The following post is an excerpt from my thesis entitled Linked open data for public sector information.
Open data creates opportunities that may end up being missed if the challenges associated with them are left unaddressed. The previous blog posts raised some of the questions the open data “movement” would have to face and resolve in order not to lose these opportunities and restore the faith in the transformative potential of open data.
Open data agenda is biased by its prevailing focus on the supply side of open data and its negligence of the demand side that gets to use the data. A significant part of the challenges associated with open data stems from a narrow-minded view of open data as a technology-triggered change that might be engineered. Although open data brings a change in which technology plays a fundamental role, it is important not to fail to recognize its side effects and the issues that cannot be solved by better engineering.
It is comfortable to abstract away from these issues at hand. So far, the challenges of open data are in most cases temporarily bypassed. While the essential features of open data are described thoroughly, its impact is left mostly unexplored. In fact, open data advocates frequently substitute their expectations for the effects of this relatively new phenomenon. The full implications of open data still need to be worked out. The blog posts about the challenges associated with open data can be thus read as an outline of some of the areas in which further research may be conducted and case studies may be commissioned.
2012-09-12
2012-09-11
Challenges of open data: procured data
The following post is an excerpt from my thesis entitled Linked open data for public sector information.
The public sector is not only considered to be unable to deliver applications in a cost-efficient way, it may also lack the abilities to collect some data. There are several kinds of data, including geospatial surveys, that are difficult to gather using the means available in the public sector. The solution that public bodies adopt for such cases is to outsource data collection to private companies. Using the standard procedures of public procurement, the public bodies contract a provider to produce the requested data.
The challenge starts to appear when commercial data suppliers recognize the value of the procured data and become aware of the possibilities for reuse of such data that might generate revenue for them. Hence the suppliers offer the data under the terms of licences that prevent public sector bodies to share the data with the public, since releasing the data as open data would hamper the suppliers’ prospects to resell it. Should the public sector require a licence that allows to open the procured data, it would markedly increase the contract price.
Privatisation of collection of public sector data might be a way to achieve a better efficiency [1], yet without a significant investment it prohibits releasing the data as open data. It leaves open the question asking if public sector bodies should buy in expensive data to share it with others or if the infrastructure of the public sector should be enhanced to cater for acquisition of data that would be difficult to collect without such improvements.
Note: The topic of public sector data obtained through public procurement is the subject of a previous blog post.
The public sector is not only considered to be unable to deliver applications in a cost-efficient way, it may also lack the abilities to collect some data. There are several kinds of data, including geospatial surveys, that are difficult to gather using the means available in the public sector. The solution that public bodies adopt for such cases is to outsource data collection to private companies. Using the standard procedures of public procurement, the public bodies contract a provider to produce the requested data.
The challenge starts to appear when commercial data suppliers recognize the value of the procured data and become aware of the possibilities for reuse of such data that might generate revenue for them. Hence the suppliers offer the data under the terms of licences that prevent public sector bodies to share the data with the public, since releasing the data as open data would hamper the suppliers’ prospects to resell it. Should the public sector require a licence that allows to open the procured data, it would markedly increase the contract price.
Privatisation of collection of public sector data might be a way to achieve a better efficiency [1], yet without a significant investment it prohibits releasing the data as open data. It leaves open the question asking if public sector bodies should buy in expensive data to share it with others or if the infrastructure of the public sector should be enhanced to cater for acquisition of data that would be difficult to collect without such improvements.
Note: The topic of public sector data obtained through public procurement is the subject of a previous blog post.
References
- YIU, Chris. A right to data: fulfilling the promise of open public data in the UK [online]. Research note. March 6th, 2012 [cit. 2012-03-06]. Available from WWW: http://www.policyexchange.org.uk/publications/category/item/a-right-to-data-fulfilling-the-promise-of-open-public-data-in-the-uk
2012-09-10
Challenges of open data: trust
The following post is an excerpt from my thesis entitled Linked open data for public sector information.
