations in the WASH sector perceive the GODI interface?

GODI has an obvious drawback for the interviewed organisations: transparency is not a goal for organisations working on the ground and does not in itself provoke an increase in access to safe water or environmental conservation. GODI measures the publication of water quality data, but is not seen to stimulate improved outcomes. It also does not interact with the corresponding government agency. One part of GODI’s theory of change is that civil society becomes literate about government institutions and can engage with government via the publication of government data. Our interviews suggest that our theory of change needs to be reconsidered or new survey tools need to be developed that can enhance engagement between civil society and government. Below we share some ideas for future scenarios.

Our learnings and the road ahead

Adding questions to GODI

Interviews show that GODI’s current definition of water quality data does not always align with the needs of organisations on the ground. If GODI wants to be useful to organisations in the WASH sector, new questions can be added to the survey and be used as a jumping off point for outreach to groups. Some examples include:
  1. Add a question regarding metadata and methodology documentation to capture quality and provenance water data, but also where we found and selected data.
  2. Add a question regarding who did the data collection government or partner organisation. This allows community members to trace the data producers and engage with them.
  3. Assess transparency of water reports. Reports should be considered since they are an important source of information for civil society.

Customising the Open Data Survey for regional and local assessments

Many interviewees showed an interest in assessing water quality data at the regional and hyperlocal level. DataMeet is planning to customise the Open Data Survey and to team up with local WASH organisations to develop and maintain a prototype for a regional assessment of water quality. India will be our test case since there is local data for the whole country available at varying degrees across states. This may include to also assess quality of data and access to metadata. Highest transparency would mean to have water data from each individual lab were the samples are sent. Another use case of the Open Data Survey would include to measure the transparency of water laboratories. Bringing more transparency and accountability to labs would be the most valuable for ground groups sending samples to labs across the country.

Map of high (> 30 mg/l) fluoride values from 2013–14. From: The Gonda Water Data story

Storytelling through data

Whilst some interviewees saw little use in governmental water quality data, its usefulness can be greatly enhanced when combined with other information. As discussed earlier, governmental water data gives snapshots and may provide baseline values that serve NGOs as rough orientation for their work. Data visualisations could present river and water basin quality and tell stories about the ecological and health effects. Behavior change is a big issue when adapting to sanitation and hygiene interventions. Water quality and health data can be combined to educate people. If you got sick, have you checked your water? Do you use a public toilet? Are you washing your hands? This type of narration does not require granular accurate data.

Comparing water quality standards

Different countries and organisations have different standards for what counts as high water pollution levels. Another project could assess how the needs of South Asian countries are being served by a comparing pollution levels with different standards. For instance, fluorosis is an issue in certain parts of India: not just from high fluoride levels but also because of poor nutrition in those areas. Should fluoride affected areas have lower permissible amounts in poorer countries? These questions could be used to make water quality data actionable to advocacy  groups.

The Open Data Survey: Measuring what matters to you

- November 21, 2017 in godi, Open Data Census, Open Data Index, open data survey

I once heard a brilliant government official say that in government you only measure what matters to you. This resonated with me back when I was a public servant and it makes even more sense now that I have participated over the last few years in the Global Open Data Index (GODI), one of Open Knowledge International (OKI)’s main projects. We developed GODI to address the question how much government information is published as open data. The index uses a league table that ranks countries from most to least open, based on their results for fifteen key datasets. In addition the survey compares the openness of key datasets worldwide, and lists which countries have, for example, the most open budgets, company registers or election data. But national open government data is only one aspect of the open data ecosystem. The Open Data Survey, the tool that powers GODI, allows to collect information on any aspect of open data.  Many organisations have repurposed the survey throughout the years to foreground information that these organisations find important, urgent or that help them reach their goals. In this blogpost I will highlight a few use cases of the Open Data Survey. In a follow-up post I will explain how you can start using the survey to measure what is important for you, whether with OKI hosting an instance for you or by deploying your own survey.

The Open Data Census

Our first example is the Open Data Census, a tool usually run by our local groups and chapters to understand how their local governments are performing in data publication. We have a record of about 40 different censuses assessing local and regional open data in many different countries. The census is the only tool to assess open data on a city level.

Example of a city-level census, comparing Argentinian cities with one another

Users of the Open Data Census include Open Knowledge Argentina, Open Knowledge Brazil and the Sunlight Foundation who assessed the openness of U.S. cities as part of its Open Cities programme. The Census results did not only highlight top performing cities in the United States, but also enabled Sunlight Foundation to do further policy analysis and understand why some cities perform better than others. Similar to the Global Open Data Index, the census measures the openness of around fifteen different datasets. But it is also fully customisable, allowing any organisation to assess various aspects of open data – from open data policies, through to publication of good quality data, or whether a local government engages with citizens to identify and publish the most relevant data.  

Code for America’s digital services survey

Code for America repurposed our Open Data Survey to assess the state of digital services in U.S. cities. As part of their Digital Front Door initiative, Code for America used a fork of the survey to assess if the government websites were “meeting Code for America’s criteria for good digital services, and prioritize opportunities for improvements”. With more than 40 cities assessed, this was probably one of the biggest alternative uses for the survey and a great example how to assess aspects such as design of websites (which is an important element for open data publication).

