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The nature of management accountants’ responsibility is changing from reporting aggregated historical value to also including organizational performance measurement and providing management with decision related information. The development in corporate information systems such as enterprise resource planning (ERP) systems has granted management accountants both data storage power and computational power. With big data extracted both internally and externally, management accountants now can utilize data analytics techniques to answer the questions including: what has happened (descriptive analytics), what will happen (predictive analytics), and what is the optimized solution (prescriptive analytics). However, research shows that the nature and scope of managerial accounting has barely changed and that management accountants employ mostly descriptive analytics, some predictive analytics, and a bare minimum of prescriptive analytics. This paper proposes a Managerial Accounting Data Analytics (MADA) framework based on the balanced scorecard theory in a business intelligence context. MADA allows management accountants to utilize comprehensive business analytics to conduct performance measurement and provide decision related information. With MADA, three types of business analytics (descriptive, predictive, and prescriptive) are implemented into four corporate performance measurement perspectives (financial, customer, internal process, and learning and growth) in an enterprise system environment. Other related issues that affect the successful utilization of business analytics within a corporate-wide business intelligence (BI) system, such as data quality and data integrity, are also discussed. This paper contributes to the literature by discussing the impact of business analytics on managerial accounting from an enterprise systems and BI perspective and by providing a Managerial Accounting Data Analytics (MADA) framework that incorporates balanced scorecard theory.
Deniz A Appelbaum, Rutgers University - Newark
Zhaokai Yan, Rutgers University
Alexander Kogan, Rutgers University - Newark
Miklos A Vasarhelyi, Rutgers University - Newark