Big Data, Analytic Culture and Analytic-Based Decision Making Evidence from Australia

This study investigates how managerial decision making is influenced by Big Data, analytics and analytic culture. The results of a cross-sectional survey (n = 163) of senior IT managers reveal that Big Data Analytics creates an incentive for managers to base more of their decisions on the analytic insights. However, we also find that the main driver of analytic-based decision making is analytic culture. Considering that culture – in contrast to Big Data Analytics tools and skills – is a resource which cannot be change easily or quickly, we conclude that firms with a highly analytic culture can use this resource as a competitive weapon. Finally, our analysis reveals that managers in smaller organizations are significantly more likely to base their decisions on analytic results than managers in large organizations, which suggests the former use analytics to remain competitive against their larger counterparts

Impact of Big Data Analytics on Decision Making and Performance

‘Big Data’ has become a major topic of interest and discussion for both academics and professionals in the IT and business disciplines, and case evidence suggests that companies engaging in Big Data outperform others. It has to be noted though that ‘Bigger’ Data as such does not provide any benefits, but it is rather how organisations make sense of data and gain insights from analysing the data. Analytic capabilities and practices are required to convert Big Data (BD) into insights which arguably improve decision-making and thereby organisational performance. While protagonists of such Big Data Analytics (BDA) imply that those effects exist, so far they have not been confirmed by rigorous empirical research. Data was obtained using a cross-sectional online survey which targeted Chief Information Officers and senior IT managers of medium-to-large Australian for-profit organisations and yielded 163 complete responses, which met the standard criteria for measurement reliability and validity. PLS-SEM and multiple bootstrapping methods were used to test the hypotheses, while controlling for firm size. The present study empirically confirms claims made in the literature that BD and related analytics lead to better performance. It also reveals that such benefits are achieved primarily because BDA creates additional incentives for managers to base their decisions on analytics, and that more analytic-based decision making actually leads to superior performance. Finally, the results of our study suggest that managers in organisations which engage in BD are generally more analytics-minded in their decision making, even if the analytic tools and methods used in support of their decisions are not particularly sophisticated. The results provide evidence that neither Big Data nor Big Data Analytics are just ‘hypes’, but they do actually lead to superior performance, partly directly and partly indirectly by creating an incentive for managers to rely on analytics when making strategic or operational decisions. Interestingly, managers in smaller firms are more likely to base their decisions on analytics than larger firms, which suggests that they use analytics to compete against larger firms

Antecedents and Performance Impacts of Analytics-Based Managerial Decision-Making

This study investigates how organizations can achieve competitive advantage with data analytics. Two dimensions of the value creation process are investigated: (1) the input or antecedents of Big Data analytics (BDA) and (2) the mechanisms required to translate BDA investments into increased organizational performance. The results of survey responses from senior finance managers across a broad range of industries in Australia reveal that analytics-based decision-making (ABDM) is the main mechanism for converting analytic capabilities into competitive advantage. The technical, interaction and business skills of analysts are important antecedents of both BDA sophistication and ABDM, but the strongest driver of the latter business managers’ quantitative skills. We conclude that competing with analytics does not just require investments into analysts’ skills and related tools and IT architectures, but also into quantitative skills of business managers. Finally, our analysis reveals that managers of smaller organizations are more likely to base their decisions on analytics than those in large organizations

Management Accounting in the Big Data Era – Opportunities or Threats?

Over the past two decades, the digital revolution has brought along (a) ‘Big Data’, i.e. data which have rapidly become too big in volume, too diverse in nature and too rapidly changing to be handled in conventional databases and analysed using conventional tools, and (b) ‘data science’, “the study of the generalizable extraction of knowledge from data” (Dhar 2013), which develops and applies tools to manage and analyse (Big) Data. Data scientists are seen as new breed of managerial decision supporters, and insofar cross traditional management accounting territory. The aim of this study is to investigate the current and predict the future relationships between management accounting and the emerging data science discipline, based on a systematic analysis of the academic and practitioner literatures. While there is very little empirical evidence of an actual impact of data science on the management accounting profession, such impacts are predicted for the near future. Management accountants are expected to break with their traditions and collaborate with data scientists for mutual benefits. On the one hand, management accountants can be ‘data businesspeople’ or ‘horizontal data scientists’, who contribute essential business knowledge and data understanding to data science/Big Data projects. To succeed in such efforts, established and graduating management accountants face a need for up-skilling in technology, statistics, data mining, etc. and move into deeper analysis. Data scientists, on the other hand, can use their technical expertise to enrich established management accounting techniques and practices (e.g. the Balanced Scorecard, forecasting, etc.) with more advanced statistical or machine learning techniques