Assessing WASH resilience

Sheena Carmel Opulencia-Calub, a 2015 School of Data fellow, used the survey to produce a public local information resource centred around Water, Sanitation and Hygiene (WASH) needs, to guide policy-making in the Philippines and advance disaster risk preparedness of local authorities. The website is currently a proof-of-concept, designed for information managers from government and CSOs who take care of data gathering and sharing during emergency situations. The website also visualises the evolution of key indicators related to water and sanitation, helping local authorities and information managers to make better-informed decisions.

Interface of the WASH Resilience survey

It uses two types of league tables, including a ranking of WASH data quality and availability of the 225 cities and provinces of the Philippines. The contents are now outdated but this example shows how the Open Data Survey can be repurposed to not only assess the availability of open data in a specific sector, but also to link this assessment to follow-up actions with government.

Measure what matters to you

To conclude, the Open Data Survey is a versatile tool and can be used to rank, compare, and highlight very different aspects of (open) data. I hope abovementioned use cases sparked your interest and ideas how to use the Open Data Survey. Stay tuned – in a follow-up blogpost we will explain how to customise the survey in order to make it fit your needs.  

The future of the Global Open Data Index: assessing the possibilities

- November 1, 2017 in Global Open Data Index, godi, GODI16, Open Government Data, open-government

In the last couple of months we have received questions regarding the status of the new Global Open Data Index (GODI) from a few members of our Network. This blogpost is to update everyone on the status of GODI and what comes next. But first, some context: GODI is one of the biggest assessments of the state of open government data globally, alongside the Web Foundation’s Open Data Barometer. We notice persistent obstacles for open data year-by-year. High-income countries regularly secure top rankings, yet overall there is little to no development in many countries. As our latest State Of Open Government Data in 2017 report shows, data is often not made available publicly at all. If so, we see many issues around findability, quality, processability, and licensing. Individual countries are notable exceptions to the rule. The Open Data Barometer made similar observations in its latest report, mentioning a slow uptake of policy, as well as persistent data quality issues in countries that provide open data. So there is still a lot of work to be done. To resolve issues like engagement with our community, we started to explore alternative paths for GODI. This includes a shift in focus from a mere measurement tool to a stronger conversational device between our user groups throughout the process. We understand that we need to speak to new audiences and focus on measurement as a tool in real world applications. We need to focus more on this. We want to understand the use cases of the Open Data Survey (the tool that powers GODI and the Open Data Census) in different contexts and with different goals. We have barely seen a few of the possible uses of the tool in the open data sphere and we want to see even more. In order to learn more about how GODI is taken up by different user groups, we are also currently exploring GODI’s effects on open data policy and publication. We wish to understand more systematically how individual elements of the GODI interface (such as country ranking, dataset results, discuss forum entries) help mobilising support for open data among different user groups. Our goal is to understand how to improve our survey design and workflow so that they more directly support action around open data policy and publication. In addition we are developing a new vision for the Open Data Index to either measure open data on a regional and city-level or by topical areas. We will elaborate on this vision in a follow-up blogpost soon. Taking this all into account, we have decided to focus on working on the aforementioned use cases and a regional Index during 2018. In the meantime, we will still work with our community to define a vision that will make GODI a sustainable measurement tool: we understand that tracking the changes in government data publication is crucial for the activists and governments themselves. We know that progress around open data is slower than we would like it to be, but therefore we need to ensure that discussions around open data do not end. Please do not hesitate to submit new discussions around country entries on our forum or reach out to us if you have any ideas on how to take GODI forwards and improve. If you’re running an Open Data Census, we we’ll continue giving you support in the measurement you’re currently working on, whether it’s local, regional or you have any new idea of a Census you’d like to try. If you want to run your own Census, you can request it here, or send an email to to see how we could collaborate further.

Avoin data osana datataloutta – sumeaa möhnää vai kirkasta lähdevettä?

- August 15, 2017 in avoin data, avoin talousdata, datatalous, Featured, godi, liiketoiminta, mydata, Open Government Data, ostolaskut, talous

HS pääkirjoitus 7.8.2017 tarttui tärkeään aiheeseen, datan ja tiedon avoimuuteen – johon myös lukijat vastasivat [1, 2]. Timo Paukku puolestaan toi kolumnissaan (HS 10.8.2017) esiin Viron toiminnan digitalisaation mallimaana, toistaen usein kuullun vertauksen datasta uutena öljynä. Data on keskeisen liiketoiminnan resurssi ja moottori niin kutsutulle datataloudelle, johon Suomikin enenevässä määrin pyrkii. Avoimessa yhteiskunnassa datan pitäisi kuitenkin muistuttaa enemmän vettä kuin öljyä: kaikkien saatavilla olevaa perushyödykettä, joka kastelee ja virkistää laajasti eri aloilla julkisella, yksityisellä ja kolmannella sektorilla – kansalaisia unohtamatta.

