Datenqualität Erfolgreich Steuern

Apel, Detlef; Behme, Wolfgang; Eberlein, Rüdiger; Merighi, Christian

doi:10.3139/9783446426535

Cited by 12 publications

(6 citation statements)

References 1 publication

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“…Assessment is the “evaluation or estimation of the nature, ability, or quality of something” and extends the concept of measurement by evaluating the measurement results and drawing a conclusion about the object of assessment (McKean, 2005 ; Sebastian-Coleman, 2013 ). DQ assessment is also described as the detection and initial estimation of data quality as well as the impact analysis of occurring DQ problems (English, 1999 ; Apel et al, 2015 ). In this survey, we use the term measurement since the focus is on measurement capabilities of DQ tools, independently of the interpretation of the results by a user.…”

Section: Theoretical Background On Data Qualitymentioning

confidence: 99%

“…While the creation and application of business rules (DQM-36 and DQM-38) is supported by most DQ tools, few tools also offer predefined generally applicable business rules. A widely supported example are rules for address validation (e.g., zip codes, cities, states) that tackle the prevalent problem of failed mail deliveries due to incorrect addresses, also described by Apel et al ( 2015 ). Despite the good performance of DataCleaner in terms of data profiling and CDQM, it does not support business rules at all.…”

Section: Data Quality Tool Evaluationmentioning

confidence: 99%

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A Survey of Data Quality Measurement and Monitoring Tools

Ehrlinger

Wöß

2022

Front. Big Data

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High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of data quality issues and different generally applicable data quality dimensions and metrics have been discussed. In this work, we close the gap between data quality research and practical implementations with a detailed investigation on how data quality measurement and monitoring concepts are implemented in state-of-the-art tools. For the first time and in contrast to all existing data quality tool surveys, we conducted a systematic search, in which we identified 667 software tools dedicated to “data quality.” To evaluate the tools, we compiled a requirements catalog with three functionality areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) automated data quality monitoring. Using a set of predefined exclusion criteria, we selected 13 tools (8 commercial and 5 open-source tools) that provide the investigated features and are not limited to a specific domain for detailed investigation. On the one hand, this survey allows a critical discussion of concepts that are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable data quality metrics. On the other hand, it reveals potential for functional enhancement of data quality tools and supports practitioners in the selection of appropriate tools for a given use case.

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Section: Theoretical Background On Data Qualitymentioning

confidence: 99%

Section: Data Quality Tool Evaluationmentioning

confidence: 99%

A Survey of Data Quality Measurement and Monitoring Tools

Ehrlinger

Wöß

2022

Front. Big Data

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“…This suitability can change over time as needs change" [22]. This definition makes it clear that the quality of data depends on the point in time at which it is viewed and on the level of demand placed on the data at that point in time [23].…”

Section: Data Quality and Its Impact On Data Migrationmentioning

confidence: 99%

Without Data Quality, There Is No Data Migration

Azeroual

Jha

2021

BDCC

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Data migration is required to run data-intensive applications. Legacy data storage systems are not capable of accommodating the changing nature of data. In many companies, data migration projects fail because their importance and complexity are not taken seriously enough. Data migration strategies include storage migration, database migration, application migration, and business process migration. Regardless of which migration strategy a company chooses, there should always be a stronger focus on data cleansing. On the one hand, complete, correct, and clean data not only reduce the cost, complexity, and risk of the changeover, it also means a good basis for quick and strategic company decisions and is therefore an essential basis for today’s dynamic business processes. Data quality is an important issue for companies looking for data migration these days and should not be overlooked. In order to determine the relationship between data quality and data migration, an empirical study with 25 large German and Swiss companies was carried out to find out the importance of data quality in companies for data migration. In this paper, we present our findings regarding how data quality plays an important role in a data migration plans and must not be ignored. Without acceptable data quality, data migration is impossible.

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“…Data quality is defined here as "fitness for use" or "fit for purpose" and refers to the degree of fulfillment of the totality of requirements for the data needed for a particular purpose [13]. A variety of definitions of this term can be found, e.g., in [14][15][16][17][18]. For the management of data quality in RIS, the question arises as to which concept of quality management the scientific institutions can employ in order to ensure a high level of quality in RIS.…”

Section: Concept For Managing Data Quality In Rismentioning

confidence: 99%

Influence of Information Quality via Implemented German RCD Standard in Research Information Systems

Azeroual

Schöpfel

Ivanović

2020

Data

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With the steady increase in the number of data sources to be stored and processed by higher education and research institutions, it has become necessary to develop Research Information Systems, which will store this research information in the long term and make it accessible for further use, such as reporting and evaluation processes, institutional decision making and the presentation of research performance. In order to retain control while integrating research information from heterogeneous internal and external data sources and disparate interfaces into RIS and to maximize the benefits of the research information, ensuring data quality in RIS is critical. To facilitate a common understanding of the research information collected and to harmonize data collection processes, various standardization initiatives have emerged in recent decades. These standards support the use of research information in RIS and enable compatibility and interoperability between different information systems. This paper examines the process of securing data quality in RIS and the impact of research information standards on data quality in RIS. We focus on the recently developed German Research Core Dataset standard as a case of application.

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Datenqualität Erfolgreich Steuern

Cited by 12 publications

References 1 publication

A Survey of Data Quality Measurement and Monitoring Tools

A Survey of Data Quality Measurement and Monitoring Tools

Without Data Quality, There Is No Data Migration

Influence of Information Quality via Implemented German RCD Standard in Research Information Systems

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