A Feature-Driven Analysis of Global Cybersecurity Regulations and their Impact on Safeguarding Data Value

The promise of real-world evidence and the learning health care system primarily depends on access to high-quality data. Despite widespread awareness of the prevalence and potential impacts of poor data quality (DQ), best practices for its assessment and improvement are unknown. This review aims to investigate how existing research studies define, assess, and improve the quality of structured real-world health care data. A systematic literature search of studies in the English language was implemented in the Embase and PubMed databases to select studies that specifically aimed to measure and improve the quality of structured real-world data within any clinical setting. The time frame for the analysis was from January 1945 to June 2023. We standardized DQ concepts according to the Data Management Association (DAMA) DQ framework to enable comparison between studies. After screening and filtering by 2 independent authors, we identified 39 relevant articles reporting DQ improvement initiatives. The studies were characterized by considerable heterogeneity in settings and approaches to DQ assessment and improvement. Affiliated institutions were from 18 different countries and 18 different health domains. DQ assessment methods were largely manual and targeted completeness and 1 other DQ dimension. Use of DQ frameworks was limited to the Weiskopf and Weng (3/6, 50%) or Kahn harmonized model (3/6, 50%). Use of standardized methodologies to design and implement quality improvement was lacking, but mainly included plan-do-study-act (PDSA) or define-measure-analyze-improve-control (DMAIC) cycles. Most studies reported DQ improvements using multiple interventions, which included either DQ reporting and personalized feedback (24/39, 61%), IT-related solutions (21/39, 54%), training (17/39, 44%), improvements in workflows (5/39, 13%), or data cleaning (3/39, 8%). Most studies reported improvements in DQ through a combination of these interventions. Statistical methods were used to determine significance of treatment effect (22/39, 56% times), but only 1 study implemented a randomized controlled study design. Variability in study designs, approaches to delivering interventions, and reporting DQ changes hindered a robust meta-analysis of treatment effects. There is an urgent need for standardized guidelines in DQ improvement research to enable comparison and effective synthesis of lessons learned. Frameworks such as PDSA learning cycles and the DAMA DQ framework can facilitate this unmet need. In addition, DQ improvement studies can also benefit from prioritizing root cause analysis of DQ issues to ensure the most appropriate intervention is implemented, thereby ensuring long-term, sustainable improvement. Despite the rise in DQ improvement studies in the last decade, significant heterogeneity in methodologies and reporting remains a challenge. Adopting standardized frameworks for DQ assessment, analysis, and improvement can enhance the effectiveness, comparability, and generalizability of DQ improvement initiatives.

ObjectiveReview the impact of applying regular data quality checks to assess completeness of core data elements that support syndromic surveillance.IntroductionThe National Syndromic Surveillance Program (NSSP) is a community focused collaboration among federal, state, and local public health agencies and partners for timely exchange of syndromic data. These data, captured in nearly real time, are intended to improve the nation's situational awareness and responsiveness to hazardous events and disease outbreaks. During CDC’s previous implementation of a syndromic surveillance system (BioSense 2), there was a reported lack of transparency and sharing of information on the data processing applied to data feeds, encumbering the identification and resolution of data quality issues. The BioSense Governance Group Data Quality Workgroup paved the way to rethink surveillance data flow and quality. Their work and collaboration with state and local partners led to NSSP redesigning the program’s data flow. The new data flow provided a ripe opportunity for NSSP analysts to study the data landscape (e.g., capturing of HL7 messages and core data elements), assess end-to-end data flow, and make adjustments to ensure all data being reported were processed, stored, and made accessible to the user community. In addition, NSSP extensively documented the new data flow, providing the transparency the community needed to better understand the disposition of facility data. Even with a new and improved data flow, data quality issues that were issues in the past, but went unreported, remained issues in the new data. However, these issues were now identified. The newly designed data flow provided opportunities to report and act on issues found in the data unlike previous versions. Therefore, an important component of the NSSP data flow was the implementation of regularly scheduled standard data quality checks, and release of standard data quality reports summarizing data quality findings.MethodsNSSP data was assessed for the national-level completeness of chief complaint and discharge diagnosis data. Completeness is the rate of non- null values (Batini et al., 2009). It was defined as the percent of visits (e.g., emergency department, urgent care center) with a non-null value found among the one or more records associated with the visit. National completeness rates for visits in 2016 were compared with completeness rates of visits in 2017 (a partial year including visits through August 2017). In addition, facility-level progress was quantified after scoring each facility based on the percent completeness change between 2016 and 2017. Legacy data processed prior to introducing the new NSSP data flow were not included in this assessment.ResultsNationally, the percent completeness of chief complaint for visits in 2016 was 82.06% (N=58,192,721), and the percent completeness of chief complaint for visits in 2017 was 87.15% (N=80,603,991). Of the 2,646 facilities that sent visits data in 2016 and 2017, 114 (4.31%) facilities showed an increase of at least 10% in chief complaint completeness in 2017 compared with 2016. As for discharge diagnosis, national results showed the percent completeness of discharge diagnosis for 2016 visits was 50.83% (N=36,048,334), and the percent completeness of discharge diagnosis for 2017 was 59.23% (N=54,776,310). Of the 2,646 facilities that sent data for visits in 2016 and 2017, 306 (11.56%) facilities showed more than a 10% increase in percent completeness of discharge diagnosis in 2017 compared with 2016.ConclusionsNationally, the percent completeness of chief complaint for visits in 2016 was 82.06% (N=58,192,721), and the percent completeness of chief complaint for visits in 2017 was 87.15% (N=80,603,991). Of the 2,646 facilities that sent visits data in 2016 and 2017, 114 (4.31%) facilities showed an increase of at least 10% in chief complaint completeness in 2017 compared with 2016. As for discharge diagnosis, national results showed the percent completeness of discharge diagnosis for 2016 visits was 50.83% (N=36,048,334), and the percent completeness of discharge diagnosis for 2017 was 59.23% (N=54,776,310). Of the 2,646 facilities that sent data for visits in 2016 and 2017, 306 (11.56%) facilities showed more than a 10% increase in percent completeness of discharge diagnosis in 2017 compared with 2016.ReferencesBatini, C., Cappiello. C., Francalanci, C. and Maurino, A. (2009) Methodologies for data quality assessment and improvement. ACM Comput. Surv., 41(3). 1-52.

