Data Empowers Innovation: Government Data Openness and Firms' Knowledge Breadth

While the economic benefit of open data is undeniable, its use as an asset in industrial processes is still a challenge. The lack of quality is indeed a typical argument for not leveraging open data. In fact, per the Data Office of the French government (ETALAB) in charge among others of the French open data initiative, only 9 out of the 34822 open datasets are tagged as reference datasets, that is supplied by certified publishers and which content can be reliable to be shared broadly, privately and publicly. Yet, no actual quality indicators are provided along with the metadata catalog of these 9 files.What would then be the appropriate indicators for open data quality assessment, how would they differ from those used to assess DQ of traditional enterprise data? How can they be measured knowing the multiple reusability scenarios and how can they help users choose the datasets that best fit the purpose?In this work-in-progress paper, we will answer these open data quality indicators questions and illustrate it with some case studies from the industry. KeywordsOpen data catalogLinked open dataReference dataOpen data quality indicatorsOpen meta data quality indicators

In recent years, the amount of data in the world is growing rapidly. Data growth also occurs in the government sector. All ministries and institutions at every level are data producers. These government‐owned data have a high potential if they can be used properly. Open government data can stimulate innovation and economic growth and enhance business models. In order to increase the willingness of citizens to use open government data and enjoy the benefits mentioned, the quality of open government data needs to be improved. The quality of open government data encompasses a variety of dimensions and criteria. Also, the importance of each dimension and criterion in increasing the quality of open government data is different. Therefore, we are faced with a complex system that requires proper decision‐making and management. In fact, we are dealing with decision‐making in the complex management system. Given the importance of this issue, the purpose of this study is to provide a new and comprehensive method to improve the quality of open government data and increase the willingness of citizens to use the data by considering the complex network of citizens and organizations. For this purpose, library studies have been used to extract comprehensive and effective dimensions and criteria. The statistical population includes all articles related to the criteria of improving the quality of open government data and increasing the willingness of citizens to use the data. The probabilistic sampling method of simple random samples has been used, and 10 articles in this field have been reviewed. After extracting the criteria as well as the data of 112 governmental organizations and institutions related to each criterion from the open data portal, the complex network of citizens and governmental organizations and institutions has been analyzed in order to identify high‐degree centrality organizations. Then, the data characteristics of the organizations that were most desired by the citizens were extracted using data mining techniques including the regression model. Also, field method and multicriteria decision‐making technique including the DEMATEL technique have been used to express the solutions and identify the cause‐and‐effect relationships between the solutions. The criteria extracted in improving the quality of open government data and increasing the willingness of citizens to use the data are included: “data originality,” “license openness,” “up‐to‐datedness,” “data access,” “metadata completeness,” “number of data sets,” “format openness,” “nondiscrimination,” “understandable,” “number of categories of data sets,” “free,” “lack of missing data,” “data request ability,” “visualization,” “feedback,” and “data subject matter.” Based on the results obtained from the analysis of the complex network and the regression model, the criterion of “society subject” with a coefficient of 72.564 and a positive sign has the greatest impact on increasing the number of citizens' visits to open government data. After that, the criterion of “format openness” with a coefficient of 52.682 and a positive sign has the second rank in increasing the number of visits. Extracting comprehensive and effective criteria in improving the quality of open government data and increasing citizens' willingness to use data, calculating the weight and importance of each criterion by analyzing the complex network of citizens and organizations, as well as providing solutions, can help managers in decision‐making and proper management in the complex system of citizens and government organizations.

Government support for disclosing open data to end users greatly expanding as part of the transparency policy. Although numerous studies have addressed open data portals and data quality, only some have analyzed the influence of organizational resources and administrative processes on open data quality. This issue is particularly relevant because access to and use of open data is believed to support social and economic development. The article analyses data dissemination tools, resources, processes, and leadership practices that can influence data quality. The study was conducted based on secondary information from a perception survey aimed at teams responsible for processing and disseminating datasets from Brazilian federal organizations. The study results reveal that dedicated organizational structures, support resources, administrative processes, and open data dissemination instruments influence open data quality. Leadership was not found to influence data quality. These conclusions interest public decision-makers and professionals involved in data dissemination that seek to stimulate economic development due to the impact that the resources and processes used for data dissemination have on data quality.

Open government data has become an important movement for government administrations in numerous countries. Previous studies have paid less attention to the complex relationship among the historical performance gap, the readiness of government (including policy and action dimensions), and open data quality. Therefore, on the basis of performance feedback theory, a theoretical model has been developed and tested to analyze the impact of the historical performance gap on the readiness of government and open data quality. The model is empirically tested by the partial least squares method using sample data from 115 countries. The results show that the historical performance gap could indirectly contribute to a country's open data quality by promoting the readiness of government. In addition, the impact of this gap on the readiness of government and open data quality shows a U-shaped curve. In other words, a greater change in the effect of open data utilization in a country in the previous year entails a more significant promotion of readiness of government and open data quality in the next year. These findings drive governments to pay more attention to performance evaluation in open data and improve the readiness of government to improve their respective country's open data quality.

