To quickly redact, it has been shown that 80% of the data available as an unstructured form were generated from an increasing variety of sources like, mobile data, Facebook, twitter, blogs and emails; of which more than 70% data might have lost its value and become unimportant after 85 days (Aluya and Garraway, 2014). Undoubtedly, the major challenges confronting Information Governance (IG) have included highlighting data that has these business risks; finding, retaining and profiting from invaluable data; and eliminating the instances of irrelevant, redundant and useless data. Interestingly, it all fell under the domain or purview of the Data Analysts (DA), Data Scientists (DS) and Information Analysts (IA). Most importantly, business data analytical tools presently used falls under the umbrella of DA due to Technological Situational Happenstances (TSHs). See figure 1 below. Deft leaders within companies that have formed proactive IG programs, have had managers incorporating predictive coding technologies with subject matter experts to identify, tag, review workflow information in areas like data retention, classified information, data security, data archiving, data remediation, file transfers, fraud disposition and intellectual property (Leigh, 2013; Tamm-Daniels, 2013).

Figure 1: The collaborative role of DA, DS and IA in data analysis

Source: Aluya and Garraway, 2014a at the

Most significantly, the 21st century entrepreneur’s growth strategy has discovered methods to use data to gain more business value, unified intuition with insights from data through analytics via DA, DS and IA. According to Hacklin, Raurich and Marxt (2005), this predictable trajectory or technological pathways have resulted in the creation of Internet of Things (IoT) and Industrial Internet of Things (IIoT). For instance, the research marketing firm Acxiom in Britain applied analytics to help clients to assess returns on investment from the different channels of an advertising campaign in real time. Real time remains the operative two words here. This use of real-time data considered the short life span of the social media content harvested; as well as the practice of extracting contextual data to elucidate the possible reasons and procedures surrounding the data available (Bartram, 2013). Deft leaders in companies have discovered that organizational readiness was a significant part of the big data equation and have had to redefine business rules, thus affecting the chain of command.

Previously, the marketing mantra had been to market the right product on time, and at the right price for profit. Nonetheless, with practical analytical tools used in combination with DA, DS and IA collaborative efforts ensconced in the system, the InterContinental Hotels Group (IHC), for instance, showed professionals operating in the current hospitality industry the need for adjustment in their revenue management approaches as a result of the important data within the enclave of the big data and the use of analytics that have surged within the industry. Kelly (2010) quoted Craig Eister IHC’s Vice President of Revenue Management stating that “We’re selling a view or the fact that the room is not near an elevator. The complexity around inventory is changing”(¶ 5). Likewise, Eister noted the old approach to segmentation like grouping customers according to business or leisure classes no longer applied due to TSHs (Kelly, 2010).

With respect to the competitive advantage, the literature has noted the duplicitous nature of this phenomenon in that competitors could in fact gain the advantage by applying analytics to big data against the non-participating firms. To gain this advantage however, it has to be done through the understanding of effectively using DA, DS and IA. For example, Peoples Express Airlines was a highly successful and rapidly growing airline. The airline ceased to operate as a going concern in less than three months because it could not react to what the CEO called “sophisticated computer programs” that undercut the company’s prices. In all practicalities, the lesson learned from this case was that even though people skills, strategy and visioning continued to be important; deft leaders who overlooked the advantages of applying analytics to big data could find their businesses with no answer to the so called “sophisticated computer programs”. Without any schism, the sophisticated computer programs needed to be properly interpreted and implemented through combo of DA, DS and IA to extract the important data from the huge valued datasets for profit or better results. In addition, the dexterity of deft leadership plausibly expectation from management to be conversant and be constantly in tuned with the external environment and engrossed subordinates with any new technologies such as leading-edge analytics, whose practice could be a threat to the firm, remain critically important. Deft leaders who rely on instinct for strategic decision-making find it increasingly difficult to accept the latest concepts and innovations developing from practices like the combination of analytics and big data, especially the collaborative efforts of the DA, DS and IA. In the archetypes of deft leadership, these leaders were pigmented and regarded as bad or dysfunctional leaders. Accordingly, it would be advisable to design any new business processes with an eye towards instrumentation with better trained DA, DS and IA on board (Bell, 2008).



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