Data Quality Dimensions Research Articles

Multidimensional Indicator for Data Quality Assessment in Wireless Sensor Networks: Challenges and Opportunities

Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making.

Improving data quality by implementing an electronic medical record seems to depend on the context

Abstract Background The European healthcare system is reliant on its digital transformation to deal with challenges like rising expenditures or workforce shortage. The digital transformation is inevitably accompanied by the implementation of electronic medical records (EMR) and the ongoing adaptation of existing ones. Systematic reviews indicate that this process can have an impact on medical records data quality (DQ) [1]. At micro level, DQ is essential to ensure high quality of care. At macro level, sufficient DQ is a prerequisite for big data analyzability. In this field, completeness is a commonly analyzed dimension of DQ and empirical results indicate that completeness can improve but also deteriorate as a result of the described implementation or adoption of EMRs [2]. The aim of this work was to investigate the implementation of EMRs in comparable settings and to observe and discuss possible differences in the change in DQ. Methods Data was collected on three surgical clinics of a German academic teaching hospital before and after the implementation of an EMR. Paper-based and electronic medical records were compared. Analysis focused on ten items that were commonly documented in both record types (e.g. pain). T-tests and χ²-tests were used to compare average completeness per record type and percentage of completeness per item. Results A total of N = 659 records was analyzed. Overall, results show a significant improvement in completeness from an average of 6.0/10 items in the paper-based record type to 7.2/10 in the EMR (p<.05). At clinic level, improvement rates vary from 0.9 to 1.4. At the level of the specific items, significant deteriorations are visible in certain clinics. Conclusions Results suggest that DQs variability is context-dependent (e.g. on the clinic’s turnover rate or its patient’s length of stay). Due to the unavoidable digital transformation, a detailed context and needs analysis involving all stakeholders should be carried out before any changes are made. Key messages • The application of advanced analytics such as big data or AI training is reliant on the availability of high-quality datasets. • Electronic medical records have been demonstrated to enhance data quality, but it remains uncertain how and why improvements appear to be context-dependent.

Intrinsic data quality dimensions: expanding on Wand and Wang’s data quality model

PurposeStudies show that data quality (DQ) issues are extremely costly for companies. To address such issues, as a starting point, there is a need to understand what DQ is. In his context, the 1996 paper “Anchoring data quality dimensions ontological foundations” by Wand and Wang has been highly influential on the understanding of DQ. However, the present study demonstrates that some of the assumptions made in their paper can be challenged. On this basis, this study seeks to develop clearer definitions.Design/methodology/approachThe assumptions behind Wand and Wang’s DQ classification are discussed, on which basis three counter-propositions are formulated. These are investigated through a representation theoretical approach involving analyses of deficient mappings between real-world and information system states. On this basis, an intrinsic DQ classification is derived. A case study is conducted to investigate the value of the developed DQ classification.FindingsThe representation theoretical analysis and the case study support the three propositions. These give rise to the development of a DQ classification that includes three primary intrinsic DQ dimensions (accuracy, completeness and conciseness), which are associated with six primary value-level DQ deficiencies (inaccuracy, incorrectness, meaninglessness, incompleteness, absence and redundancy). The case study supports the value of extending Wand and Wang’s DQ classification with the three additional data deficiencies.Research limitations/implicationsBy improving the conceptual clarity of DQ, this study provides future research with an improved basis for studies and discussions of DQ.Originality/valueThe study advances the understanding of DQ by providing additional clarity.

Research on Data Quality Analysis Based on Data Mining

As the process of digitalization continues to advance, big data plays a pivotal role across various industries. However, the quality of data directly impacts the results of data analysis and the accuracy of decision-making. High-quality data can provide a reliable basis for decisions, whereas poor-quality data may lead to erroneous judgments and cause incalculable losses. Therefore, the management and enhancement of data quality have become central issues in contemporary data management. Data mining technology, as a powerful tool, excels not only in extracting valuable information from large-scale datasets but also in demonstrating significant potential in improving data quality. By mining and analyzing the patterns and characteristics hidden within data, it is possible to identify and rectify defects, thereby enhancing the overall quality of data and increasing the reliability of data-driven decision-making. This paper delves into the definition and dimensions of data quality, explores the fundamental principles of data mining technology, and examines its specific applications in data quality analysis, aiming to offer new insights and methods for data quality management.

