In our recent “Leveraging AI* for built asset Digital Twins – Turning scattered data into strategic assets” webinar, we emphasized the foundation formula (Jeffrey Ding & Allan Dafoe) for strategic asset management, ensuring the transformation of data into valuable strategic assets. This formula is a cornerstone of our approach to managing and leveraging data for optimal asset utilization, especially in the realm of built asset digital twins.
The formula from Jeffrey Ding & Allan Dafoe:
Strategic level of asset = Importance * Externality * Rivalry
1. Importance: This aspect assesses the operational impact if the data is unavailable or temporarily inaccessible. The value of data is gauged by the consequences of its absence, ranging from negligible to critical. For instance, if the unavailability of data could potentially lead to significant operational disruptions or financial losses, its importance is deemed high.
Data traceability is a key driver for built asset knowledge retention and, as such, drives the ‘importance’ factor. Also, explainable AI, through the added knowledge acquired from deductive reasoning from small data, is a key driver for the ‘importance’ factor.
2. Externality: This factor examines the data’s sensitivity to external influences. It’s about understanding how external factors, which are often beyond an organization’s control, can impact the data. This includes evaluating whether the structure or organization of the data is influenced by external entities or standards.
Open standards could initially be considered an expensive external dependency, but considering collaboration in an extended enterprise context leads us to consider open standards as facilitators rather than potential ‘knowledge drains’. The real threat would indeed be being forced to implement externally defined data models, using open standards as stability providers, and lowering the threshold to exchange information structures with external organizations. Counter-intuitively, sharing common standards is an avenue to maximize the strategic level of the data asset through its tamed externality.
3. Rivalry: The third element of the formula, rivalry, refers to the exclusivity or shared nature of the data. It assesses how exclusive the data is to the organization and its implications. The more exclusive the data, the higher its potential to provide a competitive edge.
Rivalry can be maximized for datasets when their data structures, or schemas, are shared via open standards. KPIs from consolidated data are indeed strategic decision-makers when their rivalry is maximized.
This formula is not just a theoretical construct but a practical tool we use at Eurostep to quantify and enhance the strategic level of assets through data. It allows us to systematically evaluate data’s impact, dependencies, and value, ensuring that our data management strategies are aligned with the broader business objectives of our clients.
By evaluating these formula terms, we can identify which data sets are most critical to an organization’s operations, how vulnerable they are to external changes, and how they can be leveraged for competitive advantage. This comprehensive approach to data assessment underpins our strategies in asset management, ensuring that every data element is effectively utilized to drive informed decision-making and operational excellence.