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Proposed Relational Database Metrics for Each Data Value Dimension

Proposed Relational Database Metrics for Each Data Value Dimension

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Conference Paper
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Data is becoming one of the world's most valuable resources and it is suggested that those who own the data will own the future. However, despite data being an important asset, data owners struggle to assess its value. Some recent pioneer works have led to an increased awareness of the necessity for measuring data value. They have also put forward...

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Context 1
... a visualisation dashboard is proposed to display the final results. The first step in developing the automated method was to establish a set of metrics that could be used to generate indicators for each data value dimension to be assessed in the RDB (see Table 2). Then a set of competency questions were developed for each metric to identify the RDB system attributes that would need to be exposed by queries of the database, logs or metadata. ...

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Citations

... This data value can be of social-ecological, economical [25,26], functional, and/or symbolic nature [10,26] to add a measurable business value [13]. Thereby, data value is determined by a multitude of value drivers [27,28], underlying theories [29][30][31][32][33], as well as frameworks [34,35]. ...
... From this, the dimension component is formed, which details the content of a DVBC [52]. Further, the outcomes are considered in the result dimension [35,49,53]. ...
... While qualitative data valuation focuses on generating contextual knowledge about the data value [34,38,50], quantitative data valuation concentrates on numerical information [34,35,54]. The existing literature shows that a combination [34] of both characteristics to different extents is also possible. ...
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Data and its valuation have gained vital significance in academia and enterprises, coinciding with diverse data valuation approaches encompassing various layers, dimensions, and characteristics. This paper assesses data value determination through a business capability lens based on the TOGAF standard. The paper encompasses (a) constructing a Data Valuation Business Capability (DVBC) taxonomy and (b) validating the taxonomy using two existing data valuation concepts from academia. The methodology involves information systems taxonomy development techniques backed by a previously conducted systematic literature review of 64 articles. The resultant taxonomy comprises four business capability layers, nine dimensions, and 36 characteristics. These layers and dimensions offer business, technology, and organizational perspectives, reflecting the interdisciplinary nature of data valuation alongside an enterprise architecture. Characteristics within these layers and dimensions are either exclusive or non-exclusive based on their contents. The compiled findings meet both objective and subjective quality criteria. The implications of the DVBC are multifaceted, influencing scholars and professionals alike. Scholars gain a cohesive tool enhancing transparency in the extensively debated data value domain, fostering linkages among information systems, enterprise architecture management, and data management. This empowers the progress in developing comprehensive data valuation concepts. Additionally, professionals may employ the DVBC taxonomy as a lighthouse and guiding tool, fostering internal dialog on data valuation. This entails elevating data valuation to a pivotal business capability, necessitating collaborative, regular assessment, and enhancement involving business and technological stakeholders. By adopting this taxonomy, the challenge of consistently determining data value can be effectively addressed in both academia and enterprises.
... This data value can be of social-ecological, economical [25,26], functional and/or symbolic nature [10,26] with the purpose of adding a measurable business value [13]. Thereby, data value is determined by a multitude of value drivers [27,28], underlying theories [29][30][31][32][33], as well as frameworks [34,35]. ...
... From this the dimension component is formedwhich details the content of a DVBC [52]. Further the outcomes are considered in the result dimension [35,49,53]. ...
... While qualitative data valuation focuses on generating contextual knowledge about the data value [34,38,50], quantitative data valuation concentrates on numerical information [34,35,57]. The existing literature shows that a combination [34] of both characteristics to different extents is also possible. ...
