Academic journal article Issues in Informing Science & Information Technology

From Ignorance Map to Informing Pkm4e Framework: Personal Knowledge Management for Empowerment

Academic journal article Issues in Informing Science & Information Technology

From Ignorance Map to Informing Pkm4e Framework: Personal Knowledge Management for Empowerment

Article excerpt


Human evolution has not only thrived on big brain memory and communication technology with a high degree of accuracy, but also on an insatiable urge to use this technology for the purpose intended (Hughes, 2011). Consequently, the familiar problem of information scarcity (few sources/channels, high associated costs) has recently been transformed into a never before experienced ever-increasing information abundance (the total analog and digital distribution rose from 2.6 Petabytes with 1% digital content in 1986 to 0.3 Exabytes with 94% digital content in 2007 (Hilbert, 2014)) giving rise to the prominence to 'Big Data'.

Definitions of the latter can be differentiated based on a multi-disciplinary synthesis (sciences, humanities, policy, and trade literature) as follows: (i) product-oriented with a quantitative focus on data size, speed, structure, and/or composition; (ii) process-oriented with a focus on the processes involved in data search, collection, analysis, aggregation, storage, curation, and/or use; (iii) cognition-oriented with a focus on the way human beings, with their particular cognitive capacities and limitations, can relate to data; and (iv) social-movement-oriented considerations with a focus on utopian visions of what can be done and accomplished (Ekbia et al., 2015).

Even though the term 'Big Data' gained currency only after digital data volumes rose to the exabyte level, many of the associated epistemological, methodological, aesthetic, technological, legal, and ethical dilemmas originated much earlier but are now accelerating in scope, scale, and complexity--including issues of accessibility, interpretability, comprehension, and overload (Ekbia et al., 2015). Simon (1971), for example, pointed out way ahead of the digital revolution that the "wealth of information creates a poverty of attention" and, hence, that "progress does not lie in the direction of reading information faster, writing it faster, and storing more of it" but "in the direction of extracting and exploiting the patterns of the world--its redundancy--so that far less information needs to be read, written, or stored".

However, the scaling of the web with its searchability tools have afforded users to easily publish and unrestrictedly connect with other people and ideas (while the traditional book- design endeavors to contain all relevant information required within the book's topic to lessen the need for further inquiries). Any part of any content can now be disseminated unlimited times and does not necessarily stay unchanged as previously ensured by the physics of paper (making the web vulnerable as a storage device).

As a result, the ever-increasing abundance confronting us contains rising stakes of entropy: massive duplications of original content (redundancy), partial (fragmentations) or erroneous (inconsistencies) replications or deletions of records, non-disclosure or subsequent erasure of sources (untraceabilities), unsuitable alterations of content (corruptions), lacking curation and maintenance (decay), as well as outdated (obsolescence) and falsified statements (fake facts) (Schmitt, 2016j).

Additionally, we are experiencing a 'reverse engineering' of extelligence (referring to externally stored information (Stewart & Cohen, 1999)) and knowledge. Traditionally, knowledge is depicted as the third level in the traditional Data-Information-Knowledge-Wisdom (DIKW) Hierarchy (Rowley, 2007) or the fourth step in the 7-step Knowledge Ladder (North, Brandner, & Steininger, 2016); in the age of 'Big Data', however, a case can be made that this upward differentiation no longer holds since the digitizing and datafying of content transform existing extelligence and knowledge into sets of 'Big Data and/or Information' (exemplified by Word Clouds, Google Books, Semantic Web) ready to be analyzed for patterns and correlations (Mai, 2016). …

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