Transparency brought about by the adoption of open data affects the trust in the public sector. Current governments experience a crisis of legitimacy [1, p. 58] and lack the trust of citizens. Improved visibility of the workings of public sector bodies established by the open access to their proceedings enables to track their actions in detail and improves the trust citizens put in the bodies. Nevertheless, the release of open data may reveal many fallacies of public sector bodies, which may produce a temporary disillusion, distrust in government, and loss of interest in politics [2].
The initial assumption of most open data advocates is that the data made in the public sector may be relied on. However, the public sector data cannot be treated as neutral and uncontested resource. “Unaudited, unverfied statistics abound in government data, particularly when outside parties-local government agencies, federal lobbyists, campaign committees-collect the data and turn it over to the government” [1, p. 261]. False data may be fabricated to provide alibi for corruption behaviour. For instance, Nithya Raman draws attention to an Indian dataset on urban planning in which non-existent public toilets are present, so that the spending, that supposedly goes for the toilets’ maintenance, may be justified [3]. Another example that demonstrates how false data is contained with the public sector data is the exposure of errors in subsidies awarded by the EU Common Agricultural Policy. The data shows that the oldest recipients of these funds, coming from Sweden, were 100 years old, though both dead [4, p. 85].
In the light of such facts, it is important to acknowledge that “public confidence in the veracity of government-published information is critical to Open Government Data take-off, essential to spurring demand and use of public datasets” [5]. If the data is regarded as manipulated instead of being recognized as trustworthy, the impact of open data will be significantly diminished.
Transparency brought about by the adoption of open data affects the trust in the public sector. Current governments experience a crisis of legitimacy [1, p. 58] and lack the trust of citizens. Improved visibility of the workings of public sector bodies established by the open access to their proceedings enables to track their actions in detail and improves the trust citizens put in the bodies. Nevertheless, the release of open data may reveal many fallacies of public sector bodies, which may produce a temporary disillusion, distrust in government, and loss of interest in politics [2].
The initial assumption of most open data advocates is that the data made in the public sector may be relied on. However, the public sector data cannot be treated as neutral and uncontested resource. “Unaudited, unverfied statistics abound in government data, particularly when outside parties-local government agencies, federal lobbyists, campaign committees-collect the data and turn it over to the government” [1, p. 261]. False data may be fabricated to provide alibi for corruption behaviour. For instance, Nithya Raman draws attention to an Indian dataset on urban planning in which non-existent public toilets are present, so that the spending, that supposedly goes for the toilets’ maintenance, may be justified [3]. Another example that demonstrates how false data is contained with the public sector data is the exposure of errors in subsidies awarded by the EU Common Agricultural Policy. The data shows that the oldest recipients of these funds, coming from Sweden, were 100 years old, though both dead [4, p. 85].
In the light of such facts, it is important to acknowledge that “public confidence in the veracity of government-published information is critical to Open Government Data take-off, essential to spurring demand and use of public datasets” [5]. If the data is regarded as manipulated instead of being recognized as trustworthy, the impact of open data will be significantly diminished.
References
- LATHROP, Daniel; RUMA, Laurel (eds.). Open government: collaboration, transparency, and participation in practice. Sebastopol: O'Reilly, 2010. ISBN 978-0-596-80435-0.
- FIORETTI, Marco. Open data, open society: a research project about openness of public data in EU local administration [online]. Pisa, 2010 [cit. 2012-03-10]. Available from WWW: http://stop.zona-m.net/2011/01/the-open-data-open-society-report-2/
- RAMAN, Nithya V. Collecting data in Chennai city and the limits of openness. Journal of Community Informatics [online]. 2012 [cit. 2012-04-12], vol. 8, no. 2. Available from WWW: http://ci-journal.net/index.php/ciej/article/view/877/908. ISSN 1712-4441.
