Data Quality Framework Research Articles

Enhancing data quality to mine credible patterns

The importance of big data is widely accepted in various fields. Organisations spend a lot of money to collect, process and mine the data to identify patterns. These patterns facilitate their future decision-making process to improve the organisational performance and profitability. However, among discovered patterns, there are some meaningless and misleading patterns which restrict the effectiveness of decision-making process. The presence of data discrepancies, noise and outliers also impacts the quality of discovered patterns and leads towards missing strategic goals and objectives. Quality inception of these discovered patterns is vital before utilising them in making predictions, decision-making process or strategic planning. Mining useful and credible patterns over social media is a challenging task. Often, people spread targeted content for character assassination or defamation of brands. Recently, some studies have evaluated the credibility of information over social media based on users’ surveys, experts’ judgement and manually annotating Twitter tweets to predict credibility. Unfortunately, due to the large volume and exponential growth of data, these surveys and annotation-based information credibility techniques are not efficiently applicable. This article presents a data quality and credibility evaluation framework to determine the quality of individual data instances. This framework provides a way to discover useful and credible patterns using credibility indicators. Moreover, a new Twitter bot detection algorithm is proposed to classify tweets generated by Twitter bots and real users. The results of conducted experiments showed that the proposed model generates a positive impact on improving classification accuracy and quality of discovered patterns.

SparkDQ: Efficient generic big data quality management on distributed data-parallel computation

In the big data era, large amounts of data are under generation and accumulation in various industries. However, users usually feel hindered by the data quality issues when extracting values from the big data. Thus, data quality issues are gaining more and more attention from data quality management analysts. Cutting-edge solutions like data ETL, data cleaning, and data quality monitoring systems have many deficiencies in capability and efficiency, making it difficult to cope with complicated situations on big data. These problems inspire us to build SparkDQ, a generic distributed data quality management model and framework that provides a series of data quality detection and repair interfaces. Users can quickly build custom tasks of data quality computing for various needs by utilizing these interfaces. In addition, SparkDQ implements a set of algorithms that in a parallel manner with optimizations. These algorithms aim at various data quality goals. We also propose several system-level optimizations, including the job-level optimization with multi-task execution scheduling and the data-level optimization with data state caching. The experimental evaluation shows that the proposed distributed algorithms in SparkDQ run up to 12 times faster compared to the corresponding stand-alone serial and multi-thread algorithms. Compared with the cutting-edge distributed data quality solution Apache Griffin, SparkDQ has more features, and its execution time is only around half of Apache Griffin on average. SparkDQ achieves near-linear data and node scalability.

Framework for Integral Data Quality and Security Evaluation in Smartphones

Data quality (DQ) concept should play an important role in decision-making and engineering systems. Underestimation of DQ may lead to resource waste, wrong conclusions, or inefficient decisions. Unfortunately, current approaches to DQ incorporating into data management systems are limited to particular applications. This problem is aggravated by the DQ inequality of data sources. This is especially critical in mobile crowd-sensing applications where data may come from unverified data contributors using the smartphones and other mobile devices. To facilitate the expansion of DQ evaluation to a wider spectrum of applications, this article presents a framework for integral DQ and security evaluation in Android-based smartphones. The developed framework provides support for selecting the DQ metrics and implementing their calculus by integrating diverse sensor DQ and security metrics. We present multiple calculi for DQ and security evaluation such as hierarchical fuzzy rules expert system, neural networks, and algebraic functions. Case studies that demonstrate the framework's performance in addressing real-life tasks are presented and the achieved results are analyzed. The implementation results validate the framework's capability of performing comprehensive DQ evaluations.

PUK19 DATA Quality Considerations for Determining the IMPACT of the COVID-19 Pandemic on Outpatient Visits and Procedures in the United States

To use real-world data to better understand the temporal impact of COVID-19 on frequency of outpatient visits in urology clinics and apply a data quality framework to identify and resolve issues that could influence the validity of analyses.

Big data quality framework: a holistic approach to continuous quality management

Big Data is an essential research area for governments, institutions, and private agencies to support their analytics decisions. Big Data refers to all about data, how it is collected, processed, and analyzed to generate value-added data-driven insights and decisions. Degradation in Data Quality may result in unpredictable consequences. In this case, confidence and worthiness in the data and its source are lost. In the Big Data context, data characteristics, such as volume, multi-heterogeneous data sources, and fast data generation, increase the risk of quality degradation and require efficient mechanisms to check data worthiness. However, ensuring Big Data Quality (BDQ) is a very costly and time-consuming process, since excessive computing resources are required. Maintaining Quality through the Big Data lifecycle requires quality profiling and verification before its processing decision. A BDQ Management Framework for enhancing the pre-processing activities while strengthening data control is proposed. The proposed framework uses a new concept called Big Data Quality Profile. This concept captures quality outline, requirements, attributes, dimensions, scores, and rules. Using Big Data profiling and sampling components of the framework, a faster and efficient data quality estimation is initiated before and after an intermediate pre-processing phase. The exploratory profiling component of the framework plays an initial role in quality profiling; it uses a set of predefined quality metrics to evaluate important data quality dimensions. It generates quality rules by applying various pre-processing activities and their related functions. These rules mainly aim at the Data Quality Profile and result in quality scores for the selected quality attributes. The framework implementation and dataflow management across various quality management processes have been discussed, further some ongoing work on framework evaluation and deployment to support quality evaluation decisions conclude the paper.

