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Journal of Data and Information Science  2017, Vol. 2 Issue (3): 19-36    DOI: 10.1515/jdis-2017-0012
Expert Review     
Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata
Jane Greenberg ()
College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, USA
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Abstract  

Purpose: The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research. This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science.

Design/methodology/approach: This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science.

Findings: The “utilitarian nature” and “historical and traditional views” of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part of a metadata lingua franca to help frame research in the data science research space.

Research limitations:There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore.

Practical implications: The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem.

Originality/value: Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.



Key wordsMetadata research      Data science      Big metadata      Smart metadata      Metadata capital     
Published: 25 August 2017
Cite this article:

Jane Greenberg. Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata. Journal of Data and Information Science, 2017, 2(3): 19-36.

URL:

http://manu47.magtech.com.cn/Jwk3_jdis/10.1515/jdis-2017-0012     OR     http://manu47.magtech.com.cn/Jwk3_jdis/Y2017/V2/I3/19

Figure 1. Visual Business Intelligence: A blog by Stephen Few (January 23, 2017).
Five Vs Definition
Volume The quantity and usefulness of metadata generated daily confirms the existence of big metadata. At times metadata is less than or equal to the extent of the data it describes in size (bytes). During other times the metadata exceeds the data being described or tracked, due to the complexity of the data lifecycle activity. Linked data offers an example, with metadata renderings that can be larger than the volume of data object(s) being represented. Like big data, not all big metadata is useful, and a challenge is to identify the big metadata that is useful for data science and analytic endeavors.
Velocity Metadata is generated via automatic processes at immense speed correlating with rate of digital transactions. For example, searching Google, answering an email, purchasing an item online, and day-to-day office activities such as word processing of all log data, as well as associated metadata.
Variety Metadata reflects the wide variety of data formats, types, and genres along with the extensive range of data and metadata lifecycles. In addition, the different types of metadata (e.g. discovery, technical, preservation, etc.) as well as unique domain specific metadata requirements intensify the variety.
Variability There is an unmistakable unevenness of metadata across the digital ecosystem. Lack of uniformity is extensive for data descriptions across different domains, systems, and processes. This unevenness can even be profound within domains, given economic factors supporting metadata generation, competing standards, or, simply, differing adoption policies. For example, two organizations may use the same metadata standard, but have different implementation practices. Even when standardization is imposed, an organization, process, and human activity can contribute to inconsistencies.
Value If data is the new black gold*—akin to petroleum requiring purification, but also a money maker, then metadata is the new platinum—a malleable substance that keeps its toughness, and can serve as a catalyst, sparking a reaction.
Metadata, as the new platinum, can be modified, while remaining a strong, independent data type. Metadata stands as a durable data object that triggers various functions—the catalyst, and achieves results—a reaction. Metadata is vital to accurate data interpretation and use by both humans and machines, and the value of metadata for data science endeavors cannot be overstated or diminished.
Table 1 The five Vs of big metadata.
Figure 2. Smart metadata matrix of principles.
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