- Beyond access: open government data & the right to (re)use public information [online]. Access Info Europe, Open Knowledge Foundation, January 7th, 2011 [cit. 2012-04-15]. Available from WWW: http://www.access-info.org/documents/Access_Docs/Advancing/Beyond_Access_7_January_2011_web.pdf
- GIGLER, Bjorn-Soren; CUSTER, Samantha; RAHEMTULLA, Hanif. Realizing the vision of open government data: opportunities, challenges and pitfalls [online]. World Bank, 2011 [cit. 2012-04-11]. Available from WWW: http://www.scribd.com/WorldBankPublications/d/75642397-Realizing-the-Vision-of-Open-Government-Data-Long-Version-Opportunities-Challenges-and-Pitfalls
2012-09-09
Challenges of open data: data quality
The following post is an excerpt from my thesis entitled Linked open data for public sector information.
Data quality is required for data that may be depended upon. Yet public sector data may be mired in errors and suffer from unintentional omissions that may markedly decrease usability of data. For example, Michael Daconta [1] identified 10 common types of mistakes in datasets in the U.S. data portal Data.gov.
Data quality is required for data that may be depended upon. Yet public sector data may be mired in errors and suffer from unintentional omissions that may markedly decrease usability of data. For example, Michael Daconta [1] identified 10 common types of mistakes in datasets in the U.S. data portal Data.gov.
- Ommission errors violating data completeness, missing metadata definitions, using code without providing code lists
- Formatting errors violating data consistency, syntax errors not fulfilling requirements of the employed data formats’ specifications
- Accuracy errors violating correctness, errors breaking range limitations
- Incorrectly labelled records violating correctness, for example, some datasets misnamed as CSV even though they are just dumps from Excel files that do not meet the standards established in the specification the CSV data format
- Access errors referring to incorrect metadata descriptions, for example, not linking to the content described by the link’s label
- Poorly structured data caused by improper selection of data format, using formats that are innapropriate for the expected uses of data
- Non-normalized data violating the principle of normalization, which attempt to reduce redundant data by, e.g., removing duplicates
- Raw database dumps violating relevance and providing raw database dumps that are hard to interpret and use correctly
- Inflation of counts that is a metadata quality issue having an adverse impact on usability, for instance, when datasets pertaining to the same phenomena are not properly grouped and thus difficult to find
- Inconsistent data granularity violating expected quality of metadata, such that datasets use widely varying levels of data granularity without their explicit specification
References
- DACONTA, Michael. 10 flaws with the data on Data.gov. Federal Computer Week [online]. March 11th, 2010 [cit. 2012-04-10]. Available from WWW: http://fcw.com/articles/2010/03/11/reality-check-10-data-gov-shortcomings.aspx
- Beyond access: open government data & the right to (re)use public information [online]. Access Info Europe, Open Knowledge Foundation, January 7th, 2011 [cit. 2012-04-15]. Available from WWW: http://www.access-info.org/documents/Access_Docs/Advancing/Beyond_Access_7_January_2011_web.pdf
2012-09-08
Challenges of open data: privacy
The following post is an excerpt from my thesis entitled Linked open data for public sector information.
In the pursuit of the public task public sector bodies collect personal data as well. Such data does not fall under the scope of open data. Principles of open data explicitly exclude personal data from being released and suppose it to be left closed in well-secured databases.
A complaint that is heard with regard to privacy is that the public sector collects more personal information than the minimum it needs. An example where public data collection posed a potential privacy breach comes from Finland [1]. A Finnish travel system logged all instances when a travel card was scanned by reader machine on different public transport lines. Since travel cards can be traced to individual persons, in this arrangement the travel system had location data for a large number of people, which was perceived as a violation of privacy. Ultimately, based on the data protection legislation, the travel card data was ceased to be collected.