Data play an essential role in asset management decisions. The amount of data is increasing through accumulating historical data records, new measuring devices, and communication technology, notably with the evolution toward smart grids. Consequently, the management of data quantity and quality is becoming even more relevant for asset managers to meet efficiency and reliability requirements for power grids. In this work, we propose an innovative data quality management framework enabling asset managers (i) to quantify the impact of poor data quality, and (ii) to determine the conditions under which an investment in data quality improvement is required. To this end, an algorithm is used to determine the optimal year for component replacement based on three scenarios, a Reference scenario, an Imperfect information scenario, and an Investment in higher data quality scenario. Our results indicate that (i) the impact on the optimal year of replacement is the highest for middle-aged components; (ii) the profitability of investments in data quality improvement depends on various factors, including data quality, and the cost of investment in data quality improvement. Finally, we discuss the implementation of the proposed models to control data quality in practice, while taking into account real-world technological and economic limitations.

Developments in medical data analysis bring important social benefits but also challenge privacy and other ethical values. At the same time, excessive data protection restrictions can be a serious threat to observational studies. The debate on whether data privacy or data correlation should be the core value of research policies has remained unresolved. Today, in the health care sector, which is developing rapidly, it is highly important to maintain integrity, privacy, and access to data (1). Data governance plays an important role in navigating this path, ensuring compliance with regulations, and paving the way for improved patient outcomes. Data governance in the field of health care is an organized and standardized method for managing, analyzing, and sharing medical data in an ethical and transparent manner, all of which align with legal and ethical standards (1, 2). Data Governance in Healthcare It covers the entire life cycle of patient information, from initial admission to long after discharge. In essence, healthcare data is a valuable asset. Effective data governance is very useful in making informed decisions and providing high-level patient care. A concrete example can be given from an article entitled "Challenges Caused by the COVID-19 Pandemic: The Long-Standing Need for Strong Data Governance", which revealed addressing aspects such as intellectual property rights, interoperability, data storage, sharing, and ensuring equitable access to healthcare resources (1). According to case reports, there are also obstacles and challenges for the management of healthcare and health data, the most important of which are mentioned below: · Privacy and data security · Compliance with regulations · Management of diverse data sources · Ensuring interoperability in different systems and data formats (2). Addressing these barriers and challenges with a comprehensive data management strategy can simplify processes and create data standards, in addition to improving data quality and access to qualitative data and strengthening collaboration between stakeholders. Therefore, in a wide treatment network with numerous electronic files, better coordination and coherence have been created, and while speeding up treatment processes, conditions will be provided for an advanced and more effective health control operation. <center> <img alt="" src="/images/hmrc/hakemiate-dade-en.JPG" style="width: 55%; height: 434%;" /></center> Based on Figure 1, effective data governance in health care will have several advantages. In fact, data governance is based on a specific pattern of input and output. Data input includes data and metadata management, especially data definitions and standards, quantitative classification and data quality control. If these important factors and indicators of data and information input are done accurately and without mistakes, the result of the work, i.e., the output of data governance, will lead to the adoption of appropriate strategies to meet the demand of the industry, more effective clinical care and reliable operations in the health system (2, 4). Effective Data Governance in Healthcare Reveals Several Advantages 1- Improving patient care through interoperability: By setting standards and promoting data integration, data governance strengthens interoperability and ultimately leads to improved care coordination and comprehensive patient care. 2- Optimized services and resource efficiency: Simplifying data processes and eliminating redundant items can lead to providing optimal services and improving patient experiences. 3- Fostering innovation and research: By establishing clear data access and sharing policies, data governance supports innovation and research and enables large-scale data analysis, trend identification, and epidemiological studies. 4- Informed decision-making: Improving the quality and accuracy of data is the basis of informed decision-making and increases patient safety and care results. It also enables data-driven insights into various aspects of healthcare, including resource allocation and clinical outcomes. 5- Compliance with regulations and ethical data management: Ensuring alignment with legal requirements and the best industrial practices reduces risks and sets a standard for ethical data practices (3, 4). Creating a Comprehensive Data Governance Framework Creating a data governance framework for health care includes drawing policies, standards, roles, responsibilities, and accountability related to data. The key components of this framework include data standards and definitions, data quality management, metadata management, master data management, and data architecture (2). Implementing Data Governance: A Step-by-Step Guide 1- Define the scope: Specify the organizational structure, authorities, councils, and roles. 