Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context
- Big Data is being used to improve decision making in the healthcare industry by increasing the potential of evidence-based medicine’s (EBM’s) “small data” . EBM is defined as “the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients” . Personalized decision support systems (PDSS) are enhancing personalized medicine or evidence-based medicine through big data analytics .
- Healthcare frauds are very serious issues in many countries. Big Data, with the help of data mining combined with machine learning, can play a major role in fraud detection. Data mining can identify some fraud as soon as it happens and therefore positively increase prevention.
- Big Data analytics is being applied with the aim of reducing patients’ readmission numbers. Patient readmission is not only very expensive for hospitals, but the ratio of patients’ dying after readmission is alarmingly high .
- Big data is proving to be a very useful tool for medical research. As there are many very large medical datasets, such as the human genomic dataset, pharmaceutical companies are harnessing the power of Big Data analytics to discover new medicines and understand diseases.
- Through the use of Internet of Things (IoT) in healthcare, data is retrieved in a pervasive manner. The data collected through IoT governs the daily life of the patient. Through integrating Big Data and IoT with healthcare, both patients and health facilities cut down costs by reducing the repetition of tests, so they benefit from more accurate diagnoses.
2. Literature Review
2.1. Data Governance and Data Quality
2.2. Data Quality Dimensions
2.3. Dimensions of Data Quality for Big Data
- Contextual consistency refers to how far big datasets are used within the same domain of interest independently of data format, size, and velocity of the production of data. For the current research, the domain of interest refers to health data. Relevancy, credibility, ease of understanding, accuracy, and confidentiality are key DQDs for this type of consistency.
- Temporal consistency conveys the idea that data needs to be understood in a consistent time slot such that the same data might not be comparable if it is from another time slot. Time concurrency, availability, and currency are deemed to be essential for temporal consistency.
- Operational consistency brings in the operational influence of technology on the production and use of data. The sources of data could be more than one in Big Data scenarios; hence, operational consistency is crucial for ensuring the veracity of data. Availability, portability, precision, completeness, and traceability are considered the main connected dimensions here. Table 2 shows a mapping of the 3Vs of Big Data (volume, variety, velocity) to the 3Cs (contextual consistency, temporal consistency, operational consistency) of data quality.
2.4. Data Quality Dimensions Specific for the Healthcare Industry
3. Research Methodology
3.1. Description of IHC
3.2. Conceptual Reasoning
- Should the Intrinsic category of DQDs still be applicable in the health industry context?
- How does the breadth of health data use cases impact the Contextual category of DQDs?
- Is the Representational category of DQDs negatively affected by the variety characteristic of Big Data and the fact that the quality of data would depend upon the aims of data analysis?
- Does the fact that health datasets are very often publicly available and voluminous data slows down access and retrieval of data negatively impact the Accessibility category of DQDs?
4. Work Undertaken
6. Conclusions and Implications
Conflicts of Interest
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|Accessibility||Extent to which data is available, or easily and quickly retrievable|
|Appropriate amount of data||Extent to which volume of data is appropriate for the task at hand|
|Believability||Extent to which data is regarded as true and credible|
|Completeness||Extent to which data is not missing and is of sufficient breadth and depth for the task at hand|
|Consistent representation||Extent to which data is presented in the same format|
|Ease of manipulation||Extent to which data is easy to manipulate and apply to different tasks|
|Free-of-error||Extent to which data is correct and reliable|
|Interpretability||Extent to which data is in the appropriate languages, symbols, and units, and the definitions are clear|
|Objectivity||Extent to which data is unbiased, unprejudiced, and impartial|
|Relevancy||Extent to which data is applicable and helpful for the task at hand|
|Reputation||Extent to which data is highly regarded in terms of its source and content|
|Security||Extent to which access to data is restricted appropriately to maintain its security|
|Timeliness||Extent to which data is sufficiently up-to-date|
|Understandability||Extent to which data is easily comprehended|
|Value-added||Extent to which data is beneficial and provides advantages from its uses|
|Contextual||Consistency, Credibility, Confidentiality||Completeness, Credibility||Accuracy, Consistency, Understandability|
|Temporal||Consistency, credibility, Currentness, Availability||Availability||Consistency, Currentness, Compliance|