However, in most cases personal data is not collected at an excessive rate and is governed by an access regime that is strictly limited to authorized users from the public sector to prevent accidental leaks of private data. In line with this observation, Marco Fioretti notes that privacy issues of open data have almost always been a non-issue [2].
Nonetheless, a new privacy risk is being recognized in the danger of statistical re-identification. This privacy threat is inflicted by the availability of large amounts of machine-readable data, that contains indirect personal identifiers, and the technologies allowing to combine it.
So far, privacy was guaranteed by the “practical obscurity” [3, p. 867]. It existed chiefly due to the difficulty of obtaining and combining data. In many cases, personal data was not logged down at all. Under such conditions, the right to privacy was akin to the right to be forgotten [2]. However, this assumption loses ground when confronted with the ever-increasing amount of data that is currently being recorded and stored.
Data anonymization that is based on removal of direct identifiers, such as identity card numbers, is insufficient on its own. A subject may be identified and linked to sensitive information through a combination of indirect identifiers [3, p. 8]. Indirect identifier is a data item that narrows down the set of persons who might be described by the data. An example of an indirect identifier that works this way is gender. When enough indirect identifiers are combined, they may narrow down the set of subjects they might identify to a single person.
There are established techniques for protecting personal privacy in data by limiting the risks of re-identification by statistical methods. Chris Yiu lists several of them, most of which have adverse impact on data quality and openness [5, p. 26].
In the pursuit of the public task public sector bodies collect personal data as well. Such data does not fall under the scope of open data. Principles of open data explicitly exclude personal data from being released and suppose it to be left closed in well-secured databases.
A complaint that is heard with regard to privacy is that the public sector collects more personal information than the minimum it needs. An example where public data collection posed a potential privacy breach comes from Finland [1]. A Finnish travel system logged all instances when a travel card was scanned by reader machine on different public transport lines. Since travel cards can be traced to individual persons, in this arrangement the travel system had location data for a large number of people, which was perceived as a violation of privacy. Ultimately, based on the data protection legislation, the travel card data was ceased to be collected.
However, in most cases personal data is not collected at an excessive rate and is governed by an access regime that is strictly limited to authorized users from the public sector to prevent accidental leaks of private data. In line with this observation, Marco Fioretti notes that privacy issues of open data have almost always been a non-issue [2].
Nonetheless, a new privacy risk is being recognized in the danger of statistical re-identification. This privacy threat is inflicted by the availability of large amounts of machine-readable data, that contains indirect personal identifiers, and the technologies allowing to combine it.
So far, privacy was guaranteed by the “practical obscurity” [3, p. 867]. It existed chiefly due to the difficulty of obtaining and combining data. In many cases, personal data was not logged down at all. Under such conditions, the right to privacy was akin to the right to be forgotten [2]. However, this assumption loses ground when confronted with the ever-increasing amount of data that is currently being recorded and stored.
Data anonymization that is based on removal of direct identifiers, such as identity card numbers, is insufficient on its own. A subject may be identified and linked to sensitive information through a combination of indirect identifiers [3, p. 8]. Indirect identifier is a data item that narrows down the set of persons who might be described by the data. An example of an indirect identifier that works this way is gender. When enough indirect identifiers are combined, they may narrow down the set of subjects they might identify to a single person.
There are established techniques for protecting personal privacy in data by limiting the risks of re-identification by statistical methods. Chris Yiu lists several of them, most of which have adverse impact on data quality and openness [5, p. 26].