Odotukset avoimen datan liiketoimintaa kohtaan ovat olleet epärealistiset – merkittävät taloudelliset vaikutukset tulossa

Suurimmat avoimeen dataan liittyvät odotukset liittyvät sen liiketoimintaa piristävään vaikutukseen. EU:ssa arvioidaan luotavan 100 000 työpaikkaa avoimen datan ympärille vuoteen 2020 mennessä.  Avoimen datan potentiaaliseksi suoraksi hyödyksi on arvioitu 40 miljardia ja välillisiksi 100 miljardia euroa vuosittain pelkästään EU-alueella. Esim. Berends et al. listaavat joitain avoimen datan vaikutuksia. Lähde: _data.pdf Odotukset uutta liiketoimintaa kohtaan ovat niin suuret, että ne ovat väistämättä epärealistiset. Avoimen datan merkitys liiketoiminnalle sekä laajemmin taloudelle on merkittävä, mutta ei irrallisena saarekkeena, vaan osana laajempaa kehitystä, johon kuuluvat avoimen datan lisäksi myös ilmiöt ja teknologiat kuten My Data (ihmisten itseään koskeva ns. omadata), massadata, data-analytiikka ja tekoäly. Saadaksemme hyötyä irti avoimesta datasta, on meidän perinteisen innovaatiotoiminnan lisäksi panostettava avoimen datan löydettävyyteen ja laatuun sekä datatalouden osaamiseen kaikilla koulutustasoilla. On kuiteknin syytä muistaa, että suuri osa avoimen datan vaikuttavuudesta tulee muualta kuin suoraan liiketoiminnasta. Jos ajatellaan vaikkapa leikkausjonojen pituutta, hoitoonpääsytilastoja, velkaantumista, valtion hankintoja tai avoimia lobbaustietoja, on helppo kuvitella miten hyödyllistä olisi saada dataa ja analysoida sitä eri näkökulmista. Suomessa julkisen datan avaamista ja hyödyntämistä edistetään laajasti, hyvässä yhteistyössä julkishallinnon, yritysten ja järjestöjen kesken. Open Knowledge Finland ry ja kansainvälinen Open Knowledge -järjestöverkosto edistävät tiedon avaamista Suomessa ja ympäri maailmaa. Laajat julkiset hankkeet, kuten kuuden suurimman kaupungin 6Aika-hanke, edistävät avoimeen dataan perustuvan uuden liiketoiminnan syntymistä kuudessa suurimmassa kaupungissamme – -sivustolta löytyy toista sataa avointa dataa hyödyntävää palvelukuvausta! Pääkaupunkiseudun kaupunkien erinomaiseen Helsinki Region Infoshare -palveluun, josta löytyy satoja tietoaineistoja, käydään puolestaan jatkuvasti tutustumassa ympäri maailmaa. Elinkeinoelämän tutkimuslaitos Etla ja Open Knowledge Finland selvittivät yhdessä avoimen datan vaikuttavuutta liiketoiminnassa. Selvityksen perusteella avointa dataa innovaatiotoiminnassaan hyödyntävät yritykset tuovat markkinoille uusia tavara- ja palveluinnovaatioita suhteellisesti useammin kuin muut yritykset. Dataa uusien innovaatioiden kehittämisessä käyttäneiden ICT-yritysten liikevaihto kasvoi vuosina 2012–14 keskimäärin yli 17 prosenttia muita enemmän. Erityisesti liikennetietojen hyödyntäminen kiihdytti liikevaihdon kasvua. (Tämän tutkimukset sisällöistä tulossa oman kirjoitus myöhemmin.)

Talousdataa on avattu ja avataan lisää lähiaikoina

Pääkirjoituksessa ettei dataa, jonka avulla voisi seurata esimerkiksi valtion ja kuntien viranomaisten rahankäyttöä, ole ollut saatavilla. Kirjoittaja on kyllä osin oikeassa siinä mielessä, että kansainvälisissä vertailuissa nimenomaan taloustietojen avoimuuden osalta Suomen sijoittuu surkeasti, mutta juuri nyt on hyvä mahdollisuus kohentaa tilannetta. Toisaalta, kaupunkien ostodataa on avattu useissa suomalaisissa kaupungeissa. Esimerkiksi Helsinki on avannut yksityiskohtaisen ostodatansa jo vuosia sitten (vrt. esim.  HS 28.11.2014) ja kaupunginjohtaja on arvioinut pelkästään datan avaamisen tuovan 1-2% säästöt vuosittain. Valmisteilla oleva ns. Hansel-laki avaisi koko julkishallinnon hankinnat ja ostot. Tämän seurauksena Suomi täyttäisi kansainvälisiä sitoumuksiaan, saisi verovarat tehokkaammin käyttöön, kilpailu julkisista hankinnoista olisi reilumpaa ja julkinen rahankäyttö olisi ylipäätään avoimempaa. Lähde: Pääkirjoituksessa mainittiin myös, että “toistaiseksi avattu data on verotietoja lukuunottamatta harmitonta”. Avoimuus arveluttaa monia. On selvää, että avattu data ei saa loukata esimerkiksi henkilön yksityisyyttä. Sen sijaan huoli avoimuuden ja toiminnan läpinäkyvyyden mukana mahdollisesti tulevasta kritiikistä ei ole kestävä peruste tiedon panttaamiselle. Avoimen datan avulla voidaan tunnistaa hallinnon tai yritysten tehottomuutta tai epäselvyyksiä, auttaa kansalaisia ymmärtämään talouden tai ympäristön tilaa paremmin tai kehittämään palveluita, jotka esimerkiksi helpottavat metsän hoitoa tai myyntiä. Julkisten tietovarantojemme monipuolinen ja älykäs hyödyntäminen on yhteiskuntamme etu ja kilpailuvaltti. Juuri siksi meidän on edelleen pistettävä painetta päättäjiä ja datanomistajia kohtaan. Tahdomme enemmän! The post Avoin data osana datataloutta – sumeaa möhnää vai kirkasta lähdevettä? appeared first on Open Knowledge Finland.

Valtion hankintatiedot avoimena datana – hieno edistysaskel tulossa?!

