The role of 3S in big data quality: a perspective on operational performance indicators using an integrated approach

PurposeThis study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.Design/methodology/approachA fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.FindingsThe effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.Research limitations/implicationsThe outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.Originality/valueBig data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

DATA QUALITY DIMENSIONS, METRICS, AND IMPROVEMENT TECHNIQUES

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 techniques are presented in this paper. Many data quality dimensions can be applied to both traditional and big data, while few number of quality dimensions are either applied to traditional data or big data. Few number of data quality metrics and barely handling techniques are presented in the current works.

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: A Multi-Method Study (Preprint)

BACKGROUND There is a growing interest in using person-generated wearable device data for biomedical research, but concerns in the quality of data such as missing or incorrect data exists. This emphasizes the importance of assessing data quality prior to conducting research. In order to perform data quality assessments, it is essential to define what data quality means for person-generated wearable device data by identifying data quality dimensions. OBJECTIVE The goal of this study was to identify data quality dimensions for person-generated wearable device data for research purposes. METHODS Study was conducted in three phases: (1) literature review, (2) survey, and (3) focus group discussion. Literature review was conducted following the PRISMA guideline to identify factors affecting data quality and its associated data quality challenges. In addition, a survey was conducted to confirm and complement results from the literature review, and to understand researchers’ perception on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. The survey was sent out to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. Based on the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions. RESULTS Nineteen studies were included in the literature review. Three major themes emerged: device- and technical-related, user-related, and data governance-related factors. Associated data quality problems were incomplete data, incorrect data, and heterogeneous data. Twenty respondents answered the survey. Major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that dimensions for secondary use of EHR data is applicable to person-generated wearable device data. There were three focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features such as conformance, completeness, and plausibility, and contextual/fitness-for-use data quality features such as completeness (breadth and density) and temporal data granularity are important data quality dimensions for assessing person-generated wearable device data for research purposes. CONCLUSIONS In this study, intrinsic and contextual/fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed in regards to each dimension is needed.

Importance of the Open Data Assessment: An Insight Into the (Meta) Data Quality Dimensions

Data are the most important resource of the 21st century. The open data (OD) movement provides publicly available data for the development of a knowledge-based society. As such, the concept of OD is a valuable information technology (IT) tool for economic, social, and human development, which adds value. To further develop these processes on a global scale, users need to manage the quality of OD in their practices. Otherwise, what is the point of using data just for the sake of using it (in science or practice) without thinking about data compliance with norms, standards, and so forth? This article aims to provide an overview of (meta)data quality dimensions, sub-dimensions, and metrics used within OD assessment-related research papers. To achieve this, the authors performed a systematic literature review (SLR) and extracted data from 86 relevant studies dealing with the evaluation of OD. The article endows the progress made so far in OD assessment research. Findings of reviewing the assessment of the OD in the light of existing (meta)data quality dimensions unveil the potential of metadata. Furthermore, the analysis disclosed the need for greater use of quantitative methods in research, and metadata can greatly assist in this.

Exploring big data traits and data quality dimensions for big data analytics application using partial least squares structural equation modelling

The popularity of big data analytics (BDA) has boosted the interest of organisations into exploiting their large scale data. This technology can become a strategic stimulation for organisations to achieve competitive advantage and sustainable growth. Previous BDA research, however, has focused more on introducing more traits, known as Vs for big data traits, while ignoring the quality of data when examining the application of BDA. Therefore, this study aims to explore the effect of big data traits and data quality dimensions on BDA application. This study has formulated 10 hypotheses that comprised of the relationships of big data traits, accuracy, believability, completeness, timeliness, ease of operation, and BDA application constructs. This study conducted a survey using a questionnaire as a data collection instrument. Then, the partial least squares structural equation modelling technique was used to analyse the hypothesised relationships between the constructs. The findings revealed that big data traits can significantly affect all constructs for data quality dimensions and that the ease of operation construct has a significant effect on BDA application. This study contributes to the literature by bringing new insights to the field of BDA and may serve as a guideline for future researchers and practitioners when studying BDA application.

Big Data Quality Dimensions: A Systematic Literature Review

Although big data has become an integral part of businesses and society, there is still concern about the quality aspects of big data. Past research has focused on identifying various dimensions of big data. However, the research is scattered and there is a need to synthesize the ever involving phenomenon of big data. This research aims at providing a systematic literature review of the quality dimension of big data. Based on a review of 17 articles from academic research, we have presented a set of key quality dimensions of big data.