Assessing Hospital Data Quality: Application of a Data Quality Tool in 15 Countries

IntroductionGlobally, differences in coding practices and guidelines lead to variations in hospital-administrative data. Studies on patient safety found that better hospital-data quality is associated with higher rates of patient safety events. This study outlines our process of creating a standardized tool for countries to assess their hospital-administrative data quality. ApproachWe developed 24 indicators across five different dimensions through a Delphi-consensus method. To test applicability of these indicators, we approached representatives from 50 countries with an online survey comprised of qualitative and quantitative questions. Characteristics of each country and indicator scores were noted. A data quality overall score, out of 20, was calculated for each country based on survey responses. The score was classified into 3 data quality categories: high (≥18), moderate (13-17.9), and low (<13). ResultsOf the 50 countries invited, 17 responded. Surveys from two countries were excluded due to insufficient data. Country responses (n=15) were evaluated and scored by dimension. The data quality indicators showed positive face validity and were applicable for most countries providing comparative information for development of the tool with good discrimination. Canada, USA, New Zealand, UK, and Spain were among the countries with an overall high data quality score (≥18). Most countries scored high in 3 out of 5 dimensions of data quality. A few countries scored 0 out of 4 in ‘Relevance’ and ‘Timeliness’ dimensions resulting in a lower overall score. ConclusionWe propose our developed tool be used for comparing hospital-administrative data quality for applying the same standard across countries.

Public data and corporate employment: Evidence from the launch of Chinese public data platform

We investigate whether public data influences corporate employment. Our identification scheme treats the launch of Chinese public data platforms as a quasi-natural experiment and then conducts a difference-in-differences estimation. The results show that firms have hired more employees after opening public data platforms in the provinces where their offices are located, especially for companies in the mature stage, service industry companies, and companies located in regions with more advanced digital infrastructure and more developed financial system. Mechanism tests show that public data platform openness plays a role in corporate employment through the production scale expansion effect, the business scope expansion effect, and the digital asset investment effect. In addition, examining the two dimensions of data quality and platform construction quality, we find that the quality of public data openness has a significant effect on corporate employment. Our findings provide evidence for Chinese government to promote the openness of public data.

Data Quality-Driven Improvement in Health Care: Systematic Literature Review.

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.

RQSS: Referencing quality scoring system for Wikidata

Wikidata is a collaborative multi-purpose Knowledge Graph (KG) with the unique feature of adding provenance data to the statements of items as a reference. More than 73% of Wikidata statements have provenance metadata; however, few studies exist on the referencing quality in this KG, focusing only on the relevancy and trustworthiness of external sources. While there are existing frameworks to assess the quality of Linked Data, and in some aspects their metrics investigate provenance, there are none focused on reference quality. We define a comprehensive referencing quality assessment framework based on Linked Data quality dimensions, such as completeness and understandability. We implement the objective metrics of the assessment framework as the Referencing Quality Scoring System – RQSS. The system provides quantified scores by which the referencing quality can be analyzed and compared. RQSS scripts can also be reused to monitor the referencing quality regularly. Due to the scale of Wikidata, we have used well-defined subsets to evaluate the quality of references in Wikidata using RQSS. We evaluate RQSS over three topical subsets: Gene Wiki, Music, and Ships, corresponding to three Wikidata WikiProjects, along with four random subsets of various sizes. The evaluation shows that RQSS is practical and provides valuable information, which can be used by Wikidata contributors and project holders to identify the quality gaps. Based on RQSS, the average referencing quality in Wikidata subsets is 0.58 out of 1. Random subsets (representative of Wikidata) have higher overall scores than topical subsets by 0.05, with Gene Wiki having the highest scores amongst topical subsets. Regarding referencing quality dimensions, all subsets have high scores in accuracy, availability, security, and understandability, but have weaker scores in completeness, verifiability, objectivity, and versatility. Although RQSS is developed based on the Wikidata RDF model, its referencing quality assessment framework can be applied to KGs in general.