- August 9, 2017 in avoin data, avoin hallinto, Featured, godi, godi 2016, hansel, julkiset hankinnat, Open Government Data, Open Government Partnership, Open Spending

Tiedon avoimuutta on tarpeen lisätä, Helsingin Sanomien pääkirjoituksessa 7.8.2017 todetaan. Olemme samaa mieltä! Viime vuosina useat Suomen kunnat ovat julkaisseet tietoja omista hankinnoistaan, jopa kuittitasolla. Tämä käytäntö on laajenemassa uuden ns. Hansel-lain myötä, jonka myötä eri ministeriöiden, laitosten, virastojen ja mahdollisesti maakuntien hankinnat julkaistaisiin keskitetysti valtion hankintayhtiön Hansel Oy:n toimesta. Hallitus valmistelee uutta ns. Hansel-lakia (Hallituksen esitys HE 63/2017 vp Hallituksen esitys eduskunnalle laiksi Hansel Oy -nimisestä osakeyhtiöstä annetun lain muuttamisesta). Laki on käsittelyssä talousvaliokunnassa, jossa sen yksityiskohtia viimeistellään. Avoimuuden ja avoimen datan kannalta erityisen kiinnostavia ovat lakiehdotuksen kohdat, jossa ehdotetaan säädettäväksi uusi säännös hankintatiedon käsittelyyn liittyvästä tietojensaanti- ja käsittelyoikeudesta (2§ ja 5§). Nähdäksemme toteutuessaan Hansel-laki lisää hallinnon avoimuutta erinomaisella tavalla. Samalla kun Suomi täyttää kansainvälisiä sitoumuksiaan, saamme verovarat tehokkaammin käyttöön, kilpailu julkisista hankinnoista on reilumpaa ja julkinen rahankäyttö on ylipäätään avoimempaa.

Hansel-laki ja hankintoja koskeva avoin data

Hansel Oy on siis toiminut valtion yhteishankintayksikkönä ja kilpailuttanut asiakkailleen sellaisia tavara- ja palveluhankintoja, joita valtionhallinnossa käytetään laajasti. Hanselin tehtäviin on kuulunut myös asiakkaiden omien hankintojen kilpailuttaminen sekä erilaiset hankintatoimeen liittyvät asiantuntijatehtävät. Viime vuosina yhtiön tehtävät ovat kehittyneet muun muassa valtion hankintatoimen digitalisointiohjelman myötä, minkä vuoksi lakiin ehdotetaan tehtäväksi joitakin yhtiön tehtäviin liittyviä täsmennyksiä.  Laissa yhtiön tehtäviä siis ajantasaistetaan. Talousvaliokunnassa lakitekstiä on tiettävästi muotoiltu eteenpäin, mutta viimeisin julkinen versio (HE 63/2017) kuvaa Hanselin muuttuneita tehtäviä mm. seuraavasti (boldaus kirjoittajan): 2§ Yhtiön tehtävät 2 mom: Yhtiön tehtävänä on tuottaa asiakkailleen yhteishankintatoimintoja ja hankintojen tukitoimintoja. Yhtiö ylläpitää hankintasopimuksia ja tuottaa asiakkailleen hankintasopimuksiin liittyvää asiantuntijapalvelua. Lisäksi yhtiön tehtävänä on tuottaa asiakkailleen hankintatoimeen liittyviä asiantuntija- ja kehittämispalveluja sekä hankintatiedon käsittely- ja analysointipalveluja ja näihin liittyviä teknisiä ratkaisuja. 5 §  Tiedonsaantioikeus ja tietojen tuottaminen 4 mom: Yhtiö voi tuottaa, luovuttaa ja julkaista hankintatietoa käsittävää tietoaineistoa, jos tietoaineiston luovuttaminen ei sen muodostamisessa käytettyjen hakuperusteiden, tietojen määrän, laadun tai sisällön taikka tietoaineiston käyttötarkoituksen vuoksi ole vastoin sitä, mitä tietojen salassapidosta ja henkilötietojen suojasta säädetään. Alla muutama Open Knowledge Finland ry:n näkemys lakiin liittyen.

Hansel-laki lisää julkisten hankintojen tervettä kilpailua ja tehokkuutta

Hankintatietojen avoimuus edistää reilua kilpailua eri toimittajien kesken. Hankintojen vertailutietojen kautta saadaan kustannustehokkuutta hankintoihin ja sitä kautta verovarojen käyttöön. Kun hankinnat kuvataan vertailukelpoisesti, voidaan helposti seurata esimerkiksi, maksaako joku yksikkö huomattavan erilaista hintaa toiseen verrattuna tai onko hankinnoissa jotain muuta poikkeavaa tai erikoista ja kenties parannettavaa (kuten hankintojen kasautuminen vuoden loppuun). Pidemmällä tähtäimellä vertailukelpoiseen dataan voisi lisätä tai yhdistää vaikkapa alkuperätietoa, sertifiointeja, eettistä tietoa tai muuta vertailutietoa. Yksityiskohtainen hankintatieto voi auttaa ehkäisemään korruptiota ja harmaata taloutta.

Julkisuuslaki, läpinäkyvyys ja oikeus tietoon koskee myös hankintatietoja

Joka tapauksessa julkisuuslain mukaan kansalaisilla, järjestöillä ja medialla on jo nyt olemassa oikeus tietoon – myös hankintatietoon – silloin kun kyse ei ole erityisistä seikoista kuten esimerkiksi turvallisuusasioista tai tietyn tyyppisistä yrityssalaisuuksista. Riippumatta Hansel-laista, oikeus tähän tietoon on olemassa, eikä siltä osin ole tiedossa muutoksia.  Mutta kiinnostava muutos on, että Hansel-lain myötä saadaan selkeyttä ja yhdenmukaisuutta tiedon julkaisuun liittyviin käytänteihin ja laki toteuttaa ja tarkentaa siten julkisuuslain henkeä.

