Data Quality Dimensions

Data quality management has been the key aspect in information systems within the past few years. The requirement for standard data quality management protocols and frameworks are in very high demand. This is mainly because the quality of data is all that matters when organizations use the IS to make decisions. Data with lower quality will eventually provide decisions with lower quality and perfection. This paper discusses the definition of data quality along with detailed explanations about six core dimensions of data quality. Also about different frameworks involved in analyzing the data quality of a system and finally about different data characteristic trade-offs which must be taken in order to achieve optimal outputs from the information systems. These frameworks and concepts are used by data analysts in managing the data quality over years within information systems. But along with the rapid expansion of the cloud services and its involvement in business management system; especially information systems (IS) the requirement for improvements in the existing techniques and invention of new methods are in high demand.

Achieving high level of data quality is considered one of the most important assets for any small, medium and large size organizations. Data quality is the main hype for both practitioners and researchers who deal with traditional or big data. The level of data quality is measured through several quality dimensions. High percentage of the current studies focus on assessing and applying data quality on traditional data. As we are in the era of big data, the attention should be paid to the tremendous volume of generated and processed data in which 80% of all the generated data is unstructured. However, the initiatives for creating big data quality evaluation models are still under development. This paper investigates the data quality dimensions that are mostly used in both traditional and big data to figure out the metrics and techniques that are used to measure and handle each dimension. A complete definition for each traditional and big data quality dimension, metrics and handling t...

Big Data (BD) is everywhere and quite a lot of benefits have been derived from its usage by different organizations. Notwithstanding, there are still numerous technical and research challenges that must be tackled to comprehend and gain its full potential. The major challenges of BD are not just its processing, storage and analytics, there are also challenges associated with it that run across the BD value chain such as the data collection phase, integration and the enforcement of quality. This paper propose a DQ transformation model to evaluate BD quality from the data collection phase through to the visualization phase involving both data-driven and process-driven quality evaluation by assessing the quality of data itself first then assessing the process quality. This is still an ongoing research and hopefully will be experimented using specific Data Quality Dimensions (DQDs) like completeness, consistency, accuracy and timeliness with process quality dimensions such as Throughput, response time, latency with their corresponding metrics.

Data quality (DQ) has been studied in significant depth over the last two decades and has received attention from both the academic and the practitioner community. Over that period of time a large number of data quality dimensions have been identified in due course of research and practice. While it is important to embrace the diversity of views of data quality, it is equally important for the data quality research and practitioner community to be united in the consistent interpretation of this foundational concept. In this paper, we provide a step towards this consistent interpretation by providing a lens to analyse the dimensions towards developing clear and concise metrics to manage DQ. Through a systematic review of research and practitioner literature, we identify previously published data quality dimensions and embark on the analysis and consolidation of the overlapping and inconsistent definitions.

In the health industry, the use of data (including Big Data) is of growing importance. The term ‘Big Data’ characterizes data by its volume, and also by its velocity, variety, and veracity. Big Data needs to have effective data governance, which includes measures to manage and control the use of data and to enhance data quality, availability, and integrity. The type and description of data quality can be expressed in terms of the dimensions of data quality. Well-known dimensions are accuracy, completeness, and consistency, amongst others. Since data quality depends on how the data is expected to be used, the most important data quality dimensions depend on the context of use and industry needs. There is a lack of current research focusing on data quality dimensions for Big Data within the health industry; this paper, therefore, investigates the most important data quality dimensions for Big Data within this context. An inner hermeneutic cycle research approach was used to review rel...

This paper presents the research and discusses the findings concerning the identification and definition of quality dimensions related to the monitoring data obtained in the safety control of major civil engineering works, namely dams and bridges. The analysis of the behavior and the safety evaluation of these works essentially depend on the quality of the data collected by the sensors, named the observation data, besides the quality of the models and, last but not least, the knowledge and experience of the experts involved. In addition, LNEC 1 stores and preserves the observation data concerning major civil engineering works, which represents an important research source for LNEC and Universities and, thus, shapes itself as an e-Science scenario. Differently from other e-Science environment authors, we have not restricted our work to objective dimensions, whose measures can be calculated automatically. It appears, from the experts' opinions, that some subjective dimensions can largely enrich the quality information about archived data at the expense of some additional work in data curation. In the development of this research we used an online version of the Delphi method with the Q-Sort technique, complemented with upstream interviews and a downstream meeting with the experts. We found out, and sorted by the importance level assigned by the experts, ten dimensions for the quality of observation data, which are error-of-observation, coherence, relevancy, interpretability, timeliness, completeness, accessibility, appropriate amount of data, access security and preservation.

In the health industry, the use of data (including Big Data) is of growing importance. The term ‘Big Data’ characterizes data by its volume, and also by its velocity, variety, and veracity. Big Data needs to have effective data governance, which includes measures to manage and control the use of data and to enhance data quality, availability, and integrity. The type and description of data quality can be expressed in terms of the dimensions of data quality. Well-known dimensions are accuracy, completeness, and consistency, amongst others. Since data quality depends on how the data is expected to be used, the most important data quality dimensions depend on the context of use and industry needs. There is a lack of current research focusing on data quality dimensions for Big Data within the health industry; this paper, therefore, investigates the most important data quality dimensions for Big Data within this context. An inner hermeneutic cycle research approach was used to review rel...