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Data and its valuation become increasingly crucial for enterprises and academia, which coincides a multitude of data valuation approaches including numerous affected focus areas, dimensions, and characteristics. Therefore, this paper analyzes different approaches to determine data value from a business capability perspective according to the TOGAF standard. Specifically, this paper deals with (a) the development of a taxonomy for data valuation business capabilities (DVBC) as well as (b) the taxonomy validation by the use of existing data valuation approaches. The applied methodologies are taxonomy development techniques for information systems, which are based on a previously executed systematic literature review with a sample size of 67 articles. Further, the data valuation business capability taxonomy is validated through applying two recent data valuation approaches from academia. As a result, the taxonomy developed consists of four business capability layers, nine dimensions, and 36 characteristics. The characteristics are of exclusive or non-exclusive nature, depending on their meaningfulness, and are validated by the successful application of two data valuation approaches. Compiled findings meet both objective and subjective quality standards. With the developed DVBC taxonomy, scholars and professionals are equipped with a tool to classify and structure their data valuation endeavors. In addition, the DVBC taxonomy bridges the domains of information systems, enterprise architecture management, and data management to serve as a foundation for interdisciplinary value generation with and through data.
... A specific line of research proposes general methodologies to derive the value of data. These methodologies can be based on human input [3,16,10], data processing [4,7] or both [5]. This ability to materialize the value of data in concrete numbers is all the more important as it is a crucial ingredient for a sound data governance and more generally for any decision supported by data [3,9]. ...
... Observation 8 -The selected dimensions should take a maximum of data value aspects into account [7]. ...
... Dimensions Batini et al. [6] accuracy, completeness, consistency, timeliness, currency, volatility, uniqueness, appropriate amount of data, accessibility, credibility, interpretability, usability, derivation integrity, conciseness, maintainability, applicability, convenience, speed, comprehensiveness, clarity, traceability, security, correctness, objectivity, relevance, reputation, ease of operation, interactivity Brennan et al. [9] usage, cost, quality, intrinsic, IT operations, contextual, utility Brennan et al. [10] operational impact/utility, dataset replacement costs, competitive advantage, regulatory risk, timeliness Wang et al. [27] content, credibility, critical thinking, copyright, citation, continuity, censorship, connectivity, comparability, context, site access and availability, resource identification and documentation, author identity, author authority, information structure and design, content relevance and scope, content effectiveness, accuracy and balance of content, navigation within documents, link quality, aesthetic and emotional aspects, information source, scope, discussion, technology factors, text format, information organization, price, availability, user support system, authority, credibility, accuracy, reasonableness, support, timeliness, integrity, consistency, acquisition cost Attard et al. [5] usage, quality, data, infrastructure Holst et al. [14] usage, quality, monetization, data sourcing costs, data processing and analysis needs, importance for business model and decisions Stein et al. [25] usage, quality, costs, completeness, conciseness, relevance, correctness, reliability, accuracy, precision, granularity, currency, timeliness Bendechache et al. [7] volume, usage, utility, replacement cost, legislative risk, timeliness, competitive advantage, quality, security pretability. We thus revise our definitions of usability and interpretability to better express these aspects. ...
... Data value is determined by various factors such as data complexity, number of records in the dataset, number of variables, and quality of the data (25). Furthermore, unidentified health records are less valuable because researchers need dates and geocodes to contextualize disease progression and comorbidities (26). ...
... Vezyridis and Timmons (26) described that within the National Health Services electronic health record systems, the billing codes used to record the same disease could vary widely between healthcare practices. In addition, the massive volume of electronic health datasets also affects quality because it is challenging to implement data standards and ranges (25). ...
... As there is a growing interest in managing health datasets as assets, blockchain technologies can improve data valuation and asset management (8). However, there are no uniform approaches to valuation (26) or assetization of health information (25). Thus far, most research conducted on data value has focused on the factors that can influence perceived data value (25). ...
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... Data value is determined by various factors such as data complexity, number of records in the dataset, number of variables, and quality of the data (25). Furthermore, unidentified health records are less valuable because researchers need dates and geocodes to contextualize disease progression and comorbidities (26). ...
... Vezyridis and Timmons (26) described that within the National Health Services electronic health record systems, the billing codes used to record the same disease could vary widely between healthcare practices. In addition, the massive volume of electronic health datasets also affects quality because it is challenging to implement data standards and ranges (25). ...
... As there is a growing interest in managing health datasets as assets, blockchain technologies can improve data valuation and asset management (8). However, there are no uniform approaches to valuation (26) or assetization of health information (25). Thus far, most research conducted on data value has focused on the factors that can influence perceived data value (25). ...
