The role of a predictive analytics project initiator in the integration of financial and operational forecasts
Huikku, Jari; Hyvönen, Timo; Järvinen, Janne (2017-07-11)
Jari Huikku, Timo Hyvönen, Janne Järvinen, (2017) "The role of a predictive analytics project initiator in the integration of financial and operational forecasts", Baltic Journal of Management, Vol. 12 Issue: 4, pp.427-446, https://doi.org/10.1108/BJM-05-2017-0164
© Emerald Publishing Limited 2017
Purpose: The purpose of this paper is to investigate the initiation of accounting information system projects. Specifically, it examines the role of the predictive analytics (PA) project initiator in the integration of financial and operational sales forecasts.
Design/methodology/approach: The study uses a field study method to address the studied phenomenon in eight Finnish companies that have recently adopted PA systems. The data are primarily based on 19 interviews in the companies and five interviews with the PA consultants.
Findings: The authors found that initiators appear to play a major role regarding the degree of integration of financial and operational sales forecasts. The initiators from an accounting function have a tendency to pay more attention to the integration than the representatives from other functions, such as operations and sales.
Practical implications: The study also makes a practical contribution to companies in showing and discussing the important role of the accounting department as an initiator of a project if the target is to achieve a tight coupling of financial and operational forecast figures, i.e., “one set of numbers”.
Originality/value: Even though companies have increasingly adopted PA systems in recent years, we still know little about how the initiation affects the design of accounting information systems overall. The central contribution of the paper, therefore, is to show that if a PA project is initiated by the accounting department, data integration becomes more likely. It contributes also to the discussion related to the appropriateness of data integration in the context of forecasting.
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