- Access and query control, e.g., filtering and limiting size of query results to samples
- Anonymisation, or deidentification, such as stripping personal information from data
- Obfuscation, that may, for example, reduce precision in data by replacing values with ranges
- Perturbation, introducing random errors into data
- Pseudonymisation, including replacing persons’ names with identifiers
References
- DIETRICH, Daniel; GRAY, Jonathan; MCNAMARA, Tim; POIKOLA, Antti; POLLOCK, Rufus; TAIT, Julian; ZIJLSTRA, Ton. The open data handbook [online]. 2010 — 2012 [cit. 2012-03-09]. Available from WWW: http://opendatahandbook.org/
- FIORETTI, Marco. Open data, open society: a research project about openness of public data in EU local administration [online]. Pisa, 2010 [cit. 2012-03-10]. Available from WWW: http://stop.zona-m.net/2011/01/the-open-data-open-society-report-2/
- SHADBOLT, Nigel; O’HARA, Kieron; SALVADORES, Manuel; ALANI, Harith. eGovernment. In DOMINGUE, John; FENSEL, Dieter; HENDLER, James A. (eds.). Handbook of semantic web technologies. Berlin: Springer, 2011, p. 849 — 910. DOI 10.1007/978-3-540-92913-0_20.
- YAKOWITZ, Jane. Tragedy of the data commons. Harvard Journal of Law & Technology. Fall 2011, vol. 25, no. 1. Also available from WWW: http://ssrn.com/abstract=1789749
- YIU, Chris. A right to data: fulfilling the promise of open public data in the UK [online]. Research note. March 6th, 2012 [cit. 2012-03-06]. Available from WWW: http://www.policyexchange.org.uk/publications/category/item/a-right-to-data-fulfilling-the-promise-of-open-public-data-in-the-uk
2012-09-07
Challenges of open data: misinterpretation
The following post is an excerpt from my thesis entitled Linked open data for public sector information.
Another argument pointing at the potential risks in disclosure of public data was presented by Lawrence Lessig in an article titled Against transparency [1], in which he draws attention to adverse effects of misinterpretation of public data. He highlights the issues that arise when monopoly on interpretation is removed and members of the public are provided with raw, uninterpreted data [2, p. 2]. Disintermediation causes decontextualization of public sector data that may lead to highly divergent interpretations of the same data [3]. Such change may be perceived as a loss of control the civil servants used to have. Instead of an “official” interpretation of open data this would potentially lead to a plurality “competing” and possibly conflicting interpretations, some of which may be driven by malicious interests.
Lessig claims, paying respect to the alleged shortening attention spans of members of the public, that it is easier to come up with an incorrect judgement based on public data than one that is based on solid understanding [1]. The ability to correctly interpret data is largely prevalent only among people with suffiecient expertise and data literacy skills. Moreover, Archon Fung and David Weil argue that the way open data is disclosed is conducive to pessimistic view of the public sector. They claim that “the systems of open government that we’re building - structures that facilitate citizens’ social and political judgments - are much more disposed to seeing the glass of government as half or even one-quarter empty, rather than mostly full” [4, p. 107]. Such conditions may also make users of data susceptible to apophenia, a phenomenon of seeing patterns that actually do not exist [5, p. 2]. In fact, Lessig writes, encountered with the vast amounts of available public data, ignorance is a rational investment of attention [1]. Without a significant time investment and data literacy skills people will usually come to shallow and premature conclusions based on their examination of public data. Unfounded conclusions may be quickly adopted and spread by the media, which may cause significant harm of reputation of public sector bodies, civil servants, or politicians, until these assertions are re-examined and proven to be false. For example, unverified oversimplifications may be yielded from public data to support political campaigns. Open data can be misused for skewed interpretations supporting political actions, casting suspicion on public image of politicians that are the target of discreditation campaigns.
Misinterpretations may increase distrust in the public sector. Thus, Lessig makes the case for disclosing a limited amounts of public data prone to misinterpretation [Ibid.]. Even though, he is not completely opposing the transparency initiatives, he warns that careful considerations should be given when releasing sensitive information that may be misused for defamation.
Unrestricted access to communication channels provided by new media gives strong voice to all competing interpretations, unhindered by the filtering mechanisms of traditional publishing. This state of affairs results in unfounded claims and rumours to amplify and spread with an impact that was previously impossible to achieve, causing harm to personal reputations and the public image of government. Fortunately, the self-repairing properties of communication networks eventually lead to the rebuttal of misinformation. The openness of public data thus brings not only a greater control of the public sector, but indirectly also a better control of unproven claims.
