Regarding theoretical implications, our framework contributes to big data and big data analytics (Akter et al., 2016, 2018; Wamba et al., 2017) and business failure research (Amankwah-Amoah, 2016; Zhang et al., 2018) by deepening our understanding of the relationship between these two important subjects. The study also advances the literatures by clarifying how lack of data analytic capabilities could lead to business failure in some industries. In addition, given that both empirical and conceptual papers are essential for fields of study to flourish, it is surprising that the number of conceptual papers is in sharp decline (Yadav, 2010). The dearth of conceptual articles has in many ways limited theoretical development and advancements. In this direction, this study also responds to the call for new streams of conceptual papers as a means of revitalising social science subjects and expanding the body of knowledge (Yadav, 2010).
24 The preceding analysis offers some practical implications. First, the analysis highlights that the era of big data requires strategic agility, where firms continuously scan the business environment to update their knowledge and adjust their strategy as new sets of data and information emerge (Doz and Kosonen, 2010; Dubey et al., 2018). Although achieving such strategic agility can be costly (Doz and Kosonen, 2010; Teece, Peteraf and Leih, 2016), it represents overall long-term efficient utilisation of resources given the savings from strategic blunders, errors, and misallocation of resources and organisational attention. By relying on obsolete technologies inhibit data capture for analysis, organisations are likely to sow the seed of their own demise.
Given that big data is viewed as “the next frontier for innovation” (Manyika et al., 2011, p. 1;
Akter, Wamba, Barrett, & Biswas, 2018), it offers promising opportunity for companies to harness data to help them compete. Cultivating BDAC requires cross-functional teams within the organisation to translate big data into actionable knowledge for activities with the firm.
In the atmosphere of increasing reluctance to embrace BDA, mobilising organisational support and evidence would be the most effective driver for more data-driven decisions. To overcome big data analytic capability deficiency, organisations can focus on human capital development through training and workshops to raise awareness of big data utilisation and benefits to help overcome some of the cultural barriers. To surmount some of the challenges around skills formation, firm-specific training might be needed given the divergent approaches to data analysis.
Over the course of this century, analytics might shift from a distinctive competence/capability to become a threshold competence given the improvements in modern data-science technologies.
Besides accumulating a superior big data, firms also need to match this with superior analytic capability to outwit rivals. Enhancing the data analytical capabilities is seen as essential in instilling problem-solving and evidence-based culture in organisations.
25 4.2 Limitations and directions for further research
Along with the key strengths of the paper are some limitations. The first relates to the assumption that business failure is “bad”. There is an accumulated body of research that demonstrates that failure has positive externalities (Knott and Posen, 2005). Besides freeing up resources and knowledge for surviving firms to tap, business failure offers an opportunity to learn from other firms (Knott and Posen, 2005). Another limitation has to do with the exclusive focus on using big data to identify early signals of underperformance and business failure. This fails to capture informal or unreported or underreported actions and behaviours which can also create conditions for business failure to occur. In addition, it is conceivable that this conceptualisation overlooks some aspects of big data and the complexity of the subject. Nevertheless, it offers a useful starting point towards integrating resources and capabilities in harnessing big data.
Notwithstanding the limitations, there are promising lines for future research. First, an interesting question for future research relating to Quadrant IV is how can firms so efficient in utilising big data renew themselves and not become complacent, thus leading to failure? Related to Quadrant I is a question of how firms enjoying sub-optimal performance can apply big data to leapfrog rivals and transition into Quadrant IV. It might also be worthwhile for future studies to explore how third-party organisations utilise BDA in advising and soliciting clients. The analysis also highlights the need for a more detailed analysis of how failure can be predicted using big data.
Finally, it is hoped that this piece helps to stimulate new streams of research on big data and business failure.
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