Big data systems: knowledge transfer or intelligence insights?

Abstract

Purpose This paper aims to bring together the existing theory from knowledge management (KM), competitive intelligence (CI) and big data analytics to develop a more comprehensive view of the full range of intangible assets (data, information, knowledge and intelligence). By doing so, the interactions of the intangibles are better understood and recommendations can be made for the appropriate structure of big data systems in different circumstances. Metrics are also applied to illustrate how one can identify and understand what these different circumstances might look like. Design/methodology/approach The approach is chiefly conceptual, combining theory from multiple disciplines enhanced with practical applications. Illustrative data drawn from other empirical work are applied to illustrate some concepts. Findings Theory suggests that the KM theory is particularly useful in guiding big data system installations that focus primarily on the transfer of data/information. For big data systems focused on analytical insights, the CI theory might be a better match, as the system structures are actually quite similar. Practical implications Though the guidelines are general, practitioners should be able to evaluate their own situations and perhaps make better decisions about the direction of their big data systems. One can make the case that all the disciplines have something to add to improving how intangibles are deployed and applied and that improving coordination between KM and analytics/intelligence functions will help all intangibles systems to work more effectively. Originality/value To the authors’ knowledge, very few scholars work in this area, at the intersection of multiple types of intangible assets. The metrics are unique, especially in their scale and attachment to theory, allowing insights that provide more clarity to scholars and practical direction to industry.

History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations

“…A closely connected third cluster (N = 40) focused on how knowledge and information can be strategically developed, managed and leveraged in organizations (e.g. Erickson and Rothberg, 2013), and the role of BDA therein (Rothberg and Erickson, 2017;Tsui et al, 2014;Wang et al, 2013). Next, five smaller clusters were identified.…”

Research streams on digital transformation from a holistic business perspective: a systematic literature review and citation network analysis

“…On the other hand, Murray et al (2016)as well as Uden and He (2017) take a look at IoT devices and how they can enhance knowledge management systems because of the data that are automatically generated. A strict theoretical view can be found with Rothberg and Erickson (2017), who mean to bring together the existing theory from knowledge management, competitive intelligence and BD analytics. One article is quite critical of the use of BD and elucidates that "to describe it [BD in the context of knowledge management] as 'revolutionary' is premature" (Tian 2017, p. 113).…”

Exposición Temprana de Nativos Digitales en Ambientes, Metodologías y Técnicas de Investigación en la Universidad

“…Para intentar precisar términos como datos, información y conocimiento, que utilizaremos con frecuencia más adelante, hacemos uso del modelo DIKW (por sus siglas en inglés de Data, Information, Knowledge and Wisdom) propuesto por R.L. Ackoff en 1989 [14] y que se ha convertido en propuesta clásica y controvertida de la fundamentación teórica para la gestión del conocimiento ( [15,16] y las referencias allí citadas). Un esquema simplificado del modelo DIKW y las transiciones que se dan en la producción de datos, información, conocimiento e inteligencia, se puede resumir como [17]:…”