Another argument pointing at the potential risks in disclosure of public data was presented by Lawrence Lessig in an article titled Against transparency [1], in which he draws attention to adverse effects of misinterpretation of public data. He highlights the issues that arise when monopoly on interpretation is removed and members of the public are provided with raw, uninterpreted data [2, p. 2]. Disintermediation causes decontextualization of public sector data that may lead to highly divergent interpretations of the same data [3]. Such change may be perceived as a loss of control the civil servants used to have. Instead of an “official” interpretation of open data this would potentially lead to a plurality “competing” and possibly conflicting interpretations, some of which may be driven by malicious interests.
Lessig claims, paying respect to the alleged shortening attention spans of members of the public, that it is easier to come up with an incorrect judgement based on public data than one that is based on solid understanding [1]. The ability to correctly interpret data is largely prevalent only among people with suffiecient expertise and data literacy skills. Moreover, Archon Fung and David Weil argue that the way open data is disclosed is conducive to pessimistic view of the public sector. They claim that “the systems of open government that we’re building - structures that facilitate citizens’ social and political judgments - are much more disposed to seeing the glass of government as half or even one-quarter empty, rather than mostly full” [4, p. 107]. Such conditions may also make users of data susceptible to apophenia, a phenomenon of seeing patterns that actually do not exist [5, p. 2]. In fact, Lessig writes, encountered with the vast amounts of available public data, ignorance is a rational investment of attention [1]. Without a significant time investment and data literacy skills people will usually come to shallow and premature conclusions based on their examination of public data. Unfounded conclusions may be quickly adopted and spread by the media, which may cause significant harm of reputation of public sector bodies, civil servants, or politicians, until these assertions are re-examined and proven to be false. For example, unverified oversimplifications may be yielded from public data to support political campaigns. Open data can be misused for skewed interpretations supporting political actions, casting suspicion on public image of politicians that are the target of discreditation campaigns.
Misinterpretations may increase distrust in the public sector. Thus, Lessig makes the case for disclosing a limited amounts of public data prone to misinterpretation [Ibid.]. Even though, he is not completely opposing the transparency initiatives, he warns that careful considerations should be given when releasing sensitive information that may be misused for defamation.
Unrestricted access to communication channels provided by new media gives strong voice to all competing interpretations, unhindered by the filtering mechanisms of traditional publishing. This state of affairs results in unfounded claims and rumours to amplify and spread with an impact that was previously impossible to achieve, causing harm to personal reputations and the public image of government. Fortunately, the self-repairing properties of communication networks eventually lead to the rebuttal of misinformation. The openness of public data thus brings not only a greater control of the public sector, but indirectly also a better control of unproven claims.
References
- LESSIG, Lawrence. Against transparency: the perils of openness in government. The New Republic [online]. October 9th, 2009 [cit. 2012-03-29]. Available from WWW: http://www.tnr.com/article/books-and-arts/against-transparency
- DAVIES, Tim. Open data, democracy and public sector reform: a look at open government data use from data.gov.uk [online]. Based on an MSc Dissertation submitted for examination in Social Science of the Internet, University of Oxford. August 2010 [cit. 2012-03-09]. Available from WWW: http://www.opendataimpacts.net/report/wp-content/uploads/2010/08/How-is-open-government-data-being-used-in-practice.pdf
- KAPLAN, Daniel. Open public data: then what? Part 1 [online]. January 28th, 2011 [cit. 2012-04-10]. Available from WWW: http://blog.okfn.org/2011/01/28/open-public-data-then-what-part-1/
- LATHROP, Daniel; RUMA, Laurel (eds.). Open government: collaboration, transparency, and participation in practice. Sebastopol: O'Reilly, 2010. ISBN 978-0-596-80435-0.