PurposeCurrently, countries worldwide are struggling with the virus COVID-19 and the severe outbreak it brings. To better benefit from open government health data in the fight against this pandemic, this study developed a framework for assessing open government health data at the dataset level, providing a tool to evaluate current open government health data's quality and usability COVID-19.Design/methodology/approachBased on the review of the existing quality evaluation methods of open government data, the evaluation metrics and their weights were determined by 15 experts in health through the Delphi method and analytic hierarchy process. The authors tested the framework's applicability using open government health data related to COVID-19 in the US, EU and China.FindingsThe results of the test capture the quality difference of the current open government health data. At present, the open government health data in the US, EU and China lacks the necessary metadata. Besides, the number, richness of content and timeliness of open datasets need to be improved.Originality/valueUnlike the existing open government data quality measurement, this study proposes a more targeted open government data quality evaluation framework that measures open government health data quality on a range of data quality dimensions with a fine-grained measurement approach. This provides a tool for accurate assessment of public health data for correct decision-making and assessment during a pandemic.

One of the goals of the ADEQUATe project is to improve the quality of the (tabular) open data being published at two Austrian open data portals by leveraging these tabular data to Linked Data, i.e., (1) classifying columns using Linked Data vocabularies, (2) linking cell values against Linked Data entities, and (3) discovering relations in the data by searching for evidences of such relations among Linked Data sources. Integrating data at Austrian data portals with existing Linked (Open) Data sources allows to, e.g., increase data completeness and reveal discrepancies in the data. In this paper, we describe lessons learned from using TableMiner+, an algorithm for (semi)automatic leveraging of tabular data to Linked Data. In particular, we evaluate TableMiner+’s ability to (1) classify columns of the tabular data and (2) link (disambiguate) cell values against Linked Data entities in Freebase. The lessons learned described in this paper are relevant not only for the goals of the ADEQUATe project, but also for other data publishers and wranglers who need to increase quality of open data by (semi)automatically interlinking them to Linked (Open) Data entities.

With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open datasets are usually evaluated based on their metadata only, while leaving the content and the adherence to the recommended data structure to the sole responsibility of the data providers. The interoperability of open datasets remains unknown. This paper therefore aims to propose a novel content-aware quality evaluation framework that assesses the quality of open datasets based on five data quality dimensions. With the proposed framework, we provide a fundamental view on the interoperability-oriented data quality of Czech open datasets, which are published in NODC. Our evaluations find that domain-specific open data quality assessments are able to detect data quality issues beyond traditional heuristics used for determining Czech open data quality, increase their interoperability, and thus increase their potential to bring value for the society. The findings of this research are beneficial not only for the case of the Czech Republic, but also can be applied in other countries that intend to enhance their open data quality evaluation processes.

Government datasets are newly available on open data platforms that are publicly accessible, available in nonproprietary formats, free of charge, and with unlimited use and distribution rights. They provide opportunities for health research, but their quality and usability are unknown. To describe available open health data, identify whether data are presented in a way that is aligned with best practices and usable for researchers, and examine differences across platforms. Two reviewers systematically reviewed a random sample of data offerings on NYC OpenData (New York City, all offerings, n = 37), Health Data NY (New York State, 25% sample, n = 71), and HealthData.gov (US Department of Health and Human Services, 5% sample, n = 75), using a standard coding guide. Three open health data platforms at the federal, New York State, and New York City levels. Data characteristics from the coding guide were aggregated into summary indices for intrinsic data quality, contextual data quality, adherence to the Dublin Core metadata standards, and the 5-star open data deployment scheme. One quarter of the offerings were structured datasets; other presentation styles included charts (14.7%), documents describing data (12.0%), maps (10.9%), and query tools (7.7%). Health Data NY had higher intrinsic data quality (P < .001), contextual data quality (P < .001), and Dublin Core metadata standards adherence (P < .001). All met basic "web availability" open data standards; fewer met higher standards of "hyperlinked to other data." Although all platforms need improvement, they already provide readily available data for health research. Sustained effort on improving open data websites and metadata is necessary for ensuring researchers use these data, thereby increasing their research value.