Yksi toimija hankintatiedon julkaisijana on tehokas tapa lisätä läpinäkyvyyttä ilman suurta hallinnollista taakkaa julkishallinnolle

Yksi datan avaamisen haasteista julkishallinnossa on ollut julkisuuslain erilaisten tulkintojen määrä – tämä tuli esille mm. Valtioneuvoston kanslian selvitys- ja tutkimustoiminnan “Avoimen datan hyödyntäminen ja vaikuttavuus”  -raportissat, jonka ETLA ja Open Knowledge Finland tekivät. Vastaavasti kaupungit avatessaan ostolaskujaan, ovat soveltaneet toisistaan poikkeavia käytäntöjä ja dataformaatteja. 6Aika-hanke ja kuntaliitto ovatkin pyrkineet ohjeistamalla yhtenäistämään käytäntöjä. Kaavailtu käytäntö yhtenäistää käytäntöjä eri ministeriöiden, virastojen ja tutkimuslaitosten kesken. Kaavailtu käytäntö keventää hallinnollista taakkaa kun asiat, kuten tiedon siivoaminen, formatointi, priorisointi, ongelmienratkaisu, dokumentointi, julkaisukäytännöt ym. tukitoiminnot hoidetaan yhdessä paikassa, eli Hansel toimii tässä siis eräänlaisena ns. clearinghousena, laadunvarmistajana ja tiedon hyödyntäjien rajapintana. Yhtenäiset käytännöt puolestaan paitsi lisäävät julkishallinnon tehokkuutta datan julkaisussa, myös helpottavat datan löydettävyyttä ja hyödynnettävyyttä.  Sivumennen sanoen, uuden lakiehdotuksen kaavailemat tietopalvelut täydentävät muita meneillään olevia hankkeita, kuten YTI-hanke ja Kuntatieto-ohjelma. Hansel on valtionvarainministeriön ohjaukessa ja hankintatietojen avoimuus yhtenä asiana Valtion hankintojen digitalisaatio -toteutusohjelmaa, joten tahtotilaa modernisointiin tuntuu olevan laajemmin.  

Kansainväliset johtajuus ja tehtyjen sitoumuksien lunastaminen

Yleisesti Suomi sijoittuu kansainvälisesti avoimeen dataan ja avoimeen tietoon liittyvissä vertailuissa melko hyvin. Esimerkiksi uusimmassa Open Knowledge Internationalin Government Open Data Index 2016 -vertailussa olemme sijalla 5. Toisaalta, nimenomaan taloustietojen avoimuudessa olemme varsin surkeita – hankintojen (“procurement” – hankintailmoitusten ja sopimusten) tiimoilta “45% avoin” ja ostojen (“Government spending” – todellinen kulutus) jopa hälyttävällä 0% tasolla! Hansel-lain myötä pysymme mukana kansainvälisessä kehityksessä kun vahvistamme todettuja heikkouksiamme.   Suomi on myös mukana USA:n ex-Presidentti Obaman aloittamassa avoimen hallinnon kumppanuusohjelmassa (Open Government Partnership), jossa eri maiden (yli 70 maata on mukana) hallinnot yhdessä kansalaisyhteiskunnan kanssa tekevät sitovia avoimuutta edistäviä konkreettisia toimenpiteitä ja sitoumuksia.  Hankintatietojen avoimuus on myös Suomen Avoimen hallinnon 3. Toimintaohjelmassa (2017-2019) yhtenä konkreettisena lupauksena. Toimintaohjelmassa sanotaan näin.
  1. sitoumus
Julkaistaan valtion hankintatiedot kansalaisille. Julkaistaan avoimesti verkossa tiedot siitä, mitä valtio ostaa, millä rahalla ja mistä. Valtion hankintatiedot julkaistaan keväällä 2017 avoimena datana. Samalla toteutetaan kaikille avoin palvelu, jossa kansalaiset ja yritykset voivat seurata lähes reaaliaikaisesti valtion hankintoihin liittyvän rahan käyttöä. Palvelujen tietosisältönä ovat hankintojen julkiset tiedot, joista käy ilmi, mitä valtion organisaatiot hankkivat ja mistä hankinnat tehdään. Sinänsä Hansel-laki ja sen kuvailemat hankintatietoon liittyvät tietopalvelut eivät ole välttämättä suunniteltuja “vain” kansalaisille, vaan palveluille on luonnollisesti useita eri käyttäjiä, kuten yritykset, media ja julkinen sektori itse. Ylipäätään olennaista on, että data avataan. Tällöin erilaiset toimijat voivat tehdä omista näkökulmistaan erilaisia kiinnostavia sovelluksia – joku tekee vertailuja tai visualisointeja, joku myynnin ja markkinoinnin työkaluja ja niin edelleen! Näin eri toimijat täydentävät Hanselin osaamista ja tarjontaa – tieto kun ei jakamalla kulu. Eräänlainen verrokki voisi olla valtion budjetti ja sen ympärillä olevat sovellukset: valtion budjettia kuvaava, Hahmota Oy:n tekemä joka tavallaan täydentää VM:n omaa -palvelua. Vastaavasti Hanselin mahdollisesti tuottaman verkkotyökalun (jolla voi tutkia ja analysoida hankintoja tietyin kriteerein) lisäksi on hyvin mahdollista, että syntyy muita palveluita tai analyysityökaluja hankintoihin. Toteuttaakseen kaavailtua lakia sekä em. avoimen hallinnon sitoumusta, Hansel onkin käsittääkseni hahmotellut tulevaa verkkopalvelua, jossa hankintoja voisi analysoida. Alla muutamia esimerkinomaisia ruutukaappauksia sovelluksesta, jotka antavat suuntaviivoja siitä miltä verkkopalvelu voisi näyttää. Nämä ruutukaappaukset ovat tietystikin suuntaa-antavia, mutta vaikuttavat lupaavalta. Tarkempia analyysejä varten itse kukin voisi sitten ladata tietoa sopivin kriteerein rajattuna. Ylipäätään talouden ja talouselämän avoimuutta ja avointa tietoa olisi järkevää lisätä jatkossa. Tavoitteena tulisi mielestämme olla, että “kolminaisuus”, eli  julkiset budjetit, sopimukset ja hankinnat olisivat saatavilla avoimesti standardimuotoisina. Hankintatiedot on erinomainen askel.   Odotamme Open Knowledge:ssa innolla uutta Hansel-lakia ja ylipäätään julkisten hankintojen lisääntyvää avoimuutta. Avoimuus on omiaan paitsi hälventämään mahdollista epäluottamusta, myös lisäämään tehokkuutta ja reilua kilpailua. Kyseessä on siis veronmaksajien etu ja oikeudenmukaisuus. The post Valtion hankintatiedot avoimena datana – hieno edistysaskel tulossa?! appeared first on Open Knowledge Finland.

