In an impactful article published by Harvard Business Review in 2012, Andrew McAfee et al. affirmed that “as the tools and philosophies of big data spread, they will change longstanding ideas about the value of experience, the nature of expertise, and the practice of management. Smart leaders across industries will see using big data for what it is: a management revolution. But as with any other major change in business, the challenges of becoming a big data–enabled organization can be enormous and require hands-on – or in some cases hands-o¬ – leadership. Nevertheless, it’s a transition that executives need to engage with today” (McAfee et al., 2012: 62). The management of data represents one of the most relevant challenges both for decision makers and researchers interested in the governance of social and economic dynamics (Yew Wong & Aspinwall, 2005; Espejo & Reyes, 2011; Barile & Saviano, 2011; Chen et al., 2013). Fast progresses in Computer Science are increasing the opportunities to acquire and manage big amounts of data in really short time and they are underlining the need for appropriate techniques and tools in the elaboration and use of data (Maimon & Rokach, 2005; Carayannis et al., 2006a, 2006b; Hey et al., 2009; Del Giudice et al., 2016) Almost any disciplinary domain has an interest in the management of data that is at the basis of information and knowledge (Beer, 1979; Espejo, 1996; Carayannis, 1999, 2010; Di Nauta et al., 2015; Del Giudice et al., 2016). However, the prevailing orientation appears to be more addressed to produce new data than to solve new problems (Linoff & Berry, 2011). It appears to be direct to product new (big) data to explain other data by giving rise to a circular dynamic that may not contribute to really solve social, economic or humans’ problems (Boyd & Crawford, 2012). This trend is well synthesized by Schumacher that, analysing the ‘problem of production’ in the modern era, wrote that “the changes of the last twenty-five years, both in the quantity and in the quality of man’s industrial processes, have produced an entirely new situation - a situation resulting not from our failures but from what we thought were our greatest successes” (Schumacher, 2010: 6). By reflecting on this assumption the following research questions emerge: In the pathway towards a smarter planet, what really makes new data useful for solving social, economic, or any kind of humans’ issue? Does the solution of humanity’s big problems require the collection of big data? Launching these provocative questions, the paper aims to analyse the pathway of knowledge creation as a process through which decision makers find solutions to new problems, highlighting that more information (hence more data) are useful to solve new (i.e. never experienced) problems only in the case in which the decision maker has the capability to correctly manage and interpret them (Barile, 2009a). A very attractive and surprisingly simple curve can show the dynamic of knowledge, which any of us can easily experience in any problematic situation of life, as a process of progressive shifting from conditions of chaos to complexity to complication to certainty: the knowledge “4Cs” curve (Figure 1) (Barile, 2009b). By adopting the interpretative lens of the Viable Systems Approach (Golinelli, 2010; Barile et al., 2013, 2016) and on the basis of a literature review on the topics of data and knowledge management, the paper highlights the key requirements of a knowledge creation process. More specifically, the paper underlines that any incoming unit of data collected to solve a problem commonly increases the variety by generating entropy and chaos. This process keeps on until something happens that support the effective understanding of the problem by directing towards a possible solution and/or explanation (Caputo et al., 2016). By suggesting to shift the focus from the quantitative management of data to the qualitative understanding of problems, the paper underlines the necessity to reinforce the decision maker’s understanding and cognitive capabilities through a ‘smart’ or even ‘wise’ use of information (Spohrer et al., 2017). In such a vein, the paper does not neglect the relevance of (smart) technologies to efficiently manage big data. It only monitors about the risk of abdicating the qualitative thinking role of humans delegating too much to technologies so losing the big(ger) power of humans’ wisdom.

Big vs Smart vs … Wise- the challenge of Knowledge Management in the XXI century

Saviano Marialuisa
;
Caputo Francesco
2017

Abstract

In an impactful article published by Harvard Business Review in 2012, Andrew McAfee et al. affirmed that “as the tools and philosophies of big data spread, they will change longstanding ideas about the value of experience, the nature of expertise, and the practice of management. Smart leaders across industries will see using big data for what it is: a management revolution. But as with any other major change in business, the challenges of becoming a big data–enabled organization can be enormous and require hands-on – or in some cases hands-o¬ – leadership. Nevertheless, it’s a transition that executives need to engage with today” (McAfee et al., 2012: 62). The management of data represents one of the most relevant challenges both for decision makers and researchers interested in the governance of social and economic dynamics (Yew Wong & Aspinwall, 2005; Espejo & Reyes, 2011; Barile & Saviano, 2011; Chen et al., 2013). Fast progresses in Computer Science are increasing the opportunities to acquire and manage big amounts of data in really short time and they are underlining the need for appropriate techniques and tools in the elaboration and use of data (Maimon & Rokach, 2005; Carayannis et al., 2006a, 2006b; Hey et al., 2009; Del Giudice et al., 2016) Almost any disciplinary domain has an interest in the management of data that is at the basis of information and knowledge (Beer, 1979; Espejo, 1996; Carayannis, 1999, 2010; Di Nauta et al., 2015; Del Giudice et al., 2016). However, the prevailing orientation appears to be more addressed to produce new data than to solve new problems (Linoff & Berry, 2011). It appears to be direct to product new (big) data to explain other data by giving rise to a circular dynamic that may not contribute to really solve social, economic or humans’ problems (Boyd & Crawford, 2012). This trend is well synthesized by Schumacher that, analysing the ‘problem of production’ in the modern era, wrote that “the changes of the last twenty-five years, both in the quantity and in the quality of man’s industrial processes, have produced an entirely new situation - a situation resulting not from our failures but from what we thought were our greatest successes” (Schumacher, 2010: 6). By reflecting on this assumption the following research questions emerge: In the pathway towards a smarter planet, what really makes new data useful for solving social, economic, or any kind of humans’ issue? Does the solution of humanity’s big problems require the collection of big data? Launching these provocative questions, the paper aims to analyse the pathway of knowledge creation as a process through which decision makers find solutions to new problems, highlighting that more information (hence more data) are useful to solve new (i.e. never experienced) problems only in the case in which the decision maker has the capability to correctly manage and interpret them (Barile, 2009a). A very attractive and surprisingly simple curve can show the dynamic of knowledge, which any of us can easily experience in any problematic situation of life, as a process of progressive shifting from conditions of chaos to complexity to complication to certainty: the knowledge “4Cs” curve (Figure 1) (Barile, 2009b). By adopting the interpretative lens of the Viable Systems Approach (Golinelli, 2010; Barile et al., 2013, 2016) and on the basis of a literature review on the topics of data and knowledge management, the paper highlights the key requirements of a knowledge creation process. More specifically, the paper underlines that any incoming unit of data collected to solve a problem commonly increases the variety by generating entropy and chaos. This process keeps on until something happens that support the effective understanding of the problem by directing towards a possible solution and/or explanation (Caputo et al., 2016). By suggesting to shift the focus from the quantitative management of data to the qualitative understanding of problems, the paper underlines the necessity to reinforce the decision maker’s understanding and cognitive capabilities through a ‘smart’ or even ‘wise’ use of information (Spohrer et al., 2017). In such a vein, the paper does not neglect the relevance of (smart) technologies to efficiently manage big data. It only monitors about the risk of abdicating the qualitative thinking role of humans delegating too much to technologies so losing the big(ger) power of humans’ wisdom.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4707801
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