This study aims to shed light on determinant factors for the introduction of Government as a Platform (GaaP) by public officers who can be platform providers or main stakeholders in GaaP. It focuses on public officers in 261 Korean public agencies. In order to verify the research model’s hypotheses, we take advantage of Structural Equation Modeling and the Technology Acceptance Model. As a first result, concerning the direct effect on GaaP, the quality of open data, the scope of the stakeholders, and attitudes to civic engagement have a positive relationship on the perceived usefulness of Intelligent Information Technology (IIT). Second, in terms of the effect of IIT on GaaP, the perceived usefulness of IIT mediates positively on the intention to adopt GaaP based on the quality of open data, the scope of the stakeholders, and attitudes towards civic engagement. Based on these results, policy implications can be described as follows. Besides managing internal open data, governments should actively mine new open data that can create added value for innovation. They need to create an easily cooperative environment with other stakeholders, especially non-public participants, and governments should encourage public officers to more actively accept and utilize IIT in their jobs.

Nowadays open data is entering the mainstream - it is free available for every stakeholder and is often used in business decision-making. It is important to be sure data is trustable and error-free as its quality problems can lead to huge losses. The research discusses how (open) data quality could be assessed. It also covers main points which should be considered developing a data quality management solution. One specific approach is applied to several Latvian open data sets. The research provides a step-by-step open data sets analysis guide and summarizes its results. It is also shown there could exist differences in data quality depending on data supplier (centralized and decentralized data releases) and, unfortunately, trustable data supplier cannot guarantee data quality problems absence. There are also underlined common data quality problems detected not only in Latvian open data but also in open data of 3 European countries.

Against the backdrop of rapid digital technology advancement, Open Government Data (OGD) provides abundant data resources for enterprise innovation. This study treats the launch of OGD platforms across Chinese cities as a policy shock, employing a multi-period difference-in-differences (DID) model to examine the impact of OGD on enterprise innovation. It was found that the OGD has a promoting effect on enterprise innovation, and this is mainly achieved through reducing information asymmetry of enterprises, expanding enterprise resource acquisition, and improving the operational efficiency of enterprises. Heterogeneity analysis indicates that the innovation levels of non-state-owned enterprises, those located within the five major urban agglomerations, enterprises in capital- and technology-intensive industries, and those in the stage of regional economic downturn are more susceptible to the impact of OGD. Further analysis reveals that the quality of open data and the strength of policy safeguards are critical determinants of the magnitude of improvement in enterprise innovation levels. This research has strong theoretical implications for promoting the formation of an efficient operation mechanism for data openness and sharing and also provides beneficial ideas for releasing the value of data elements and promoting enterprise innovation.

Commercial reuse of open government data in value added services has gained a lot of interest both as practice and as a research topic over the last few years. However, utilizing open data without proper understanding of potential quality issues carries the risk of undermining the value of the service that relies on public sector information. Instead of establishing a data quality assessment framework this research considers a review of typical open data quality issues and intends to connect them to the causes leading to these various data problems. Open data specific problems are concluded from a case study and then theoretical and empirical arguments are used to connect them to root causes emerging from the peculiarities of the public sector data management process. This way both practitioners could be more conscious about appropriate cleansing methods and participants shaping the data management process could aim at eliminating root causes of data quality issues.

Big Data analysis often relies on open data, integrating it with large private data sets, using it as ground truth information, or providing it as part of the input to large simulations. Data can be released openly by governments to achieve various objectives: transparency, informing citizen engagement, or supporting private enterprise, to name a few. To the latter objective, Big Data analytics algorithms rely on high-quality, timely access to various data sources, including open data. Examples include retail analytics drawing on open demographic data and weather forecast systems drawing on open weather and climate data. In this paper, we describe the rise of post-truth in society, and the risks this poses to the quality, integrity, and authenticity of open data. We also discuss approaches to identifying, assessing, and mitigating these risks, and suggest future steps to manage this data quality concern.

In recent years, the amount of open data from governments and academic communities have increased rapidly. Open data are expected to promote the transparency and accountability of governments and academic communities, enable public participation, and facilitate digital innovation. However, open data are still facing problems such as unsatisfactory quality, insufficient data governance, increasing preservation cost and ineffective data reuse at present. This panel will invite experts in related fields to discuss the quality, reuse, and governance of open data, and propose feasible solutions from an international perspective. We will verify and promote the action plan in practice, and have more academic discussion with ASIS&amp;T and relevant academic communities.

Since the early days, cadastral data represent the geographic extent of the past, current, and future rights and interests in real property. Thus, these data are extremely important for the proper development of statehood and society. The article analyses the process of formation of the real estate cadastre in two countries – Poland and Ukraine – from the time of its establishment to the present. Both countries were in the past (and Ukraine still is) victims of various historical events that deprived them of statehood, stability and opportunities for development; therefore, it affected the development of the cadastre and its accessibility to all stakeholders. The authors attempt to answer the question of if and how geographic location and historical conditions can affect the quality and availability of open cadastral data (OCD). After all, our perception of reality is a direct result of processes originating in history. This paper contains comparisons between a post-Soviet (Ukraine) and a post-communist country (Poland) and their path from early cadastral maps to OCD.