Ποια δεδομένα χρειαζόμαστε; Η ιστορία της υποτροφίας του Cadasta GODI

- June 26, 2017 in Featured, Featured @en, godi, News, ανοικτά δεδομένα, Νέα

Από τη Mor Rubinstein Αυτό το blogpost γράφτηκε από τις Lindsay Ferris και Mor Rubinstein Υπάρχουν πολλά δεδομένα εκεί έξω, αλλά ποιοι χρήστες δεδομένων χρειάζονται για να λύσουν τα προβλήματά τους; Πώς μπορούμε εμείς, ως εξωτερικό σώμα, να γνωρίζουμε ποια δεδομένα είναι ζωτικής σημασίας, ώστε να τα μετρήσουμε; Επιπλέον, τι πρέπει να κάνετε όταν δημοσιεύονται […]

What is the difference between budget, spending and procurement data?

- May 18, 2017 in Global Open Data Index, godi, OpenSpending

Fiscal data is a complex topic. It comes in all different kind of formats and languages, its’ availability cannot be taken for granted and complexity around fiscal data needs special skills and knowledge to unlock and fully understand it. The Global Open Data Index (GODI) assesses three fiscal areas of national government: budgets, spending, and procurement. Repeatedly our team receives questions why some countries rank low in budgets, public procurement or spending, even though fiscal data is openly communicated. The quick answer: often we find information that is related to this data but does not exactly describe it in accordance with the described GODI data requirements. It appears to us that a clarification is needed between different fiscal data. This blogpost is dedicated to shed light on some of these questions. As part of our public dialogue phase, we also want to address our experts in the community. How should we continue to measure the status of these three key datasets in the future? Your input counts! Should we set the bar lower for GODI and avoid measuring transactional spending data at all? Is our assessment of transactional spending useful for you? You can leave us your feedback or join the discussion on this topic in our forum.

The different types of fiscal data

A government budget year produces different fiscal data types. Budgeting is the process where a government body sets its priorities as to how it intends to spend an amount of money over a specific time period (usually annually or semi-annually). Throughout the budgeting cycle  (the process of defining the budget), an initial budget can undergo revisions to result in a revised budget. Spending is the process of giving away money. This mean, the money might be given as a subsidy, a contract, refundable tax credit, pension or salary. Procurement is the process of selecting services from a supplier who fits best the need. That might involve selecting vendors, establishing payment terms, some strategic tender or other vetting mechanism meant to prevent corruption. Not only are the processes linked to each other, the data describing these processes can be linked too (e.g. in cases where identifiers exist linking spending to government budgets and public contracts). For laypersons, it might be difficult to tell the difference when they are confronted with a spending or procurement dataset: Is the money I see in a dataset spending, or part of contracting? The following paragraphs explain the differences.


As mentioned above, budgeting is called the process where a government body decides how to spend money over a certain time period. The amount is broken into smaller amounts (budget items) which can be classified as follows:
  • Administrative (which government sub-unit gets the money)
  • Functional (what the money is going to be used for)
  • Economic (how the money is going to be used, e.g., procurement, subsidies, salaries etc.)
  • Financing source (where the money should come from).
After the budget period ends, we know how much money was actually spent on each item – in theory. The Global Open Data Index assesses budget information at the highest administrative level (e.g. national government, federal government), which is broken down in one of these classifications. Here is an example of some fully open budget data of Argentina’s national government.

Example of Argentina’s national government budget 2017 (table shortened and cleaned)

The image shows the government entity, and expenditures split into economic classification (how the money is used). At the far right, we can see a column describing the total amount of money effectively spent on a planned budget expenditure. It basically compares allocated and paid money. This column must not be mixed with spending information on a transactional level (which displays each single transaction from a government unit to a recipient).


