DESIGNING A DATA MANAGEMENT SYSTEM FOR STRATEGIC DECISION-MAKING
Abstract
Modern organizations invest substantially in data infrastructure, yet the quality of strategic decisions does not improve proportionally to these investments. A central reason for this gap is that existing approaches to designing data management systems are predominantly technology-driven and treat data as a neutral, universally applicable resource. This article challenges that assumption. It argues that data is not arbitrary - it is fundamentally determined by the strategic position a company already occupies: the value it creates, the customers it serves, and the assumptions it holds about the future. The purpose of the article is to develop a conceptual model for designing a data management system that is derived from and aligned with the company's strategic position, and to substantiate the logic of transition from strategic positioning through data governance and data management to a data architecture that enables high-quality strategic decision-making. The study employs a conceptual research methodology combining systematic literature review, critical analysis of existing frameworks, and constructive theorizing. On this basis, an integrative conceptual model is constructed and a diagnostic instrument is derived. The article makes four principal contributions. First, it establishes the principle of strategic determinacy of data: the domains of data a company requires are defined by the components of its strategic position - value proposition, target customer profile, competitive differentiation, operating model, and strategic assumptions. Second, it proposes a clear distinction between data governance and data management within a strategic context: governance is reframed as a strategic function that establishes intent, priorities, ownership, and definitions, while management is understood as operational execution of that intent through pipelines, storage, processing, and monitoring. Third, a four-level design model is developed - strategic position, data governance, data management, data architecture - with explicit mechanisms of connection between levels and a feedback loop through which strategic learning can revise governance priorities. Fourth, requirements for a strategically oriented data architecture are formulated, including temporal depth, external integration, hypothesis-testability, and interpretability, and a diagnostic matrix is proposed to assess the degree of alignment between an existing data system and the company's strategic position. The proposed model and diagnostic instrument provide managers and strategists with a structured approach to auditing, prioritizing, and redesigning data systems in alignment with strategic intent.
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Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152. DOI: https://doi.org/10.1145/1629175.1629210
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Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. DOI: https://doi.org/10.1016/j.lrp.2017.06.007
Ciampi, F., Demi, S., Magrini, A., Marzi, G., & Papa, A. (2021). Exploring the impact of big data analytics capabilities on business model innovation. Journal of Business Research, 123, 1–13. DOI: https://doi.org/10.1016/j.jbusres.2020.09.023
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Porter, M. E. (1996). What is strategy? Harvard Business Review, 74(6), 61–78.
Hambrick, D. C., & Fredrickson, J. W. (2005). Are you sure you have a strategy? Academy of Management Perspectives, 19(4), 51–62. DOI: https://doi.org/10.5465/ame.2005.19417907
Rumelt, R. (2011). Good strategy bad strategy: The difference and why it matters. Crown Business.
Christensen, C. M., Hall, T., Dillon, K., & Duncan, D. S. (2016). Know your customers' "jobs to be done." Harvard Business Review, 94(9), 54–62.
Schoemaker, P. J. H., Heaton, S., & Teece, D. (2018). Innovation, dynamic capabilities, and leadership. California Management Review, 61(1), 15–42. DOI: https://doi.org/10.1177/0008125618790246
Courtney, H., Kirkland, J., & Viguerie, P. (1997). Strategy under uncertainty. Harvard Business Review, 75(6), 67–79.
McGrath, R. G., & MacMillan, I. C. (2009). Discovery-driven growth: A breakthrough process to reduce risk and seize opportunity. Harvard Business Press.
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