|Operational||Completeness, Accessibility, Efficiency, Traceability, Availability, Recoverability||Completeness, Accessibility, Efficiency, Availability, Recoverability||Accuracy, Compliance, Accessibility, Efficiency, Traceability, Availability, Recoverability, Precision|
|Category||Description and Main DQ Dimensions|
|Intrinsic||Is explained by data having quality in their own right. (accuracy, objectivity, believability, and reputation)|
|Contextual||Highlights the idea that data quality is a factor of the task at hand (value-added, relevancy, timeliness, completeness, and appropriate amount of data)|
|Representational||Includes aspects linked with the format and meaning of data (interpretability, ease of understanding, representational consistency, and concise representation)|
|Accessibility||Emphasizes the role of getting access to data (accessibility and access security)|
|Research Article USED||DQ Dimensions||Weight|
|(Panahy et al., 2013) ||Accuracy, Completeness, Consistency, Timeliness.||L|
|(Khan et al., 2012) ||Consistency, Completeness, Accuracy||M|
|(Jones et al., 2017) ||Accuracy||L|
|(Amoakoh-Coleman et al., 2015) ||Accuracy, Completeness||L|
|(Jacke et al., 2012) ||Accuracy, Completeness||M|
|(Langley et al., 2006) ||Accuracy||L|
|(Serhani et al., 2016) ||Accuracy, Completeness, Consistency, Timeliness||H|
|(Batini et al., 2006) ||Accuracy, Completeness, Accessibility, Trust, Readability, Consistency||M|
|(Xiao et al., 2017) ||Availability, Completeness||L|
|(O’Reilly et al., 2016) ||Accuracy, Capture, Completeness||M|
|(Giarrizzo-Wilson et al., 2011) ||Accuracy, Completeness, Trust, Legibility||L|
|(Giarrizzo-Wilson et al., 2011) ||Accuracy, Completeness||L|
|(Varshney et al., 2015) ||Accuracy, Privacy and Security, Heterogeneity, Provenance and Trust, Availability, Completeness||M|
|(Li et al., 2007) ||Completeness, Reliability, Correctness, Consistency, ‘minimality’||L|
|(Sidi et al., 2012) ||Accuracy, Completeness, Consistency||L|
|(Weber et al., 2015) ||Correctness, Provenance, Currency, Plausibility||M|
|(Leon et al., 2016) ||Accuracy, Completeness, Consistency, Currency, Reliability, Uniqueness||H|
|(Pinto, 2006) ||Relevance, Consistency, Accuracy, Currency, Comprehensiveness, Format||L|
|(Arts et al., 2002) ||Accuracy, Completeness, Clarity, Format||M|
|(Weiskopf and Weng, 2012) ||Completeness, Correctness, Plausibility Concordance, Currency||M|
|(Geisler et al., 2011) ||Accuracy, Completeness, Consistency, Timeliness, Confidence, Data Volume||M|
|(Todoran et al., 2015) ||Accuracy, Completeness, Currency, Reliability||H|
|(Nystrom et al., 2013) ||Accuracy, Precision||L|
|(Huang et al., 2012) ||Accuracy, Usefulness, Accessibility, Relevance, Security||H|
|(Aday and Cynamon, 2010) ||Accuracy, Reliability, Consistency||H|
|(Cure, 2012) ||Accuracy, Completeness||M|
|(Vattulainen, 2015) ||Completeness, Redundancy, Accuracy, Representativeness, Consistency||L|
|(Byrd and Byrd, 2013) ||Accuracy, Completeness, Timeliness||M|
|(Lin et al., 2016) ||Completeness, Consistency, Coincidence||M|
|(Gibson, 1997) ||Accuracy, Completeness, Precision, Verifiability, Validity, Plausibility||M|
|(Vetro et al., 2016) ||Accuracy, Completeness, Understandability, Traceability, Compliance||M|
|(Craswell et al., 2016) ||Accuracy, Consistency, Clarity||M|
|(Lee and Haider, 2013) ||Believability, Security, Accuracy, Timeliness||L|
|(Lima et al., 2009) ||Reliability, Validity, Coverage, Accuracy, Completeness||M|
|(CDC, 2009) ||Consistency, Accuracy, Plausibility||M|
|(Cai and Zhu, 2015) ||Availability, Usability, Reliability, Relevance, Presentation Quality||M|
|(Blake and Mangiameli, 2011) ||Accuracy, Completeness, Consistency, Timeliness||L|
|(Salati et al., 2016) ||Completeness, Reliability||L|
|(Rhodegero, 2014) ||Completeness, Accuracy||M|
|(Kahn et al., 2012) ||Format, Availability, Timeliness, Consistency||L|
|(White, 2014) ||Consistency, Conciseness, Completeness, Expandability, Sensitivity.||M|
|DQD||Total Weighted Count||DQD||Total Weighted Count|
|Intrinsic||Accuracy, Trust, Plausibility, Precision, Compliance, Traceability, Verifiability, Provenance, Confidence, Concordance, Correctness||87|
|Contextual||Completeness, Timeliness, Currency, Reliability, Availability, Uniqueness, Relevance, Validity, Expandability, Sensitivity, Coverage, Data volume, Comprehensiveness, Heterogeneity||108|
|Representational||Consistency, Format, Usefulness, Readability, Capture, Coincidence, Understandable, Usability, Presentation quality||48|
|Accessibility||Accessibility, Security, Privacy, Compliance||14|
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Juddoo, S.; George, C.; Duquenoy, P.; Windridge, D. Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context. Appl. Syst. Innov. 2018, 1, 43. https://doi.org/10.3390/asi1040043
Juddoo S, George C, Duquenoy P, Windridge D. Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context. Applied System Innovation. 2018; 1(4):43. https://doi.org/10.3390/asi1040043Chicago/Turabian Style
Juddoo, Suraj, Carlisle George, Penny Duquenoy, and David Windridge. 2018. "Data Governance in the Health Industry: Investigating Data Quality Dimensions within a Big Data Context" Applied System Innovation 1, no. 4: 43. https://doi.org/10.3390/asi1040043