Analyzing Social Media Research: A Data Quality and Research Reproducibility Perspective

Social media platforms have become very popular these days among individuals and organizations. On the one hand, organizations use social media as a potential tool to create awareness of their products among consumers, and on the other hand, social media data is useful to predict the national crisis, election polls, stock prediction, etc. However, nowadays, a debate is going on about the quality of data generated on social media platforms, whether it is relevant for prediction and generalization. The article discusses the relevance and quality of data obtained from social media in the context of research and development. Social media data quality issues may impact the generalizability and reproducibility of the results of the study. The paper explores possible reasons for quality issues in the data generated over social media platforms along with the suggestive measures to minimize them using the proposed social media data quality framework.

Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use.

IntroductionSecondary use of electronic health record (EHR) data for research requires that the data are fit for use. Data quality (DQ) frameworks have traditionally focused on structural conformance and completeness of clinical data extracted from source systems. In this paper, we propose a framework for evaluating semantic DQ that will allow researchers to evaluate fitness for use prior to analyses.MethodsWe reviewed current DQ literature, as well as experience from recent multisite network studies, and identified gaps in the literature and current practice. Derived principles were used to construct the conceptual framework with attention to both analytic fitness and informatics practice.ResultsWe developed a systematic framework that guides researchers in assessing whether a data source is fit for use for their intended study or project. It combines tools for evaluating clinical context with DQ principles, as well as factoring in the characteristics of the data source, in order to develop semantic DQ checks.ConclusionsOur framework provides a systematic process for DQ development. Further work is needed to codify practices and metadata around both structural and semantic data quality.

Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R

BackgroundNo standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.MethodsDevelopments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).ResultsThe data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website.ConclusionsThe presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.

Nearshore Monitoring With X‐Band Radar: Maximizing Utility in Dynamic and Complex Environments

AbstractCoastal management and engineering applications require data that quantify the nature and magnitude of changes in nearshore bathymetry. However, bathymetric surveys are usually infrequent due to high costs and complex logistics. This study demonstrates that ground‐based X‐band radar offers a cost‐effective means to monitor nearshore changes at relatively high frequency and over large areas. A new data quality and processing framework was developed to reduce uncertainties in the estimates of radar‐derived bathymetry and tested using data from an 18‐months installation at Thorpeness (UK). In addition to data calibration and validation, two new elements are integrated to reduce the influence of data scatter and outliers: (a) an automated selection of periods of “good data” and (b) the application of a depth‐memory stabilization. For conditions when the wave height is >1 m, the accuracy of the radar‐derived depths is shown to be ±0.5 m (95% confidence interval) at 40 × 40‐m spatial resolution. At Thorpeness, radar‐derived bathymetry changes exceeding this error were observed at time scales ranging from 3 weeks to 6 months. These data enabled quantification of changes in nearshore sediment volume at frequencies and spatial cover that would be difficult and/or expensive to obtain by other methods. It is shown that the volume of nearshore sediment movement occurring at time scale as short as few weeks are comparable with the annual longshore transport rates reported in this area. The use of radar can provide an early warning of changes in offshore bathymetry likely to impact vulnerable coastal locations.

Provoking a Cultural Shift in Data Quality.

Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.

Critical success factors to improve data quality of electronic medical records in public healthcare institutions

Background: Electronic medical record (EMR) systems have been identified as having the potential to improve healthcare and allow the healthcare sector to reap a number of benefits when implemented successfully. However, problems with the data quality of EMRs are often reported as barriers to successful implementation of these systems. Objectives: The purpose of this study was to investigate the critical success factors that can improve the data quality of EMRs in public healthcare institutions in the North West province of South Africa. Method: This study utilised data quality framework (DQF) as a lens to collect qualitative data through interviews with seven nurses and one information technology specialist chosen through purposive sampling. The interview guide was developed using the DQF. Results: The data quality of EMRs is influenced by the six data quality dimensions of the DQF, namely, completeness, consistency, conformity, accuracy, integrity and timeliness. Therefore, it is important that healthcare workers understand the five factors identified in this study in order to ensure good-quality data in EMRs. Conclusion: This study recommends that public healthcare institutions should consider using the DQF to improve the quality of patient data, which is documented in EMRs. Implementing the DQF may ensure that the healthcare workers follow the correct standards of documenting patient data, which are accurate, complete, consistent and timely, conforming to the appropriate standards and with integrity.