- BOYD, Danah; CRAWFORD, Kate. Six provocations for big data. In Proceedings of A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society, 21 — 24 September 2011, University of Oxford. Oxford (UK): Oxford University, 2011. Also available from WWW: http://ssrn.com/abstract=1926431
2012-09-06
Challenges of open data: data literacy
The following post is an excerpt from my thesis entitled Linked open data for public sector information.
Even though open data bridges the data divide between the public sector and members of the public, it might be introducing a new data divide that separates those with resources to make use of the data and those who do not. Despite the fact that open data virtually eliminates the cost of data acquisition, the cost of use remains “sufficiently high to compromise the political impact of open data” [1, p. 11].
An oft-cited quote attributed to Francis Bacon claims that “knowledge is power”. If data is a source of knowledge, then opening it up creates a shift in access to a source of power. However, equal access to data does not imply equal use, nor equal empowerment, as transforming data into power requires not only access. Letting aside the concerns of unequal access addressed by the agenda of the digital divide, while the principles of open data lead to the removal of barriers to access, they do not remove all barriers to use. In this respect, it is vitally important to distinguish between the “opportunity” and the actual “realization” of use of open data [2]. Even though everyone may have equal opportunities to access and use open data, only someone is able to achieve “effective use” [Ibid.]. In the light of this assertion, open data empowers only the already empowered; those that have access to technologies and computer skills that are necessary to make use of the data.
The belief in transformative potential of open data is based on optimistic assumptions about the citizens’ data literacy. The technocratic perspective with which open data principles are drafted takes high level of skills necessary for working with data for granted. Thus, the open data initiatives are in a way exclusive as they are limited mostly to technically inclined citizens [3, p. 268].
The minimalist role of the public sector, withdrawn into the background to serve as a platform, proceeds of the supposition that members of the society have all the necessary ingredients to make effective use of open government data, such as high level of information processing capabilities [4]. Even though ICT penetration and internet connectivity may be sufficient to access open data, it is not enough to make use of it. What is also needed are the abilities to process and interpret the data. However, open data released in a raw form may not be easily digestible without a substantial proficiency in data processing. Therefore, it should not be underestimated that users are required to possess technical expertise to process the data.
The bottom line is that access to data may in fact increase the asymmetry in society. If all interest groups have equal access to public sector information, then we can expect that the better organized and well-equipped groups to make better use of it [5]. The asymmetry may stem from the fact, that the interest groups that are able to take advantage of the newly released information will prosper at the expense of groups that cannot do that.
On the other hand, this type of unequality is in a sense natural. Such state of affairs should not be considered as a final one, but rather as a starting point. David Eaves compares the challenge of increasing data literacy to increasing literacy in libraries and reminds us that “we didn’t build libraries for an already literate citizenry. We built libraries to help citizens become literate” [6]. In the same way, we do not publish open data expecting everyone will be able to use it. The data are released since access is a necessary prerequisite for use. Direct access to data by the empowered, technically-skilled infomediaries may become a basis for an indirect access for many more [7]. Coming from this perspective, the most effective uses of open data can be thought of as those that let others make effective use of the data.
Even though open data bridges the data divide between the public sector and members of the public, it might be introducing a new data divide that separates those with resources to make use of the data and those who do not. Despite the fact that open data virtually eliminates the cost of data acquisition, the cost of use remains “sufficiently high to compromise the political impact of open data” [1, p. 11].
An oft-cited quote attributed to Francis Bacon claims that “knowledge is power”. If data is a source of knowledge, then opening it up creates a shift in access to a source of power. However, equal access to data does not imply equal use, nor equal empowerment, as transforming data into power requires not only access. Letting aside the concerns of unequal access addressed by the agenda of the digital divide, while the principles of open data lead to the removal of barriers to access, they do not remove all barriers to use. In this respect, it is vitally important to distinguish between the “opportunity” and the actual “realization” of use of open data [2]. Even though everyone may have equal opportunities to access and use open data, only someone is able to achieve “effective use” [Ibid.]. In the light of this assertion, open data empowers only the already empowered; those that have access to technologies and computer skills that are necessary to make use of the data.