The Spending Data Handbook describes spending as “data relating to the specific expenditure of funds from the government”. Money might be given as a subsidy, as payment for a provided service, a salary (although salaries will seldom be published on a transactional level), a pension fund payment, a contract or a loan, to name just a few.
GODI focusses on transactions of service payments (often resulting from a prior procurement process). Monetary transactions are our baseline for spending data. GODI assesses the following information:
  • The amount that was transferred
  • The recipient (an entity external to the government unit)
  • When the transaction took place
  • Government office paying the transaction
  • Data split into individual transactions
GODI exclusively looks for single payment transfers. The reason why we are looking at this type of data is that spending patterns can be detected, and fraud or corruption uncovered. Some of the questions one might be able to address include: Who received what amount of money? Could government get its services from a cheaper service provider? Is government contracting to a cluster of related companies (supporting cartels)? GODI’s definition of spending data, even though ambitious in scope, does not consider the entire spectrum of transactional spending data. Being produced by many agencies, spending data is scattered  across different places online. We usually pick samples of specific spending data such as government payments to external suppliers (e.g. the single payments through a procurement process). Other types of payment, such as grants, loans or subsidies are then left aside. Our assessment is also ‘generous’ because we accept spending data that is only published above a certain threshold. The British Cabinet Office, a forerunner in disclosing spending data, only publishes data above £25,000. GODI accepts this as valid, even though we are aware that spending data below this amount remains opaque. There are also many more ways to expand GODI’s definition of spending data. For instance, we could ask if each transaction can be linked to a budget item or procurement contract so that we understand the spending context better.

Example image of British Spending data (Cabinet Office spending over £25,000)

Above is an example image of Great Britain’s Cabinet Office spending. You can see the date and the amount paid by government entity. Using the supplier name, we can track how much money was paid to the supplier. However, in this data no contract ID or contract name is provided that could allow to fully understand as part of what contracts these payments have been made.


When purchasing goods and services from an external source, government units require a certain process for choosing the supplier who fits best the need. This process is called procurement and includes planning, tendering, awarding, contracting and implementation. Goals are to enable a fair competition among service providers and to prevent corruption. Many data traces enable to shed light on each procurement stage. For example one might want to understand from which budget a service is gonna be paid, or what amount of money has been awarded (with some negotiation possible) or finally contracted to a supplier. This blogpost by the Open Contracting Partnership illustrates how each of the procurement stages can be understood through different data. GODI focuses on two essential stages, that are considered to be a good proxy to understand procurement. These however do not display all information. Tender phase
  • Tenders per government office
  • Tender name
  • Tender description
  • Tender status
Award phase
  • Awards per government office
  • Award title
  • Award description
  • Value of the award
  • Supplier’s name
Any payment resulting out of government contracts with external suppliers (sometimes only one, sometimes more) has to  be captured in government spending. For example, there might a construction contractor that is being paid by milestone, or an office supplies dealer which is chosen as a supplier. Then each spending transaction is for a specific item purchased through a procurement process. Below you can see a procurement database of Thailand. It displays procurement phases, but does not display individual transactions following from these. This particular database does not represent actual spending data (monetary transactions), but preceding stages of the contracting process. Despite this the platform is misleadingly called “Thailand Government Spending”.

Procurement database in Thailand

Another example is a procurement database indicating how much money has been spent on a contract:

Example for the procurement website ‘Cuánto y a quién se contrató’ (Colombia)

The road ahead – how to measure spending data in the future

Overall, there is slow but steady progress around the openness of fiscal data. Increasingly, budget and procurement data is provided in machine-readable formats or openly licensed, sometimes presented on interactive government portals or as raw data (more detail see for example in the most recent blogpost of the Open Contracting Partnership around open procurement data). Yet, there is a long way to go for transactional spending data. Governments do first laudable steps by creating budget or procurement websites which demonstrate how much money will or has been spent in total. These may be confusingly named ‘spending’ portals because in fact they are linked to other government processes such as budgeting (e.g. how much money should be spent) or procurement (how much money has been decided to pay for an external service). The actual spending in form of single monetary transactions is missing. And to date there is no coherent standard or specification that would facilitate to document transactional spending. We want to address our experts in the community. How should we continue to measure the status of these three key datasets in the future? Your input counts!  You can leave us your feedback and discuss this topic in our forum.   This blog was jointly written by Danny Lämmerhirt and Diana Krebs (Project Manager for Fiscal Projects at Open Knowledge International)

How to Read the Global Open Data Index Results

- May 2, 2017 in Global Open Data Index, godi, Open Government Data

The Global Open Data Index (GODI) is a tool to educate civil society and governments about open government data publication. We do so through presenting different information, including places scores, ranking, and scores for each data category per place, and comments by our submitters and reviewers. Even though we try to make this assessment coherent and transparent as possible, interpreting the results is not always straightforward. While open data has a very strict definition, scoring of any index is a discretional action. In real life, you can’t be partly open – either the data fit the criteria, or they do not.  This blog post will help GODI user to understand the following:  – What does the final score mean?  – How to interpret scores that vary between 0%, 40% or 70%?  – What does a score of 0% mean? For a more thorough explanation on how to read the results, go to 

What does the score mean?