Application of a Data Quality Framework to Ductal Carcinoma In Situ Using Electronic Health Record Data From the All of Us Research Program.

The specific aims of this paper are to (1) develop and operationalize an electronic health record (EHR) data quality framework, (2) apply the dimensions of the framework to the phenotype and treatment pathways of ductal carcinoma in situ (DCIS) using All of Us Research Program data, and (3) propose and apply a checklist to evaluate the application of the framework. We developed a framework of five data quality dimensions (DQD; completeness, concordance, conformance, plausibility, and temporality). Participants signed a consent and Health Insurance Portability and Accountability Act authorization to share EHR data and responded to demographic questions in the Basics questionnaire. We evaluated the internal characteristics of the data and compared data with external benchmarks with descriptive and inferential statistics. We developed a DQD checklist to evaluate concept selection, internal verification, and external validity for each DQD. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) concept ID codes for DCIS were used to select a cohort of 2,209 females 18 years and older. Using the proposed DQD checklist criteria, (1) concepts were selected and internally verified for conformance; (2) concepts were selected and internally verified for completeness; (3) concepts were selected, internally verified, and externally validated for concordance; (4) concepts were selected, internally verified, and externally validated for plausibility; and (5) concepts were selected, internally verified, and externally validated for temporality. This assessment and evaluation provided insights into data quality for the DCIS phenotype using EHR data from the All of Us Research Program. The review demonstrates that salient clinical measures can be selected, applied, and operationalized within a conceptual framework and evaluated for fitness for use by applying a proposed checklist.

Development of Data Quality Indicators for Improving Hospital International Classification of Diseases-Coded Health Data Quality Globally.

Hospital inpatient data, coded using the International Classification of Diseases (ICD), is widely used to monitor diseases, allocate resources and funding, and evaluate patient outcomes. As such, hospital data quality should be measured before use; however, currently, there is no standard and international approach to assess ICD-coded data quality. To develop a standardized method for assessing hospital ICD-coded data quality that could be applied across countries: Data quality indicators (DQIs). To identify a set of candidate DQIs, we performed an environmental scan, reviewing gray and academic literature on data quality frameworks and existing methods to assess data quality. Indicators from the literature were then appraised and selected through a 3-round Delphi process. The first round involved face-to-face group and individual meetings for idea generation, while the second and third rounds were conducted remotely to collect online ratings. Final DQIs were selected based on the panelists' quantitative and qualitative feedback. Participants included international experts with expertise in administrative health data, data quality, and ICD coding. The resulting 24 DQIs encompass 5 dimensions of data quality: relevance, accuracy and reliability; comparability and coherence; timeliness; and Accessibility and clarity. These will help stakeholders (eg, World Health Organization) to assess hospital data quality using the same standard across countries and highlight areas in need of improvement. This novel area of research will facilitate international comparisons of ICD-coded data quality and be valuable to future studies and initiatives aimed at improving hospital administrative data quality.

Investigation of Tanks Inventory at Fuel Terminals Based on Data Quality Dimensions

Investigation of Tanks Inventory at Fuel Terminals Based on Data Quality Dimensions

