Management accounting applying data analytics : A case study of a cloud-ready analytics platform provider and its customers
Lehtinen, Pilvi (2018-05-22)
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Digitalisation has transformed organisations’ operations and their decision making during the last decades. The new digitalisation enabled technologies have created new systems and organisational functions, such as Business Intelligence (BI) and BI specialised teams consisting of inter alia data scientists. The changes have also influenced organisations’ traditional functions, such as the financial administration including management accountants. Data analytics has emerged as one of these new digitalisation triggered systems that aim to provide useful information for decision-makers through statistical and quantitative analysis and explanatory and predictive models (Schneider, Dai, Janvrin, Ajayi & Raschke 2015, 720). It has been debated whether data analytics and other BI systems can add value to organisational decision making, or whether organisations have been applying such systems only to follow the current trends in the market and, thus, at its worst lose their strategic and operational focus in decision making in the big data and various analyses. It has also been discussed whether the increasing amount of automatization and robotization will ultimately replace the need for various professionals, such as management accountants (Bhimani and Willcocks 2014; Quattrone 2016). Therefore, this study aims to find out under which conditions BI and data analytics can support decision making and bring value to management accounting and its systems (MAS). The chosen approach to study this phenomenon is a multiple case study, where a data visualisation analytics provider and three of its customer companies were interviewed for providing explanations on BI analytics, their effects on professionals’ roles and decision making. The findings of this Master’s Thesis are that when BI analytics are implemented only when they truly adding value on decision making and they are well integrated throughout the organisation, based on the organisation’s conditions framed by its contingency factors, they will indeed contribute to improved decision making. A new version of the Machine Analogy (Burchell, Clubb, Hopwood, Hughes, Nahapiet 1980) in the BI analytics environment is proposed. It highlights how BI analytics systems can operate as an Answer Machine only to some of the most simplistic operational decision making, but for the more complicated strategic decision making human judgement cannot be replaced by systems and their computations. Thus, it is suggested that when organisations are applying BI analytics to their MASs, they use their BI systems in strategic decision making as Learning Machines or Ammunition Machines and in operational decision making settings Learning Machines, Ammunition Machines or in the simplest scenarios Answer Machines.