Strategies for data analytics projects in business performance forecasting: a field study
1University of St. Gallen, St. Gallen, Switzerland.
Journal of Management Control
|June 27, 2022
Summary
Implementing data analytics for business forecasting requires trust and strategic planning. Successful projects focus on a central controller and begin with achievable goals, expanding scope over time for sustained success.
Area of Science:
- Business Analytics
- Information Systems
- Organizational Behavior
Background:
- Companies increasingly use data analytics for business performance forecasting.
- Implementation challenges hinder the successful adoption of data analytics solutions.
- Lack of trust in analytics tools is a significant barrier to adoption.
Purpose of the Study:
- To identify critical success factors for data analytics implementation in business forecasting.
- To explore patterns of planning, implementation, and usage of data analytics.
- To understand the role of key personnel in fostering trust in analytics.
Main Methods:
- Cross-sectional field study approach.
- Qualitative analysis of company data analytics projects.
- Identification of implementation patterns and success factors.
Main Results:
- The head controller plays a pivotal role in building trust and ensuring project success.
- Three distinct patterns of data analytics implementation were identified.
- Successful patterns involve starting with limited, tangible objectives and gradually expanding.
Conclusions:
- Building trust through key personnel is essential for data analytics success.
- Phased implementation, starting with clear, achievable goals, leads to better outcomes.
- Strategic planning and user trust are key to leveraging data analytics for business forecasting.
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