Big Data and Business Analytics

This contrast between KM/IC and CI provides a natural entry to a deeper discussion on Big Data and business analytics. A number of these topics should seem very familiar to anyone with experience with the latter. Big Data, by definition, is about massive amounts of data and information, the wider view of what intangible assets are valuable and the impact of Big Data comes through business analytics processes able to organize, analyze, and find higher level insights from these databases. Indeed, the Big Data, business analytics, and business intelligence area often references prior work in KM and these other areas (Bose 2009; Jourdan, Rainer and Marshall 2008).

For ease of reference, we’ll refer to the whole area as either Big Data or business analytics from here on out. The seeds of current excitement about business analytics comes from rapid increases in the power of modern IT systems. Substantially decreased costs for storage and processing, including in the cloud, have enabled organizations to save more and more data while doing more and more analysis of the resulting massive databases (Bussey 2011; Vance 2011b). Much of the data or information collected revolves around operations, supply chain, and channel performance; transactional and customer information; and communications (including social media) (Vance 2011a). According to a much-cited McKinsey Global Services report, Big Data adds value with greater transparency and more immediate feedback on performance, an ability to experiment in real time, provide opportunities for more precise segmentation, rationalized decision making, and generating new product ideas (Manyika et al. 2011).

Scholarly development in this new field is limited. One does see repeated mention of the three Vs (and sometimes additional Vs), referencing data volume, velocity, and variety (Laney 2001). All have increased with the drop in IT costs, allowing for the increased storage and higher level analysis already mentioned, potentially leading to better decision making at all levels (Beyer and Laney 2012). Metrics to date have generally centered on data storage (Manyika et al. 2011) and case studies are used to good effect in explaining the details (Liebowitz 2013). As we know, however, it’s not just the size of the databases that is important, it’s what’s done with them. Per KM/IC, CI, and related disciplines, the really valuable part of data and information comes from the higher level insights and the knowledge and intelligence derived from their analysis. A human element, through analytical techniques, is necessary to the process (Zhao 2013).

 
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