Decision-Making Under Uncertainty: The Strategic Value of External Data

A company’s performance largely depends on the quality of the decisions it makes, and the quality of decisions depends on the information available. Effective decisions allow the company to move forward with favorable conditions and, should circumstances change, to promptly assess a change in direction and adjust its course.

However, information is not free: acquiring it always requires time, resources, and organization. For this reason, many companies first focus on internal data, making use of business intelligence tools.

It is widely acknowledged that acquiring information from data produced directly by the company generally provides benefits that far exceed the costs. For this reason, business intelligence projects based on internal data are typically launched as soon as the company has the necessary organizational and financial resources for their implementation.

Less immediate, however, is the decision to incorporate external data into company information systems. In these cases, costs can be high and benefits more uncertain. Many decisions, however, require going beyond internal data: it is necessary to understand the market, the territory, and the competitors. In such cases, the company often faces a trade-off: precisely define the required information and bear the generally high costs of ad hoc market research, or check for the existence of secondary data that may contain the desired information and develop methods to extract it.

From Tariffs to Commodity Tensions: How to Extract Relevant Information for Business Decisions

In recent years, the availability of external data has grown significantly across many economic sectors. Today, there is an enormous amount of public and open data, which on one hand offers potentially valuable information, but on the other hand is often complex to use. This growing availability of secondary data makes their utilization increasingly strategic, shifting the focus to information extraction methods. In many cases, this approach represents the only feasible solution for small and medium-sized enterprises, which have limited resources to commission highly specialized market research.

This article focuses on two significant empirical cases of this decision-making challenge: determining pricing policies for an exporting company in the United States in response to rising tariffs, and defining procurement strategies amid possible fluctuations in supply prices and potential logistical challenges.

Pricing Policies for an Exporting Company Facing Rising Tariffs

A company exporting to the United States, following the “reciprocal” tariffs introduced by the Trump administration last April, faced a non-trivial choice: keep its prices to the importer unchanged, fully passing the tariff increase onto the final product price in the U.S. market, or absorb all or part of the tariff increase, reducing export prices to contain the price rise in the U.S. market.
During 2025, the company’s decision to adopt one strategy over the other likely depended on an assessment of the possible reduction in demand due to the price increase.

Economically, this means measuring the demand substitution elasticity in response to relative price changes. With elasticity close to zero, the company has no incentive to absorb the tariff increase: the quantity demanded likely remains unchanged even with a higher final price, making full cost transfer rational. Conversely, with significant elasticity, the price increase could lead to a substantial reduction in sold quantities, making it advantageous for the company to absorb at least part of the tariff.

Last spring, the decision problem was therefore strongly dependent on the availability of reliable information on the level of demand substitution elasticity. Conducting a specific market research study in the U.S. to quantify this parameter is costly and often unsustainable for small or medium-sized companies. Alternatively, the information can be extracted from foreign trade data collected by statistical offices of different countries through appropriate econometric estimates^[1].

In light of the ongoing turbulence in global trade rules caused by the Trump administration’s reciprocal tariffs, information on the potential substitution elasticity of its products with cheaper locally produced goods or those offered by other foreign countries is information that the management of all exporting companies should possess, at least as an initial hypothesis to be refined over time.

Procurement Policies Amid Price Variations and Supply Difficulties

Commodity prices reflect not only imbalances between supply and demand but, increasingly down the supply chains, production costs. Understanding the relationships between different commodities along the production chains allows anticipating how changes in feedstock prices may propagate through the supply chain.
For example, tensions—and now war—between the United States and Iran have been translated by financial markets into higher oil and gas prices. In this scenario, relevant information for a purchaser of steel or polyethylene is knowing to what extent the price shocks in oil and gas will impact the operating costs of steel and polyethylene and, consequently, their international prices.

Economically, this means measuring the cost pass-through along the various supply chains. Once the parameters of this mechanism are estimated, it becomes relatively easy to quantify the effects of rising Persian Gulf tensions on the prices of different commodities, starting from financial market reactions in terms of oil and gas prices. In this specific case, internal company data is almost entirely useless. Measuring cost pass-through can only be done using external data on market prices of different commodities, through appropriate econometric estimates to calculate the parameters.

Conclusions

The increasing availability of external data today makes it increasingly possible for a company to acquire, at a contained cost, relevant information to improve the quality of its decisions. In some cases, information extraction from available data is relatively straightforward, as in the analysis of the EU gas market described in the article What the Numbers Really Say About Gas in Europe. In other cases, such as the two examples developed in this article, information extraction is more complex and requires the use of more sophisticated statistical tools.

Nevertheless, extracting information from the growing volume of external data can be particularly fruitful and allow even companies with limited resources to significantly improve the quality of their decision-making.