As more companies move toward digitising their data, the potential to leverage this information for actionable business insights is growing. Like reserves of underground oil waiting to be tapped, these expanding stores of data hold great value for businesses, and there’s a temptation to think one could simply drop a pipe and bring the contents to the surface as quickly as possible. But just as a sound mining strategy is necessary to extract as much oil as possible from a given reservoir, a vast pool of digital data also requires a step-by-step approach to ensure that the data can be monetised effectively.
New business strategies, such as “decision architecture” are emerging to help organisations not only analyse their data but to help them make business decisions that the analytics will support. For example, a company can mine data based on a decision to increase overall ROI or to study a particular facet of the business, such as cash flow churn.
The next step in a data monetisation plan involves making sure your analytics are in alignment with your corporate objectives. Aligning decisions regarding key business drivers tightly with corporate objectives will ensure that the data that “hits the surface” is already on a clear monetisation path.
Rather than adding a layer of complexity, decision theory actually helps reduce the frictional path that data has to take in order to be commercialised. Applying a structure to the decision making process, where decision makers lay out anticipated actions, variables, outcomes, and potential rewards can help them see the big picture, gauge the impact of their actions, and avoid costly “guesswork.”
For example, Tungsten Network Analytics accesses data generated by payment processes, such as suppliers’ on-time delivery history, to help managers decide which suppliers can get goods to market more quickly. Those decisions can be correlated to other data points, like supplier discounts, so managers can choose which partnerships are more likely to increase ROI over time.