2- Form a governance team: Create a team that includes a chief data officer and a chief medical information officer to drive accountability. 3- Identify key metrics: Focus on data accuracy, process improvement, and efficiency. 4- Understand compliance standards: Familiarize yourself with existing regulations and policies. 5- Secure the system: Implement encryption, masking, and hashing policies for protected health information. 6- Setting user permissions: Create authentication protocols and monitor data access. 7- Educate your team: Educate your teams on adopting and following a data governance plan(2, 5). Evaluation of Data Governance Solutions When evaluating solutions, prioritize capabilities such as data classification and tagging, data search and discovery, metadata management, data provenance and traceability, data security and privacy, compliance and reporting, and integration and interoperability (4, 5). In summary, data governance is of practical importance at all levels of healthcare and health and ensures that doctors, therapists, and health care providers have access to accurate, safe, and private data of patients. This requires an effort at all organizational levels (Ministry of Health), including interdisciplinary teams of experts. This commentary provides a foundation for understanding the importance, benefits, framework components, and steps for implementing data governance in healthcare. As the healthcare landscape continues to evolve, data governance initiatives must also evolve to improve the efficiency of health system management.

This chapter demonstrates the extent of the data protection problems in China, and the public's growing concern about loss of privacy and abuse of their personal data. It proceeds to show that under China's Cyber Security Law, the government has responded to this issue by strengthening 'data protection' from abuse by private companies but without shielding 'data privacy' from government intervention. In particular, enforced real-name user registration for online services potentially allows the Chinese government to demand access to the local data of any person who uses an online service in China, for national security or criminal investigation purposes. The chapter argues that this internal contradiction within the Cyber Security Law – increased data protection while demanding real-name user registration – may also benefit AI development. This is due, in part, to the vagueness of key terms within the Cyber Security Law, and the accompanying fuzzy logic within the Privacy Standards issued under that law, which allow both tech firms and government regulators considerable discretion in how they comply with and enforce data protection provisions. In the final part of the chapter, it is argued that due to the potential benefits of AI in solving serious governance problems, the Chinese government will only selectively enforce the data privacy provisions in the Cyber Security Law, seeking to prevent commercial abuse without hindering useful technological advances.

BackgroundHigh-quality data are critical to inform, monitor and manage health programs. Over the seven-year African Health Initiative of the Doris Duke Charitable Foundation, three of the five Population Health Implementation and Training (PHIT) partnership projects in Mozambique, Rwanda, and Zambia introduced strategies to improve the quality and evaluation of routinely-collected data at the primary health care level, and stimulate its use in evidence-based decision-making. Using the Consolidated Framework for Implementation Research (CFIR) as a guide, this paper: 1) describes and categorizes data quality assessment and improvement activities of the projects, and 2) identifies core intervention components and implementation strategy adaptations introduced to improve data quality in each setting.MethodsThe CFIR was adapted through a qualitative theme reduction process involving discussions with key informants from each project, who identified two domains and ten constructs most relevant to the study aim of describing and comparing each country’s data quality assessment approach and implementation process. Data were collected on each project’s data quality improvement strategies, activities implemented, and results via a semi-structured questionnaire with closed and open-ended items administered to health management information systems leads in each country, with complementary data abstraction from project reports.ResultsAcross the three projects, intervention components that aligned with user priorities and government systems were perceived to be relatively advantageous, and more readily adapted and adopted. Activities that both assessed and improved data quality (including data quality assessments, mentorship and supportive supervision, establishment and/or strengthening of electronic medical record systems), received higher ranking scores from respondents.ConclusionOur findings suggest that, at a minimum, successful data quality improvement efforts should include routine audits linked to ongoing, on-the-job mentoring at the point of service. This pairing of interventions engages health workers in data collection, cleaning, and analysis of real-world data, and thus provides important skills building with on-site mentoring. The effect of these core components is strengthened by performance review meetings that unify multiple health system levels (provincial, district, facility, and community) to assess data quality, highlight areas of weakness, and plan improvements.

