If Data Is an Asset, Then Where Is Its Competitive Weight?
From Infrastructure to Impact: Rethinking Data’s Role in Competitive Advantage
Data is often referred to as the new oil, an asset class essential to competitive performance in the digital age (Laney 2017; Manyika et al. 2011). I have never been fond of that metaphor and for good reason. If we were to draw on a natural analogy, I would argue data is more like water: essential, flowing, constantly reused and often taken for granted. But whether we frame it as oil, water or something else, the deeper issue remains. Despite its rhetorical status as an asset, we have rarely treated it with the criticality or strategic care that genuine assets demand. The gap between how we talk about data and how we operate on it remains stark. While strategy drives capital allocation, capability prioritization and organizational design, data continues to be relegated to infrastructure, compliance or operational afterthought.
But what if we took the asset metaphor seriously?
An asset is something you protect, activate and leverage. It is not something you merely document, store or forget. Taking this framing seriously means locating data not just in systems but within the mechanisms of value creation. It means moving beyond completeness and asking instead: Where does data carry strategic weight?
The Strategic Context: From Resources to Return
The visual model clarifies this. Data exists within a broader architecture of value. At the base are Resources, the foundational infrastructure of systems, platforms, pipelines and governance protocols. These resources are made meaningful when informed by Organizational & Operating Model Literacy, which enables the enterprise to align resources with intent (Snow, Miles & Miles 2005). Without this literacy, resource investment risks fragmentation.
Next to resources sit Core Competences, the firm-specific data assets, unique knowledge graphs, proprietary models and semantic standards that constitute internal know-how (Prahalad & Hamel 1990). These assets aren’t merely technical; they’re deeply embedded in how the firm interprets its context, designs its workflows and differentiates itself. Competences give strategic coherence to data resources and direct their use toward relevant organizational outcomes.
Resources are allocated and competences specialized according to what the organization understands about itself, including its operating model, its decision rights and its strategic goals.
This literacy is not optional. It is the lens through which the organization defines the capabilities it must build. As Winter (2003) notes, capabilities are the high-level routines that determine how an organization can respond to environmental complexity. In this view, Organizational Capabilities represent the applied layer, including the decision-making, coordination and sensing mechanisms that deliver adaptive capacity.
These capabilities are enabled by competences and determined by strategy. Strategy here is not abstract. It is specific: a data-informed articulation of how the firm intends to compete. This is Corporate Strategy, and within a modern enterprise, it must be coupled with Data & AI Strategy, a strategic expression of how data will accelerate, shape and amplify the capabilities the organization chooses to scale (Ross, Beath & Mocker 2021).
The strategic flow is clear. Strategy defines competences, allocates resources and drives capabilities. From these emerge the potential for Competitive Advantage, defined not by static positioning but by adaptability, the ability to sense shifts, seize opportunities and reconfigure in dynamic environments (Teece, Pisano & Shuen 1997).
The Role of Data in Dynamic Capabilities
Dynamic capability theory tells us that long-term advantage does not lie in having the best product but in being best at evolving what you do and how you do it (Eisenhardt & Martin 2000). In this light, data becomes an enabler of adaptation, a strategic lever for learning, testing, reorienting and scaling with precision. This is where D&AI Adaptability becomes a competitive edge.
From this capacity to adapt emerges Economic Return, the ultimate outcome. But this return is not automatic. It is generated through the careful orchestration of literacy, assets, capabilities and strategy.
Implications for the Data Office
If this is the architecture of value, then the role of the data office is not to build a perfect data inventory. It is to ensure that data assets:
→ Reflect what the company chooses to be good at
→ Enable the capabilities that drive adaptability
→ Inform decisions that move strategic levers
This requires data teams to be embedded not just in platforms but in strategic dialogue. It means designing governance not for completeness but for relevance. It also means allocating quality, modeling and ownership effort in proportion to strategic importance.
A mature data organization doesn’t treat all data equally. It treats data as an amplifier of strategic choice. It operates with the understanding that:
Completeness ≠ Competitiveness
So it calibrates investment in data governance and enablement according to strategic value. It follows the priorities of the business, not the illusion of documentation completeness.
When data lives where strategy lives, embedded in core competences, mapped to operating model priorities and designed to power adaptive capability, then it earns the title of asset.
In mature organizations, data doesn't just follow structure, it helps shape the capabilities that corporate strategy chooses to scale and here the competitive weight is "Adaptive Intelligence".
Until then, we’re only indexing value, not creating it.
When was the last time your data priorities changed, because your business priorities did?
References:
Eisenhardt, K. M. & Martin, J. A. (2000). Dynamic capabilities: what are they? Strategic Management Journal, 21(10-11), 1105–1121.
Laney, D. (2017). Infonomics: How to Monetize, Manage and Measure Information as an Asset. Routledge.
Manyika, J. et al. (2011). Big data: The next frontier for innovation, competition and productivity. McKinsey Global Institute.
Prahalad, C. K. & Hamel, G. (1990). The core competence of the corporation. Harvard Business Review, 68(3), 79–91.
Ross, J. W., Beath, C. M. & Mocker, M. (2021). Designing Digital Organizations: How to Deliver What Matters Most. MIT CISR.
Snow, C. C., Miles, R. E. & Miles, G. (2005). A configurational approach to the integration of strategy and organization research. Strategic Organization, 3(4), 431–439.
Teece, D. J., Pisano, G. & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
Winter, S. G. (2003). Understanding dynamic capabilities. Strategic Management Journal, 24(10), 991–9
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