Big Data (BD) is everywhere and quite a lot of benefits have been derived from its usage by different organizations. Notwithstanding, there are still numerous technical and research challenges that must be tackled to comprehend and gain its full potential. The major challenges of BD are not just its processing, storage and analytics, there are also challenges associated with it that run across the BD value chain such as the data collection phase, integration and the enforcement of quality. This paper propose a DQ transformation model to evaluate BD quality from the data collection phase through to the visualization phase involving both data-driven and process-driven quality evaluation by assessing the quality of data itself first then assessing the process quality. This is still an ongoing research and hopefully will be experimented using specific Data Quality Dimensions (DQDs) like completeness, consistency, accuracy and timeliness with process quality dimensions such as Throughput, response time, latency with their corresponding metrics.

In the health industry, the use of data (including Big Data) is of growing importance. The term ‘Big Data’ characterizes data by its volume, and also by its velocity, variety, and veracity. Big Data needs to have effective data governance, which includes measures to manage and control the use of data and to enhance data quality, availability, and integrity. The type and description of data quality can be expressed in terms of the dimensions of data quality. Well-known dimensions are accuracy, completeness, and consistency, amongst others. Since data quality depends on how the data is expected to be used, the most important data quality dimensions depend on the context of use and industry needs. There is a lack of current research focusing on data quality dimensions for Big Data within the health industry; this paper, therefore, investigates the most important data quality dimensions for Big Data within this context. An inner hermeneutic cycle research approach was used to review rel...

To succeed in their tasks, users need to manage da ta with the most adequate quality levels possible according to speci fic data quality models. Typically, data quality assessment consists of calc ulating a synthesizing value by means of a weighted average of values and weights associated with each data quality dimension of the data quality model. W e shall

The quality assurance of publication data in collaborative knowledge bases and in current research information systems (CRIS) becomes more and more relevant by the use of freely available spatial information in different application scenarios. When integrating this data into CRIS, it is necessary to be able to recognize and assess their quality. Only then is it possible to compile a result from the available data that fulfills its purpose for the user, namely to deliver reliable data and information. This paper discussed the quality problems of source metadata in Wikipedia and CRIS. Based on real data from over 40 million Wikipedia articles in various languages, we performed preliminary quality analysis of the metadata of scientific publications using a data quality tool. So far, no data quality measurements have been programmed with Python to assess the quality of metadata from scientific publications in Wikipedia and CRIS. With this in mind, we programmed the methods and algorithms as code, but presented it in the form of pseudocode in this paper to measure the quality related to objective data quality dimensions such as completeness, correctness, consistency, and timeliness. This was prepared as a macro service so that the users can use the measurement results with the program code to make a statement about their scientific publications metadata so that the management can rely on high-quality data when making decisions.

Data is a vital asset in virtually all types of organizations. These days data or information acquired from data analysis is the basis of decision making in various businesses or organizations in general and this offers numerous benefits by building accurate and dependable process. The degradation of its quality has erratic consequences resulting to wrong insights and decisions. Moreover, these are the days of Big Data (BD) which comes with varieties of vast amount of unprecedented data with unknown quality which makes its Data Quality (DQ) evaluation very challenging. DQ is therefore critical for the processes of data operations and management in order to detect associated performance problems. Besides, data of high quality has the ability to attain top services within an organization through enlarged prospects. Nonetheless, recognising different characteristics of DQ from its definition to the different Data Quality Dimensions (DQDs) are crucial for equipping methods and processes for the purpose of improving DQ. This paper focuses on the review of BD and the most commonly used DQDs for BD which are basis for the assessment and evaluation of the quality of BD.

Nowadays, activities and decisions making in an organization is based on data and information obtained from data analysis, which provides various services for constructing reliable and accurate process. As data are significant resources in all organizations the quality of data is critical for managers and operating processes to identify related performance issues. Moreover, high quality data can increase opportunity for achieving top services in an organization. However, identifying various aspects of data quality from definition, dimensions, types, strategies, techniques are essential to equip methods and processes for improving data. This paper focuses on systematic review of data quality dimensions in order to use at proposed framework which combining data mining and statistical techniques to measure dependencies among dimensions and illustrate how extracting knowledge can increase process quality.

Guaranteeing high data quality level is an important issue to increase the efficiency of the business processes. In fact, poor data quality produces wrong information, which leads to the failure of the business process improvement. Identifying data quality problems has positive impact on overall effectiveness and efficiency of the process improvement. In fact, improving data quality often requires modifying business process enriching them with the most improvement activities. Such activities depend and change based on the data quality dimensions. In this paper, we focus on review of the impact of data quality dimensions on business process improvement in order to support managers to facilitate the implementation of process improvement. The evaluations of this research will use to refine and extend knowledge of relationship between data quality dimensions and business process improvement.