Due to the rapid development of the mobile Internet and the Internet of Things, the volume of generated data keeps growing. The topic of data quality has gained increasing attention recently. Numerous studies have explored various data quality (DQ) problems across several fields, with corresponding effective data-cleaning strategies being researched. This paper begins with a comprehensive and systematic review of studies related to DQ. On the one hand, we classify these DQ-related studies into six types: redundant data, missing data, noisy data, erroneous data, conflicting data, and sparse data. On the other hand, we discuss the corresponding data-cleaning strategies for each DQ type. Secondly, we examine DQ issues and potential solutions for a public bus transportation system, utilizing a real-world traffic big data platform. Finally, we provide two representative examples, noise filtering and filling missing values, to demonstrate the DQ improvement practice. The experimental results show that: (1) The GPS noise filtering solution we proposed surpasses the baseline and achieves an accuracy of 97%; (2) The multi-source data fusion method can achieve a 100% missing repair rate (MRR) for bus arrival and departure. The average relative error (ARE) of bus arrival and departure times at stations is less than 1%, and the correlation coefficient (R) is also close to 1. Our research can offer guidance and lessons for enhancing data governance and quality improvement in the bus transportation system.

Classifying, measuring and improving the quality of data in trauma registries: A review of the literature

Mass gatherings attract large crowds and can strain the planning and health resources of the community, city, or nation hosting an event. Mass-Gatherings Health (MGH) is an evolving niche of prehospital care rooted in emergency medicine, emergency management, public health, and disaster medicine. To explore front-line issues related to data quality in the context of mass gatherings, the authors draw on five years of management experience with an online, mass-gathering event and patient registry, as well as clinical and operational experience amassed over several decades. Here the authors propose underlying human, environmental, and logistical factors that may contribute to poor data quality at mass gatherings, and make specific recommendations for improvement through pre-event planning, on-site actions, and post-event follow-up. The advancement of MGH research will rely on addressing factors that influence data quality and developing strategies to mitigate or enhance those factors. This is an exciting time for MGH research as higher order questions are beginning to be addressed; however, quality research must start from the ground up to ensure optimal primary data capture and quality. Guy A , Prager R , Turris S , Lund A . Improving data quality in mass-gatherings health research. Prehosp Disaster Med. 2017;32(3):329-332.

A data warehouse aids in the management of large amounts of data that may be stored in order to handle user input during the computer process. The major issue with a data warehouse is to maintain the data that the user stores in good quality. Some traditional techniques can improve data quality while also increasing efficiency. Each unit of data has a unique feature that has been researched by many researchers and has an influence on data quality. This research article has enhanced the K-Means method by utilizing the Euclidean Distance metric to detect missing values from the gathered sources and replace them with closest values while maintaining the data's consistency, exactness, and quality. yThe improved data will assist developers in analysing data quality prior to data integration by allowing them to make informed decisions quickly in accordance with business requirements. Improved K-Means achieves better accuracy and requires less computational time for clustering data objects when compared to other related approaches.

Data quality improvement is an important aspect of enterprise data management. Data characteristics can change with customers, with domain and geography making data quality improvement a challenging task. Data quality improvement is often an iterative process which mainly involves writing a set of data quality rules for standardization and elimination of duplicates that are present within the data. Existing data cleansing tools require a fair amount of customization whenever moving from one customer to another and from one domain to another. In this paper, we present a data quality improvement tool which helps the data quality practitioner by showing the characteristics of the entities present in the data. The tool identifies the variants and synonyms of a given entity present in the data which is an important task for writing data quality rules for standardizing the data. We present a ripple down rule framework for maintaining data quality rules which helps in reducing the services effort for adding new rules. We also present a typical workflow of the data quality improvement process and show the usefulness of the tool at each step. We also present some experimental results and discussions on the usefulness of the tools for reducing services effort in a data quality improvement.

