The following guest article was submitted by Mikheil Shengelia, Research Analyst at Eagle Alpha, an alternative data aggregation platform providing supporting advisory services for data buyers and vendors.
In response to the latest developments in trade policy, the team at Eagle Alpha have curated a dedicated Tariff Collection on our alternative data platform, featuring 17 high-value datasets across categories such as trade flows, consumer behavior, and supply chain dynamics. These resources are designed to help clients get an early read on how tariffs are impacting markets—from shifting import/export patterns to pricing pressures and manufacturing disruption.
For more information on topics relating to alternative data sourcing and compliance and to learn more about how Eagle Alpha is assisting financial institutions with their alternative data strategy, please contact us.
Background
In a dramatic week, President Donald Trump announced sweeping new tariffs on dozens of US trade partners, triggering a global economic scare and market crash. He declared a “Liberation Day” calling trade deficits a national emergency. President Trump’s tariffs were calculated in a highly unusual way: based on the size of the US trade deficit with each country, not on existing import tax levels or specific products. This meant that the larger the trade deficit, the higher the tariff — a method that alarmed economists and investors.
Businesses, lawmakers, and global leaders reacted negatively and even President Trump’s advisers clashed publicly. Amid backlash and fears of recession, President Trump abruptly paused most of the tariffs for 90 days, saying flexibility was key. China, however, was hit the hardest. President Trump kept a massive 125% tariff on Chinese imports which signaled a tougher stance toward the US’s biggest trade rival and increased fears of a deeper trade war.
Other major US trading partners like the European Union, Mexico, and Canada were granted a 90-day pause and are currently only subject to a temporary 10% blanket tariff. Originally, the EU was slated for a 25% tariff, with similar rates for Canada and Mexico. Japan, South Korea, India, and several Southeast Asian countries were also on the list for higher tariffs ranging from 24% to nearly 50%, but they too benefit from the pause—provided they haven’t retaliated.
The political message is clear: bringing production back onshore and reshaping trade relationships. But the market implications — ranging from inflationary pressures to global supply chain disruptions — are complex and developing fast. For asset managers, this is the time to start tracking the early signals, not waiting for official macro data or earnings releases.
What to Watch: Alternative Data Signals That Matter
At Eagle Alpha, we specialize in helping investors discover and apply alternative datasets that provide sharper, faster, and more granular insights. Here are some of the most relevant data types that can help you monitor and respond to the impact of new tariffs:
Economic Trade and Supply Chain Data
Economic trade datasets contain information on the size of specific commodities, volume, mode of transportation, and market value. This data monitors how all commodities and products are moved globally between countries. The data is compiled using a variety of different sources including online resources, government, public data, shipping firms, logistics companies, and transportation organizations.
The data can be used at an aggregated level for macroeconomic indicators, and it can also be used at a company level by looking at imports/exports using a harmonized system (HS) code – a standardized numerical code assigned to products by customs authorities to facilitate the classification and tracking of goods in international trade. The data can be used for pricing research, information on important importers/exporters, and insights into the market, sector, and country trade patterns.
The information can also be gathered from bill of lading (BOL) port data. A BOL is a document issued by a carrier to acknowledge the receipt of cargo for shipment. Historically the term only related to carriage by sea, a bill of lading today can be used for any type of carriage of goods. In general, there are between 10-20 years of data available in this category depending on the product or commodity.
A veteran in this space, S&P’s Global Trade Atlas covers monthly bilateral trade data for 105 countries—tracking goods at the most granular HS code level. In a recent spotlight, S&P reported a spike in Indian steel exports to Europe, driven by rising domestic prices and infrastructure demand.
S&P’s Commodities at Sea (CAS) is a high-frequency market intelligence service that tracks global trade flows of commodities transported by large tankers and bulk carriers. It provides near real-time data on cargo volumes, voyage details, and fleet activity across a wide range of commodities—from crude oil and LNG to iron ore, fertilizers, and metals.
The platform includes live vessel tracking, analytics on fleet behavior, and detailed trade flow data by commodity type, grade, region, company, and more. Click below to see Figure 1, which shows the impact of tariffs on commodity flows in North American markets.
Figure 1: US Tariffs to Shake Up North American Commodity Markets (S&P Global)
Air Freight Data
Air freight is associated with time-sensitive and higher value shipments representing around 35% of global trade by value. The type of goods shipped by air are typically smaller products, goods with a short shelf life like produce or flowers, and also goods that need a fast time to market like fast fashion.
In March 2025, air cargo demand increased by 5% year-on-year, following a slow but steady rise in the first two months of the year. However, the outlook for the industry is turning negative due to recent changes in tariffs and e-commerce policies, particularly the US’s re-imposition of restrictions on Chinese shipments. Despite a recovery in transpacific e-commerce demand, these new policies are expected to put pressure on air cargo demand starting in May, according to Xeneta. Click below to see Figure 2, which shows the impact of tariffs on the rates for air cargo.
Figure 2: Global Air Cargo Spot Rate Growth Slows to Single-Digit For the First Time in 9 Months (Source: Xeneta)
Consumer Transactions
Investors can track price sensitivity and margin pressure in real time by analyzing consumer and retail transaction data. This includes monitoring SKU-level shifts across key sectors impacted by tariffs, such as apparel, electronics, and autos. Additionally, point-of-sale (POS) data or credit card spend can help assess evolving consumer behavior, especially as shoppers adjust to higher prices or seek alternative products.
Web Traffic, Search & Sentiment
Alternative data sources like web traffic and social media sentiment provide deeper insight into how tariffs are influencing brand perception and consumer choices. Analysts can identify growing interest in non-Chinese brands or substitute goods, track sentiment around companies exposed to tariffs, and follow user engagement with direct-to-consumer (DTC) or e-commerce platforms that rely on international markets.
US Sector-Specific Signals
Many of the sectors targeted by recent tariff measures—such as manufacturing, semiconductors, and heavy industry—were already under economic pressure prior to these changes. Alternative data can help investors and analysts assess the impact by tracking key inputs like steel and aluminum pricing, monitoring trends in construction permits, production orders, and broader industrial activity. Procurement data and supply chain risk indicators from logistics partners can also provide early signals of stress or disruption.
Caveats and Considerations
Alternative datasets offer rich insights into supply chains, global trade flows, and the impact of tariffs—but they’re not perfect. Investors should be aware of the time lag in data reporting, especially in shipping data, which typically has a 4–6-week delay. Not all shipments are captured equally either: while maritime trade can be tracked with relatively high granularity (using HS codes, port data, TEUs, etc.), air freight data tends to be less detailed. Additionally, many companies intentionally redact their names or use third-party logistics firms, making it harder to attribute trade activity to a specific company. Sophisticated users often need to rely on product-level tracking or proprietary matching techniques, which introduces significant room for error.
If you’re looking for country-specific dataset recommendations or want to explore which datasets can support your tariff related investment strategies, please contact us.