The belief in transformative potential of open data is based on optimistic assumptions about the citizens’ data literacy. The technocratic perspective with which open data principles are drafted takes high level of skills necessary for working with data for granted. Thus, the open data initiatives are in a way exclusive as they are limited mostly to technically inclined citizens [3, p. 268].
The minimalist role of the public sector, withdrawn into the background to serve as a platform, proceeds of the supposition that members of the society have all the necessary ingredients to make effective use of open government data, such as high level of information processing capabilities [4]. Even though ICT penetration and internet connectivity may be sufficient to access open data, it is not enough to make use of it. What is also needed are the abilities to process and interpret the data. However, open data released in a raw form may not be easily digestible without a substantial proficiency in data processing. Therefore, it should not be underestimated that users are required to possess technical expertise to process the data.
The bottom line is that access to data may in fact increase the asymmetry in society. If all interest groups have equal access to public sector information, then we can expect that the better organized and well-equipped groups to make better use of it [5]. The asymmetry may stem from the fact, that the interest groups that are able to take advantage of the newly released information will prosper at the expense of groups that cannot do that.
On the other hand, this type of unequality is in a sense natural. Such state of affairs should not be considered as a final one, but rather as a starting point. David Eaves compares the challenge of increasing data literacy to increasing literacy in libraries and reminds us that “we didn’t build libraries for an already literate citizenry. We built libraries to help citizens become literate” [6]. In the same way, we do not publish open data expecting everyone will be able to use it. The data are released since access is a necessary prerequisite for use. Direct access to data by the empowered, technically-skilled infomediaries may become a basis for an indirect access for many more [7]. Coming from this perspective, the most effective uses of open data can be thought of as those that let others make effective use of the data.
References
- MCCLEAN, Tom. Not with a bang but with a whimper: the politics of accountability and open data in the UK. In HAGOPIAN, Frances; HONIG, Bonnie (eds.). American Political Science Association Annual Meeting Papers, Seattle, Washington, 1 — 4 September 2011 [online]. Washington (DC): American Political Science Association, 2011 [cit. 2012-04-19]. Also available from WWW: http://ssrn.com/abstract=1899790
- GURSTEIN, Michael. Open data: empowering the empowered or effective data use for everyone? First Monday [online]. February 7th, 2011 [cit. 2012-04-01], vol. 16, no. 2. Available from WWW: http://firstmonday.org/htb in/cgiwrap/bin/ojs/index.php/fm/article/view/3316/2764
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- GIGLER, Bjorn-Soren; CUSTER, Samantha; RAHEMTULLA, Hanif. Realizing the vision of open government data: opportunities, challenges and pitfalls [online]. World Bank, 2011 [cit. 2012-04-11]. Available from WWW: http://www.scribd.com/WorldBankPublications/d/75642397-Realizing-the-Vision-of-Open-Government-Data-Long-Version-Opportunities-Challenges-and-Pitfalls
- SHIRKY, Clay. Open House thoughts, Open Senate direction. In Open House Project [online]. November 23rd, 2008 [cit. 2012-04-19]. Available from WWW: http://groups.google.com/group/openhouseproject/msg/53867cab80ed4be9
- EAVES, David. Learning from libraries: the literacy challenge of open data [online]. June 10th, 2010 [cit. 2012-04-11]. Available from WWW: http://eaves.ca/2010/06/10/learning-from-libraries-the-literacy-challenge-of-open-data/
- TAUBERER, Joshua. Open government data: principles for a transparent government and an engaged public [online]. 2012 [cit. 2012-03-09]. Available from WWW: http://opengovdata.io/
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