Purpose: This paper offers a comprehensive reflection on the post-pandemic challenges and opportunities for marketing research in the 21st century. It explores how technological transformation, digitalization, and the explosion of big data are reshaping marketing theory, research design, and analytical practice. Design/Methodology/Approach: Using a conceptual and integrative approach, the study synthesizes developments in big data analytics, artificial intelligence (AI), and machine learning, emphasizing their impact on marketing research methods. The discussion builds upon prior frameworks by Hair, Harrison, and Risher (2018), updated to include implications of COVID-19 and the acceleration of digital transformation. The authors analyze methodological trends—descriptive, predictive, and prescriptive analytics—and the shift toward data-driven decision-making supported by advanced statistical and computational tools such as PLS-SEM, Bayesian modeling, and AutoML. Findings: The study identifies key drivers transforming marketing research: (1) exponential data growth; (2) improvement in data quality and analytical capabilities; (3) increased integration of predictive and prescriptive analytics; (4) data privacy and governance challenges; and (5) the global shortage of trained data scientists. It concludes that marketing research must evolve toward greater methodological rigor, interdisciplinary collaboration, and real-time analytics to remain relevant in dynamic markets. Practical Implications: Researchers and practitioners must embrace advanced analytics, invest in data quality, and develop ethical frameworks for AI and big data applications. Originality/Value: This work contributes a post-pandemic perspective on the digital transformation of marketing research, providing a roadmap for academia and industry to navigate data-intensive and AI-driven futures.

Over the last years many data quality initiatives and suggestions report how to improve and sustain data quality. However, almost all data quality projects and suggestions focus on the assessment and one-time quality improvement, especially, suggestions rarely include how to sustain the continuous data quality improvement. Inspired by the work related to variability in supply chains, also known as the Bullwhip effect, this paper aims to suggest how to sustain data quality improvements and investigate the effects of delays in reporting data quality indicators. Furthermore, we propose that a data quality prediction model can be used as one of countermeasures to reduce the Data Quality Bullwhip Effect. Based on a real-world case study, this paper makes an attempt to show how to reduce this effect. Our results indicate that data quality success is a critical practice, and predicting data quality improvements can be used to decrease the variability of the data quality index in a long run.

The rapid expansion of cloud computing and artificial intelligence (AI) has driven transformative change across various industries, presenting both opportunities and challenges in the realms of compliance and governance. This review examines the distinctive and overlapping compliance and governance issues faced by the United States (USA) and African countries in managing cloud computing and AI technologies. In the USA, compliance frameworks such as the California Consumer Privacy Act (CCPA), HIPAA, and the NIST AI Risk Management Framework provide regulatory infrastructure, emphasizing data privacy, sovereignty, and AI ethics. In contrast, African nations, led by South Africa’s Protection of Personal Information Act (POPIA) and regional initiatives like those promoted by the African Union, are developing data protection and AI governance structures within diverse and resource-constrained environments. Key compliance concerns include data privacy, sovereignty, and cross-border data transfers, with the USA focusing on sectoral regulations and Africa on emerging continent-wide data frameworks. Governance challenges differ across regions, especially in data ownership, AI ethics, and risk management; in the USA, well-established risk management frameworks enable more consistent cybersecurity practices, whereas African nations often face hurdles related to limited infrastructure and varying regulatory standards. This comparative analysis underscores the importance of harmonized policies, highlighting the need for collaborative, cross-regional initiatives to mitigate regulatory disparities and foster secure data flows. Ultimately, this review advocates for adaptive, flexible frameworks that incorporate ethical AI guidelines and global best practices, which are essential for supporting sustainable cloud and AI adoption across the USA and Africa. Through proactive compliance strategies and enhanced governance mechanisms, these regions can effectively navigate the challenges of a technology-driven global landscape while promoting innovation and protecting stakeholder interests.

The intelligent and connected vehicle industry is a key component of the digital economy. The data collected during vehicle operation contains a significant amount of critical information, and its leakage or tampering in cross-border data flow scenarios can result in severe security incidents. China implements a data classification and grading management system; however, current challenges such as subjective classification criteria and dynamic adjustment persist. This paper analyzes the developmental trends of global data flow policies and further explores the difficulties in data classification and grading, such as contextual dependency and data quality. Focusing on cross-border data transfer in vehicles, this study proposes a data nature and quality analysis framework. By conducting a layered analysis of data nature (foundation layer, functional layer, informational layer, and risk layer) and evaluating data quality (identifiability and temporality), the framework provides a quantitative assessment method for governing cross-border vehicle data flows.