Dark Data

Dark Data

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Dark Data

Dark data is a kind of data acquired via several computer network operations but not utilized to derive ken insights for decision making. Essentially, it is unstructured and thus cannot be used. It is persistently collected and stored with the challenge to organize in categories, organization tools or labels. Since the precious trove of unstructured data can hold keen insights when organized systematically, it is deemed to be in the dark state in the contemporary era ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"bth4U5aa","properties":{"formattedCitation":"(\\uc0\\u8220{}Dark data,\\uc0\\u8221{} 2017)","plainCitation":"(“Dark data,” 2017)","noteIndex":0},"citationItems":[{"id":71,"uris":["http://zotero.org/users/local/yvjivw9i/items/H4U47PKK"],"uri":["http://zotero.org/users/local/yvjivw9i/items/H4U47PKK"],"itemData":{"id":71,"type":"webpage","title":"Dark data: The two sides of the same coin","container-title":"Analytics Magazine","abstract":"Today, we live in a digital society. Our distinct footprints are in every interaction we make. Data generation is a default – be it from enterprise operational systems, logs from web servers, other applications, social interactions and transactions, research initiatives and connected things (Internet of Things). In fact, according to a Digital Universe study, 2.2 zettabytes of data was generated in 2012. This grew by 100 percent in 2013, and is slated to grow to 44 zettabytes by 2020 worldwide.","URL":"http://analytics-magazine.org/dark-data-two-sides-coin/","title-short":"Dark data","language":"en-US","issued":{"date-parts":[["2017",7,6]]},"accessed":{"date-parts":[["2019",4,26]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (“Dark data,” 2017). Dark data is waiting indefinitely to be analyzed and evaluated through data analytics.

A prominent example of dark data is the customer call record. Primarily holding precious information on a customer’s geolocation and thoughts, these kinds of records are persistently stored and recorded ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"O8AueqWN","properties":{"formattedCitation":"(\\uc0\\u8220{}darkdata.org,\\uc0\\u8221{} n.d.)","plainCitation":"(“darkdata.org,” n.d.)","noteIndex":0},"citationItems":[{"id":73,"uris":["http://zotero.org/users/local/yvjivw9i/items/SNQQ4CGE"],"uri":["http://zotero.org/users/local/yvjivw9i/items/SNQQ4CGE"],"itemData":{"id":73,"type":"webpage","title":"darkdata.org","URL":"https://www.darkdata.org/","accessed":{"date-parts":[["2019",4,26]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (“darkdata.org,” n.d.). However, it is an uphill task to analyze or organize them in detail. Likewise, a website log file is another example of dark data. These website logs possess the potential to hold precious information on traffic and visitor behavior. They can be, irrefutably, collected easily and persistently but there does not exist a process to analyze and organize these logs in a productive way.

As per a report published in 2011, more than 85% of the essential digital data is dark or unstructured ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"hTdpn1nW","properties":{"formattedCitation":"(\\uc0\\u8220{}5 Things Every IT Manager Should Know About Dark Data | Walden University,\\uc0\\u8221{} n.d.)","plainCitation":"(“5 Things Every IT Manager Should Know About Dark Data | Walden University,” n.d.)","noteIndex":0},"citationItems":[{"id":83,"uris":["http://zotero.org/users/local/yvjivw9i/items/HAAGHIWK"],"uri":["http://zotero.org/users/local/yvjivw9i/items/HAAGHIWK"],"itemData":{"id":83,"type":"webpage","title":"5 Things Every IT Manager Should Know About Dark Data | Walden University","URL":"https://www.waldenu.edu/online-masters-programs/ms-in-information-technology/resource/five-things-every-it-manager-should-know-about-dark-data","accessed":{"date-parts":[["2019",4,26]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (“5 Things Every IT Manager Should Know About Dark Data | Walden University,” n.d.). The technological advancements and innovation have paved the path for low-cost solutions to storing and capturing the tremendous amount of information ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"Rs53MY2E","properties":{"formattedCitation":"(Kevin, Wanyaga, Kibaara, & Dinda, 2016)","plainCitation":"(Kevin, Wanyaga, Kibaara, & Dinda, 2016)","noteIndex":0},"citationItems":[{"id":78,"uris":["http://zotero.org/users/local/yvjivw9i/items/4ZRXM48A"],"uri":["http://zotero.org/users/local/yvjivw9i/items/4ZRXM48A"],"itemData":{"id":78,"type":"article-journal","title":"Dark data: Business Analytical tools and Facilities for illuminating dark data","page":"10","source":"Zotero","abstract":"The most important asset for any organization today is data. Organizations collect and store vast amounts of data every day relating to their various business activities. Understanding this data leads to better insights, less costs and risks and provides avenues in which the organization can improve its performance, offer better services to its customers and earn more revenue giving it a competitive advantage in the market. Advanced tools have been developed to gain this much needed insight into data that was previously considered irrelevant or inaccessible based on its unstructured form. These tools help an organization drill into its data and data from other external sources such as competitors, government reports, proprietary and other multi dimensional databases available from the internet to gain knowledge that can be applied to improve the organization’s competitive position. The aim of this research is to provide insights to organizations on how business analytic tools and software can be applied in lighting up previously unknown or ignored data. This is done through an in-depth analysis of secondary data and practitioner reports to provide an understanding of the various concepts and tools essential in identifying meaningful patterns and trends into an organization’s data.","language":"en","author":[{"family":"Kevin","given":"Njeru Mwiti"},{"family":"Wanyaga","given":"Felister Munyi"},{"family":"Kibaara","given":"David"},{"family":"Dinda","given":"Wilkister Atieno"}],"issued":{"date-parts":[["2016"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Kevin, Wanyaga, Kibaara, & Dinda, 2016). Despite the enhanced awareness and utilization of data analytics with big data, the demand for organizing and harnessing the perks of dark data has accelerated. Various studies have postulated feasible solutions as opening data and making it available for each person to explore and analyze. The bottom line is that efforts are been put to discover and conquer the paradigm of dark data.

References

ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY 5 Things Every IT Manager Should Know About Dark Data | Walden University. (n.d.). Retrieved April 26, 2019, from https://www.waldenu.edu/online-masters-programs/ms-in-information-technology/resource/five-things-every-it-manager-should-know-about-dark-data

Dark data: The two sides of the same coin. (2017, July 6). Retrieved April 26, 2019, from Analytics Magazine website: http://analytics-magazine.org/dark-data-two-sides-coin/

darkdata.org. (n.d.). Retrieved April 26, 2019, from https://www.darkdata.org/

Kevin, N. M., Wanyaga, F. M., Kibaara, D., & Dinda, W. A. (2016). Dark data: Business Analytical tools and Facilities for illuminating dark data. 10.