Conclusion, implications and limitations
What has been advocated in this article is not a revolution, but rather an evolution or adjustment and an extended answer to Moser’s question in the beginning of the paper. Inspired by trends in other business areas such as logistics and finance, it is argued that MAs and management accounting research should exploit the opportunities that the analytics movement offers and start developing theories and suggestions for fact-based decisions with a high external validity, i.e., research that will make our effort relevant for practice as proposed by many accounting researchers. In the future, business analytics with its variety of techniques to handle large quantities of data will pervade most traditional accounting fields and decisions such as e.g., product mix, make-or-buy, profitability, and pricing at the operational as well as at the strategic level. In business analytics, data will be an intangible asset on an equal footing with personnel, machinery and buildings (Davenport and Harris, 2007). The important point is that we have to focus on giving management accounting students and professionals the right skills in order to convince the companies that they can add value to the business; if not, other groups of professionals are ready to take over. In today's highly connected business environment, the pace of change is rapid and the pressure to keep up is quite overwhelming. Practical business is about the ability to change course fluidly and to react to changes in all areas of the business. Faster cycles of scrutiny of performance against expectations are increasingly demanded across all levels; from tactical to operational to the strategic level. Simple modelling approaches of yesterday will be replaced by holistic models based on advanced statistical techniques which will enable the decision maker to test even small changes to see changes in outcome. The MA must during all BA steps constantly remind him/her selves on what is the purpose, the context, possible alternative interpretations from alternative techniques, assumptions, experience and so on. Not only will big data and business analytics have impact on practice as discussed above, but it should also have impact on research. For example, predictive analytics could be useful for generating new theory, comparing competing theories, improving existing theories, assessing the relevance of theories, or assessing the predictability of empirical phenomena (Schmueli and Koppius, 2011). The idea is to produce new and interesting solutions for accounting decisions – now with a data-related and holistic view as the starting point. For practice, such fact-based research would also be of high interest. The gap between research theory and practice that has been discussed since the 80s could be reduced by taking a business analytics approach.