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Full-text available
There is increasing recognition that health-oriented datasets could be regarded as intangible assets: distinct assets with future economic benefits but without physical properties. While health-oriented datasets—particularly health records—are ascribed monetary value on the black market, there are few established methods for assessing value for legitimate research and business purposes. The emergence of blockchain has created new commerce opportunities for transferring assets without intermediaries. Therefore, blockchain is proposed as a medium by which research datasets could be transacted to provide future value. Blockchain methodologies also offer security, auditability, and transparency to authorized individuals for verifying transactions. The authors will share data valuation methodologies consistent with accounting principles and include discussions of black market valuation of health data. Further, this article describes blockchain-based methods of managing real-time payment/micropayment strategies.
... Some more recent works propose to use historical on-road driving performance data to informed the navigation decisionmaking [8] or attempt to predict traffic conditions using complex features from multiple sources and combined using advance models (e.g., [9]), but this raises several issues related to data collection [10], data storage [11], data quality [12], and data processing [13]. ...
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Data has become an important asset for businesses, and it is crucial to understand its value. The valuation of data is an important step in leveraging the potential of data as an asset. It helps companies to use data effectively and adapt their strategies accordingly. A distinction must be made between valuation and pricing. Valuation refers to the process of determining the objective value of an asset based on factors, such as comparable transactions, future cash flows or market trends. Pricing, in contrast, refers to the process of setting a price for a product offered in the market. The research paper at hand aims at an investigation of factors and methods for data valuation. Thus, a structured literature review is conducted to identify a framework to classify data valuation methods and influencing factors on the value of data.
Chapter
Research data sets are not just considered highly valuable for scientific purposes; these data sets could be sold and traded for economic value. Data sets could also be regarded as intangible assets, which do not have physical properties but could provide future economic benefits. With consideration that life sciences organizations possess thousands of siloed data sets, these could be sold to support additional research and could add value when life sciences organizations are appraised. Blockchain-based technologies are increasingly used to manage the control and auditability of both data and asset transactions in ways not possible with traditional databases. This chapter encourages life sciences organizations to view their data silos differently and consider the potential value these can create for the organization. This chapter describes methods to value and monetize health-oriented life sciences research data using common accounting principles. The chapter also describes the assetization of data sets and when data sets could be classified and traded using blockchain as non-fungible tokens. Last, the authors share ethical, legal, and regulatory constraints that should be considered before implementation.KeywordsBlockchainData valuationIntangible assetsMicropaymentsData sales
Chapter
The major problem in today’s data creation and monetization is that the data creators (individual people trading, traveling, and interacting on social media) are not the data aggregators (the Googles, Facebooks, and Amazons of the world). As such, the full potential of the personal data value in the age of informatics has yet to fully materialize. This leads to constant conflict within the data ecosystem regarding who has the right to own and monetize data; the creators or aggregators. It has also led to a protracted debate on data sovereignty and expanded legislation for data privacy that we deal with every day when we navigate any website. The holy-grail solution for such a problem is vertical integration, i.e., integrating the data value chain by combining and ensuring that data creators and aggregators are the same in the data value stack. Until recently, this was deemed technologically impossible because individuals in society cannot be their own bank, e-commerce platform, their own search engine, and their own social media. However, the advent of miniaturized sensors driven by advancements in device engineering and miniaturization ushered in a new age of multifunctional sensors, often called the Internet of Things (IoT). In particular, the distributed miniaturized devices that measure the biological attributes of individuals are called the Internet of Medical Things (IoMT). This chapter describes an end-to-end ecosystem that offers a solution to this problem and the commercial pilot model it has implemented utilizing the nascent but promising blockchain technology.KeywordsInternet of Medical Things (IoMT)Quantified wellnessProof of identityHomomorphic algorithmsData monetizationBioinformatics