The Effect of Data Quality on Decision-Making. A Quasi Experimental Study

The current study aimed to investigate the relationship between data quality dimensions (completeness and timeliness) and decision-making efficiency. The researcher adopted the quasi-experimental approach to answer the research questions and hypotheses. The study participants consist of 60 subjects, distributed into 2 groups. The first group consisted of 20 sales executives from Saudi beverage manufacturing companies. The other group had 40 participants from Al-Imam university. The study experiment consisted of 4 scenarios, two for each dimension, that give the participants scenarios and ask them to make the best decision based on these data. The scenarios were applied through face-to-face meeting and time to take decisions was recorded by participants. For the first data quality dimension, completeness of data, both groups got scenarios where complete and incomplete data were offered, and they were asked to choose the best available option based on the offered data. For the second dimension, the groups were offered up-to-date data and obsolete data and were asked to choose the best decisions based on the scenarios. The results were analyzed for correlations to check If there is a correlation between the responses of the two groups. The study found strong evidence for a correlational relationship between data quality dimensions and decision-making efficiency at 0.05 and 0.01 significance level for the student and employee groups respectively. The study found that high-quality data leads to both better and faster decisions in both groups. There were no significant differences between occupation, or gender and time taken for making a decision. The current study highlights the importance of data quality dimensions. It urges organizations to use up-to-date data, and complete data sets to base their decisions.

Efficient and Reliable Estimation of Knowledge Graph Accuracy

Data accuracy is a central dimension of data quality, especially when dealing with Knowledge Graphs (KGs). Auditing the accuracy of KGs is essential to make informed decisions in entity-oriented services or applications. However, manually evaluating the accuracy of large-scale KGs is prohibitively expensive, and research is focused on developing efficient sampling techniques for estimating KG accuracy. This work addresses the limitations of current KG accuracy estimation methods, which rely on the Wald method to build confidence intervals, addressing reliability issues such as zero-width and overshooting intervals. Our solution, rooted in the Wilson method and tailored for complex sampling designs, overcomes these limitations and ensures applicability across various evaluation scenarios. We show that the presented methods increase the reliability of accuracy estimates by up to two times when compared to the state-of-the-art while preserving or enhancing efficiency. Additionally, this consistency holds regardless of the KG size or topology.

LinkedDataOps:quality oriented end-to-end geospatial linked data production governance

This work describes the application of semantic web standards to data quality governance of data production pipelines in the architectural, engineering, and construction (AEC) domain for Ordnance Survey Ireland (OSi). It illustrates a new approach to data quality governance based on establishing a unified knowledge graph for data quality measurements across a complex, heterogeneous, quality-centric data production pipeline. It provides the first comprehensive formal mappings between semantic models of data quality dimensions defined by the four International Organization for Standardization (ISO) and World Wide Web Consortium (W3C) data quality standards applied by different tools and stakeholders. It provides an approach to uplift rule-based data quality reports into quality metrics suitable for aggregation and end-to-end analysis. Current industrial practice tends towards stove-piped, vendor-specific and domain-dependent tools to process data quality observations however there is a lack of open techniques and methodologies for combining quality measurements derived from different data quality standards to provide end-to-end data quality reporting, root cause analysis or visualisation. This work demonstrated that it is effective to use a knowledge graph and semantic web standards to unify distributed data quality monitoring in an organisation and present the results in an end-to-end data dashboard in a data quality standards-agnostic fashion for the Ordnance Survey Ireland data publishing pipeline.

Assessing Data Quality in the Age of Digital Social Research: A Systematic Review

While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.

Complete and Accurate? The Role of Profit Orientation in the Production of Public Health Data

Public officials rely on performance data that are self-reported by organizations to evaluate progress on a wide range of prosocial outcomes. Policies that require public disclosure of performance in health care are thought to enable patients to select high-quality providers, which in turn may spur quality improvements as providers seek to protect their reputation or increase economic returns. Drawing on institutional theory that examines how conflicting institutional pressures influence organizational decisions, we theorize how profit orientation may mediate the response of health care providers to disclosure mandates in a public health crisis. We focus on measures of data quality—completeness and accuracy—in self-reported data on COVID-19 status and outcomes generated by thousands of long-term care facilities. Using pooled regressions and a difference-in-differences style design, we find that overall, for-profit and not-for-profit facilities report similarly complete data but that for-profit facilities report less accurate data than their not-for-profit counterparts. Our results suggest that it may be important to incentivize each dimension of data quality separately to address differential institutional pressures that affect reporting practices.

Evaluating data quality for blended data using a data quality framework.

