Data analytics seems to be turning companies more customer-centric by allowing diverse department to organize around common "pools" of data. The promise of data-driven management also increases the complexity of organizations and management, as managerial responsibilities are disconnected from well-defined functions. Digitalization promises to enable managers and their teams to recognize and respond to a large number of issues and metrics that are not always compatible or aligned. For example, data on supplier reliability, customer satisfaction, production costs, and quality can trigger issues that are at odds with one other. While data and analytics allows an increasing number of different business processes to be optimised in parallel, coping with such increased complexity may require new managerial practices and skills.
The research is funded by the Academy of Finland. The project will run from Autumn 2014 to summer 2018.
Our initial research plan posed the following five questions:
What are the processes through which big data information systems are integrated and adapted to existing practices in strategy work?
What are the processes through which information and analyses generated through big data systems shape strategy work?
How does the use of big data systems influence the outcomes of strategy processes, such as chosen goals, the number of decisions made, the form of decisions (means/ends), and organizational commitment to decisions?
What is the implied ideology of management related to the big data solution (key norms, assumptions, and organizing principles)?
How do the ideological assumptions of big data systems relate to and potential contradict more conventional sources of legitimacy in strategy work?
Data and Methods
In order to understand the processes around big data, we study the practice of strategy work in three Finnish companies. We examine how managers at various levels of hierarchy use big data systems and adapt the pre-existing organizational practices. To elaborate intra-organizational processes, we collect primary data through non-participant observation and interviews.