Our scoring (ranging from 0% open to 100% open) does not necessarily show a gradual improvement towards open data. In fact, we assess very different degrees of data openness – which is why any score below 100 percent only indicates that a dataset is partially open. These levels of openness include public data, access-controlled data, as well as data gaps (See GODI methodology). To understand the differences we highly recommend reading each score together with our openness icon bar (see image below). For instance: a score of 70% can say that we found access-controlled, machine-readable data, that cannot be downloaded in bulk. Any score below 100% means “no access”, “closed access” or “public access”. Here we explain what each of them means, and how the data for each category look in practice.

Public Access Data

Data is publicly accessible if the public can see it online without any access controls. It does not imply that data can be downloaded, or that it is freely reusable. Often it means that data is presented in HTML on a website. The image above shows a search interface of a company register. It allows for targeted searches for individual companies but does not enable to retrieve all data at once. Individual search results (non-bulk) are displayed in HTML format and can then be downloaded as PDF (not machine-readable). Therefore the score is 70% and visualised as follow openness icon bar in our ranking:

Access-controlled data

Data is access-controlled if a provider regulates who, when, and how data can be accessed. Access control includes: * Registration/identification/authentication * Data request forms, data sharing agreement (stipulating use cases), * Ordering/purchasing data.   There are many reasons for establishing access controlled data including website traffic management, or to maintain control over how data is used. It is debatable whether some registration/authentication mechanisms reduce the openness of data (especially when registration is automated). Data request forms, on the other hand, are clearly not open data.   This image shows a data request form. The dataset is entirely hidden behind a “paywall”. This often prevents our research team from assessing the data at all. In this case, we could not verify in which format the data will be provided, and neither whether the data are actually weather forecast data (the particular category we look at). Therefore this access-controlled data gained 0 points and counts as 0% open. By contrast,  access-controlled data often score very high, up to 85% (because we subtract 15 out of 100 points for access-controls like registration requirements). 

How to read a score of 0%?

The are many reasons why datasets will score 0%. We tried to address the reasons in the reviewer or submitter’s comments as well. See here for the main reasons: Data gaps A data gap can mean that governments do not produce any data in a given category. Sometimes, if GODI shows a zero percent score, we see data gaps. For instance the case for Western African countries that lack air quality monitoring systems, or countries that have no established postcode system. Data gaps indicate that the government information systems are not ready to produce open data, sometimes because resources are missing, at times because it is not a priority of government.

Exist, but only to governmental use

Sometimes government has the data, but for many reasons choose not to open it to the public at all.  

Not granular

Since our criteria look for particular granularity, we considered all datasets that didn’t reach this granularity levels as not granular, and therefore they were regarded as not available. For example – Great Britain has published elections results, but not on poll station level, which is a crucial level to detect voter fraud. Therefore, while there is some data for UK elections, it is not at the right level and considered as non-existent.

 Do not fit our criteria

We are looking for particular datasets in GODI. When they don’t have all the characteristics we are looking for, we consider them as not available. For the full explanation on how to read the results see –

Introducing the 4th Global Open Data Index – Advancing the State of Open Data Through Dialogue

- May 1, 2017 in Featured, Global Open Data Index, godi, Open Government Data

We are pleased to present the 4th edition of the Global Open Data Index (GODI), a global assessment of open government data publication. GODI compares national government in 94 places across the 15 key datasets that have been assessed by our community as the most useful for solving social challenges. For this edition, we received 1410 submitted of datasets, but only 10% of these are open according to the Open Definition. At Open Knowledge International (OKI), we believe it’s important to look further than just the numbers. GODI is not just a benchmark, it can be and should be used as a tool to improve open data publication and make data findable, useful and impactful. This is why we include a new phase, the dialogue phase for this edition. Measuring the publication of open government data is a constant challenge and while open data has a fixed definition, practices for data publishing that vary widely from one government to government, and even within each government department. The GODI community has faced a number of difficult questions that make assessment harder. ‘Which government agency publishes the data?’ or ‘Is the data published on a portal or a department web page?’ are just two examples. For this edition of the GODI, we gave attention to the development of the methodology to help us address some of these challenges in the assessment process. Not only did we incorporate feedback from the past GODIs, but we engaged in systematic and detailed in a consultation with the community. In addition, we had a more extensive review phase including an expanded quality assurance stage. We believe that these changes are necessary, and makes our assessment better than ever. In past years we published GODI as a snapshot in time and didn’t make any change to it after publication. Like any assessment tool, we are not perfect, and we did find ourselves publishing an errata section to accommodate errors. This year we want to be responsive to feedback from the community and government, and give both the opportunity to debate the results. We encourage every user to contest the results for a dataset by publishing a comment about it on our forum. We will be accepting feedback for the next 30 days, and on June 2nd we will re-evaluate the index, correct errors before closing it for submissions until the next edition. What are we looking to achieve from the dialogue stage?  
  1. Feedback from the full spectrum of stakeholders including government publishers to data users. This helps to improve future editions of the index.
  2. Help government to understand where the gaps are in their publication of data.
  3. Help citizens to find the data sets they are looking for.
  4. Help publishers of data to understand the difficulties of users accessing the data
Different challenges arise from open data publication today. Over the next couple of weeks, we will be publishing more of our insights and thoughts on these difficulties. Our main findings are:
  1. GODI helps to identify “data gaps.”
  2. Open data is not easy to find, and governments frequently don’t publish on their portals but across different government websites or split across many pages on one website.
  3. There is data online, but users find it difficult to access and work with
  4. Licenses remain an issue in open data publication
You can read the more about GODI findings on the insights page Learn how to read the results, and download the data for your own research Please take a look at the new edition of GODI and challenge us by letting us know what you think on the forum!