Middle-Market Firms Hit Reset on Data Analytics Strategy

If businesses can weather storms, they can win wars. These days, weathering the unknown is what it takes to survive.

Rising geopolitical tensions, fluctuating trade policies, and inflationary pressures have upended the predictable operating environment many mid-sized firms once relied on. These companies, often considered the backbone of national economies, are now treating trade policy volatility not as a peripheral concern but as a central strategic threat.

Their weapon of choice in navigating uncertainty? Advanced analytics. To longtime observers of the space, this might come as a surprise. After all, historically middle-market firms, defined as companies with annual revenues between $10 million and $1 billion, have been reactive. Their focus was inward: cost control, talent acquisition, and supply chain execution.

But the latest PYMNTS Intelligence from the June 2025 Certainty Project report, “Caught in the Crossfire: Middle-Market Firms Battle Policy-Driven Uncertainty,” reveals that shift is underway, one that marks a watershed moment in how these firms prepare for economic shocks. The new mid-market playbook doesn’t just hinge on efficiency or market share; it revolves around resiliency, foresight and the ability to pivot in real time.

Force Multiplier for Signal-Finding

Businesses can’t afford to be surprised any more. They need to know what the world will look like if tariffs jump 10%, or if a key port shuts down for three weeks.

Against the thicket of unpredictable tariffs, shifting trade agreements, and macroeconomic uncertainty, advanced analytics and modeling tools have become a necessity, not a luxury, for mid-market firms.

The report found these firms are treating trade policy and uncertainty as an external shock they must proactively model, with nearly 1 in 5 (18%) having embedded scenario planning and machine learning tools into their strategic planning.

What makes this trend particularly notable is not just the adoption of new tools, but how they are being deployed. It’s not just about dashboards and reporting anymore. Firms are operationalizing insights. They’re running simulations that assess how a 5% swing in steel prices might affect margin. They’re modeling inventory levels under different demand forecasts tied to geopolitical shifts. And they’re even using machine learning algorithms to identify which suppliers are most likely to be impacted by new trade policies or export controls.

The effect is compounding. Firms that once relied on historical performance and gut instinct are now using predictive models to pressure-test their business decisions.

Read the report: Caught in the Crossfire: Middle-Market Firms Battle Policy-Driven Uncertainty

This transformation is made possible, in part, by the democratization of analytics platforms. Where once only deep-pocketed conglomerates could afford robust business intelligence tools, cloud-based platforms and SaaS models have brought them within reach for mid-market players.

Companies like Tableau, Power BI, and Looker have lowered the barrier to entry for visualization and analytics. At the same time, APIs from global data providers like NielsenIQ, UN Comtrade, and S&P Global have enabled mid-tier firms to ingest real-time intelligence without building massive data infrastructure.

Five years ago, a mid-size logistics firm couldn’t afford a dedicated analytics team. Now they can plug into a global dataset, overlay their sales metrics, and run a scenario in under an hour.

This convergence of technology and usability is flattening the analytics curve. It’s also accelerating adoption. Among the most digitally advanced middle-market firms, data-driven forecasting is becoming core to every major decision — from sourcing to pricing to marketing.

Still, not all middle-market firms are moving at the same pace. Many face real challenges: data fragmentation, skills shortages, and the lingering perception that analytics is a “nice to have.”

Additionally, overreliance on data can create its own vulnerabilities. Models are only as good as the assumptions behind them. And in a world shaped by black swan events like pandemics, geopolitical unrest, supply chain collapses, and more, no model is perfect.

But one thing is certain: Foresight can be a competitive advantage.