Quality assessment of real-world data repositories across the data life cycle: A literature review.

Data quality (DQ) must be consistently defined in context. The attributes, metadata, and context of longitudinal real-world data (RWD) have not been formalized for quality improvement across the data production and curation life cycle. We sought to complete a literature review on DQ assessment frameworks, indicators and tools for research, public health, service, and quality improvement across the data life cycle. The review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Databases from health, physical and social sciences were used: Cinahl, Embase, Scopus, ProQuest, Emcare, PsycINFO, Compendex, and Inspec. Embase was used instead of PubMed (an interface to search MEDLINE) because it includes all MeSH (Medical Subject Headings) terms used and journals in MEDLINE as well as additional unique journals and conference abstracts. A combined data life cycle and quality framework guided the search of published and gray literature for DQ frameworks, indicators, and tools. At least 2 authors independently identified articles for inclusion and extracted and categorized DQ concepts and constructs. All authors discussed findings iteratively until consensus was reached. The 120 included articles yielded concepts related to contextual (data source, custodian, and user) and technical (interoperability) factors across the data life cycle. Contextual DQ subcategories included relevance, usability, accessibility, timeliness, and trust. Well-tested computable DQ indicators and assessment tools were also found. A DQ assessment framework that covers intrinsic, technical, and contextual categories across the data life cycle enables assessment and management of RWD repositories to ensure fitness for purpose. Balancing security, privacy, and FAIR principles requires trust and reciprocity, transparent governance, and organizational cultures that value good documentation.

A Quality Assurance Reference Framework for Assessing Educational Data

Digital educational content is gaining importance as an incubator of pedagogical methodologies in formal and informal online educational settings. Its educational efficiency is directly dependent on its quality, however educational content is more than information and data. This paper presents a new data quality framework for assessing digital educational content used for teaching in distance learning environments. The model relies on the ISO2500 series quality standard and beside providing the mechanisms for multi-facet quality assessment it also supports organizations that design, create, manage and use educational content with the quality tools (expressed as quality metrics and measurement methods) to provide a more efficient distance education experience. The model describes the quality characteristics of the educational material content using data and software quality characteristics.

Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data.

ObjectiveTo synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ assessment in the national Patient-centered Clinical Research Network (PCORnet).Materials and MethodsWe started with 3 widely cited DQ literature—2 reviews from Chan et al (2010) and Weiskopf et al (2013a) and 1 DQ framework from Kahn et al (2016)—and expanded our review systematically to cover relevant articles published up to February 2020. We extracted DQ dimensions and assessment methods from these studies, mapped their relationships, and organized a synthesized summarization of existing DQ dimensions and assessment methods. We reviewed the data checks employed by the PCORnet and mapped them to the synthesized DQ dimensions and methods.ResultsWe analyzed a total of 3 reviews, 20 DQ frameworks, and 226 DQ studies and extracted 14 DQ dimensions and 10 assessment methods. We found that completeness, concordance, and correctness/accuracy were commonly assessed. Element presence, validity check, and conformance were commonly used DQ assessment methods and were the main focuses of the PCORnet data checks.DiscussionDefinitions of DQ dimensions and methods were not consistent in the literature, and the DQ assessment practice was not evenly distributed (eg, usability and ease-of-use were rarely discussed). Challenges in DQ assessments, given the complex and heterogeneous nature of real-world data, exist.ConclusionThe practice of DQ assessment is still limited in scope. Future work is warranted to generate understandable, executable, and reusable DQ measures.

An Examination of the Data Quality of Online Reviews

This research synthesizes the information systems and marketing research by considering the usefulness of online product reviews in the context of Wang and Strong's Data Quality Framework. It examines the extent to which a review's intrinsic (review anonymity and use of personal pronouns), contextual (review length, verified purchase, rating, and rating extremity), and representational quality (spelling errors, grammar errors, readability) impact the perceived usefulness of a product review. The examination of Amazon reviews for an inexpensive experience product revealed number of words, verified purchase, and grammar errors have a significant positive impact on review usefulness. Rating and number of spelling errors have a negative effect, suggesting consumers use some discernment in assessing the believably of a review. Surprisingly, the opposite effect was found for grammar errors, with more grammar errors being associated with a more useful review.

Data Quality Issues With Physician-Rating Websites: Systematic Review.

