Open Data in the Enterprise Context: Assessing Open Corporate Data’s Readiness for Use

Krasikov, Pavel; Obrecht, Timo; Legner, Christine; Eurich, Markus

doi:10.1007/978-3-030-83014-4_4

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…In addition, while we have discussed the research data as a domain of our interest, the issue of duplicates is evident in all kinds of databases, i.e., in almost every Information System and Database. Recent studies by Krasikov et al [35] and Nikiforova et al [36] also pointed to the issue of duplicates in open data. Thus, one potential direction could be the application of our framework to other data.…”

Section: Discussion and Future Workmentioning

confidence: 99%

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A Record Linkage-Based Data Deduplication Framework with DataCleaner Extension

Azeroual

Jha

Nikiforova

et al. 2022

MTI

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The data management process is characterised by a set of tasks where data quality management (DQM) is one of the core components. Data quality, however, is a multidimensional concept, where the nature of the data quality issues is very diverse. One of the most widely anticipated data quality challenges, which becomes particularly vital when data come from multiple data sources which is a typical situation in the current data-driven world, is duplicates or non-uniqueness. Even more, duplicates were recognised to be one of the key domain-specific data quality dimensions in the context of the Internet of Things (IoT) application domains, where smart grids and health dominate most. Duplicate data lead to inaccurate analyses, leading to wrong decisions, negatively affect data-driven and/or data processing activities such as the development of models, forecasts, simulations, have a negative impact on customer service, risk and crisis management, service personalisation in terms of both their accuracy and trustworthiness, decrease user adoption and satisfaction, etc. The process of determination and elimination of duplicates is known as deduplication, while the process of finding duplicates in one or more databases that refer to the same entities is known as Record Linkage. To find the duplicates, the data sets are compared with each other using similarity functions that are usually used to compare two input strings to find similarities between them, which requires quadratic time complexity. To defuse the quadratic complexity of the problem, especially in large data sources, record linkage methods, such as blocking and sorted neighbourhood, are used. In this paper, we propose a six-step record linkage deduplication framework. The operation of the framework is demonstrated on a simplified example of research data artifacts, such as publications, research projects and others of the real-world research institution representing Research Information Systems (RIS) domain. To make the proposed framework usable we integrated it into a tool that is already used in practice, by developing a prototype of an extension for the well-known DataCleaner. The framework detects and visualises duplicates thereby identifying and providing the user with identified redundancies in a user-friendly manner allowing their further elimination. By removing the redundancies, the quality of the data is improved therefore improving analyses and decision-making. This study makes a call for other researchers to take a step towards the “golden record” that can be achieved when all data quality issues are recognised and resolved, thus moving towards absolute data quality.

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Section: Discussion and Future Workmentioning

confidence: 99%

“…In addition to outdated and incomplete data, duplicates are one of the three most common symptoms of poor data quality as recognised both in scientific literature and in observations of the practitioners [35,36]. The quality of data is not pure IT, but rather a data management task.…”

Section: Discussion and Future Workmentioning

confidence: 99%

A Record Linkage-Based Data Deduplication Framework with DataCleaner Extension

Azeroual

Jha

Nikiforova

et al. 2022

MTI

View full text Add to dashboard Cite

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Stakeholder-centred Identification of Data Quality Issues: Knowledge that Can Save Your Business

Nikiforova

Kozmina

2021

2021 Second International Conference on Intelligent Data Science Technologies and Applications (IDSTA)

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A Method to Screen, Assess, and Prepare Open Data for Use

Krasikov

Legner

2023

J. Data and Information Quality

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Open data's value-creating capabilities and innovation potential are widely recognized, resulting in a notable increase in the number of published open data sources. A crucial challenge for companies intending to leverage open data is to identify suitable open datasets that support specific business scenarios and prepare these datasets for use. Researchers have developed several open data assessment techniques, but those are restricted in scope, do not consider the use context, and are not embedded in the complete set of activities required for open data consumption in enterprises. Therefore, our research aims to develop prescriptive knowledge in the form of a meaningful method to screen, assess, and prepare open data for use in an enterprise setting. Our findings complement existing open data assessment techniques by providing methodological guidance to prepare open data of uncertain quality for use in a value-adding and demand-oriented manner, enabled by knowledge graphs and linked data concepts. From an academic perspective, our research conceptualizes open data preparation as a purposeful and value-creating process.

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Open Data in the Enterprise Context: Assessing Open Corporate Data’s Readiness for Use

Cited by 3 publications

References 24 publications

A Record Linkage-Based Data Deduplication Framework with DataCleaner Extension

A Record Linkage-Based Data Deduplication Framework with DataCleaner Extension

Stakeholder-centred Identification of Data Quality Issues: Knowledge that Can Save Your Business

A Method to Screen, Assess, and Prepare Open Data for Use

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