In 2020 the U.S. Federal Committee on Statistical Methodology (FCSM) released "A Framework for Data Quality", organized by 11 dimensions of data quality grouped among three domains of quality (utility, objectivity, integrity). This paper addresses the use of the FCSM Framework for data quality assessments of blended data. The FCSM Framework applies to all types of data, however best practices for implementation have not been documented. We applied the FCSM Framework for three health-research related case studies. For each case study, assessments of data quality dimensions were performed to identify threats to quality, possible mitigations of those threats, and trade-offs among them. From these assessments the authors concluded: 1) data quality assessments are more complex in practice than anticipated and expert guidance and documentation are important; 2) each dimension may not be equally important for different data uses; 3) data quality assessments can be subjective and having a quantitative tool could help explain the results, however, quantitative assessments may be closely tied to the intended use of the dataset; 4) there are common trade-offs and mitigations for some threats to quality among dimensions. This paper is one of the first to apply the FCSM Framework to specific use-cases and illustrates a process for similar data uses.

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews.

Health care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data. This review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework. A review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English. A total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions. The findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

Quality assessment of a rural population-based cancer registry (PBCR) at Ratnagiri, Maharashtra, India for the years 2017-18.

Cancer registries are valuable resources for cancer control and research. To justify their purpose, their data should be of satisfactory quality by being comparable internationally, complete in their coverage, valid in their values and timely in reporting. This study aimed to assess the quality of the Ratnagiri Population Based Cancer Registry's data for the years 2017-18 across the four dimensions of data quality. Regarding comparability, the registry procedure was reviewed vis-à-vis the rules they follow for cancer registry operation. We have used four methods for validity: re-abstraction and re-coding, diagnostic criteria methods- like the percentage of microscopically verified (MV%) and of death certificate only (DCO%) cases, missing information like proportion of cases of primary site unknown (PSU%) and internal validity. Semi-quantitative methods were employed for assessing completeness. Timeliness for all years of registry functioning was assessed qualitatively. The overall accuracy rate of the registry was found to be 91.1% (94.7% for demographic and 88% for tumour details). Mortality to incidence ratios were found to be 0.50 for females and 0.59 for males. MV% was found to be 90.8% for males and 91.5% for females. The average number of sources per case was found to be 1.5. DCO% was found to be 2.7%. PSU% was 7.4%. We have positive results regarding the data's validity and comparability, but there is scope for improvement concerning completeness. Continuous training of the registry personnel and monitoring of the registry is recommended.

Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions.

Electronic health record (EHR)-based real-world data (RWD) are integral to oncology research, and understanding fitness for use is critical for data users. Complexity of data sources and curation methods necessitate transparency into how quality is approached. We describe the application of data quality dimensions in curating EHR-derived oncology RWD. A targeted review was conducted to summarize data quality dimensions in frameworks published by the European Medicines Agency, The National Institute for Healthcare and Excellence, US Food and Drug Administration, Duke-Margolis Center for Health Policy, and Patient-Centered Outcomes Research Institute. We then characterized quality processes applied to curation of Flatiron Health RWD, which originate from EHRs of a nationwide network of academic and community cancer clinics, across the summarized quality dimensions. The primary quality dimensions across frameworks were relevance (including subdimensions of availability, sufficiency, and representativeness) and reliability (including subdimensions of accuracy, completeness, provenance, and timeliness). Flatiron Health RWD quality processes were aligned to each dimension. Relevancy to broad or specific use cases is optimized through data set size and variable breadth and depth. Accuracy is addressed using validation approaches, such as comparison with external or internal reference standards or indirect benchmarking, and verification checks for conformance, consistency, and plausibility, selected on the basis of feasibility and criticality of the variable to the intended use case. Completeness is assessed against expected source documentation; provenance by recording data transformation, management procedures, and auditable metadata; and timeliness by setting refresh frequency to minimize data lags. Development of high-quality, scaled, EHR-based RWD requires integration of systematic processes across the data lifecycle. Approaches to quality are optimized through knowledge of data sources, curation processes, and use case needs. By addressing quality dimensions from published frameworks, Flatiron Health RWD enable transparency in determining fitness for real-world evidence generation.