BackgroundIn recent years, online physician-rating websites have become prominent and exert considerable influence on patients’ decisions. However, the quality of these decisions depends on the quality of data that these systems collect. Thus, there is a need to examine the various data quality issues with physician-rating websites.ObjectiveThis study’s objective was to identify and categorize the data quality issues afflicting physician-rating websites by reviewing the literature on online patient-reported physician ratings and reviews.MethodsWe performed a systematic literature search in ACM Digital Library, EBSCO, Springer, PubMed, and Google Scholar. The search was limited to quantitative, qualitative, and mixed-method papers published in the English language from 2001 to 2020.ResultsA total of 423 articles were screened. From these, 49 papers describing 18 unique data quality issues afflicting physician-rating websites were included. Using a data quality framework, we classified these issues into the following four categories: intrinsic, contextual, representational, and accessible. Among the papers, 53% (26/49) reported intrinsic data quality errors, 61% (30/49) highlighted contextual data quality issues, 8% (4/49) discussed representational data quality issues, and 27% (13/49) emphasized accessibility data quality. More than half the papers discussed multiple categories of data quality issues.ConclusionsThe results from this review demonstrate the presence of a range of data quality issues. While intrinsic and contextual factors have been well-researched, accessibility and representational issues warrant more attention from researchers, as well as practitioners. In particular, representational factors, such as the impact of inline advertisements and the positioning of positive reviews on the first few pages, are usually deliberate and result from the business model of physician-rating websites. The impact of these factors on data quality has not been addressed adequately and requires further investigation.

On Two Existing Approaches to Statistical Analysis of Social Media Data

SummaryUsing social media data for statistical analysis of general population faces commonly two basic obstacles: firstly, social media data are collected for different objects than the population units of interest; secondly, the relevant measures are typically not available directly but need to be extracted by algorithms or machine learning techniques. In this paper, we examine and summarise two existing approaches to statistical analysis based on social media data, which can be discerned in the literature. In the first approach, analysis is applied to the social media data that are organised around the objects directly observed in the data; in the second one, a different analysis is applied to a constructed pseudo survey dataset, aimed to transform the observed social media data to a set of units from the target population. We elaborate systematically the relevant data quality frameworks, exemplify their applications and highlight some typical challenges associated with social media data.

Factors Influencing the Knowledge Adoption of Mobile Game Developers in Online Communities : Focusing on the HSM and Data Quality Framework

Recently, with the advance of the wireless Internet access via mobile devices, a myriad of game development companies have forayed into the mobile game market, leading to intense competition. To survive in this fierce competition, mobile game developers often try to get a grasp of the rapidly changing needs of their customers by operating their own official communities where game users freely leave their requests, suggestions, and ideas relevant to focal games. Based on the heuristic-systematic model (HSM) and the data quality (DQ) framework, this study derives key content, non-content, and hybrid cues that can be utilized when game developers accept suggested postings in these online communities. The results of hierarchical multiple regression analysis show that relevancy, timeliness, amount of writing, and the number of comments are positively associated with mobile game developers’ knowledge adoption. In addition, title attractiveness mitigates the relationship between amount of writing/the number of comments and knowledge adoption.

Assessment of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database using a data quality index

A comprehensive data quality assessment is necessary to expand a nursing database that is designed for evaluating the impact of implementing Best Practice Guidelines (BPG) developed by the Registered Nurses' Association of Ontario (RNAO). This case report presents a method to standardize data quality assessments of the Nursing Quality Indicators for Reporting and Evaluation (NQuIRE) database by developing a data quality framework (DQF) and assessing key dimensions of the framework using a data quality index (DQI). The data quality index is a single key performance metric for assessing the quality of the database. The aims of sharing this case report are 2-fold: (1) to promote best practices for assessing data quality by developing and implementing a data quality framework and (2) to demonstrate an unprecedented method of assessing the data quality of a nursing database.

Service Data Quality Management Framework to Enable Through-life Engineering Services

With the growth of the product-service system (PSS) in recent years, how to better manage the service data to improve the informed decision-making capability has become an on-going aim among the Through-life Engineering Services (TES) firms. This scenario has led managers, more and more, to turn their attention to the quality of data and information created, gathered and used within the company. Encouraged by this background, a service data quality framework has been developed aiming to provide companies with a set of methods and tools to prioritise relevant service data and assess its quality levels. The process involves four main steps that go through: (1) Mapping out important data for Through-life Support available within the company and its internal and external flows; (2) Application of a multiple criteria decision-making technique to prioritise the relevant data set considering its costs for being collected and maintained, business impact, frequency of use and ease of obtainability; (3) Quality assessment of the prioritised dataset, based on a capability maturity model; (4) Defining strategies to address data quality issues. Validation on an industrial case study demonstrates potential benefits of the process and further work opportunities.