A big contributor to this situation is that data in our industry has always been scarce and imperfect, so it was easy to ignore the messy part of it and instead have leaders fill gaps through their years of experience in the markets. As a result, the organizational capabilities to reevaluate perceptions via new analytic insights are not consistently embedded. And while data is still far from perfect (and likely will never be perfect), I believe that equipping leadership with the right skills to succeed in their increasingly complex roles and evolving the support infrastructure—that is, commercial excellence capabilities that go beyond operational support and reporting and enable true business partnership—will be critical components for driving more data- and insights-driven decision-making in an organization.


Here are three examples for how commercial leaders should use the magnitude of data available to inform strategic decisions, plus how to quantify the impact of these changes to build a convincing case for change: 

  1.  Portfolio strategy: We often encounter situations where an organization is dealing with a broad portfolio that includes both mature products with declining margins as well as various innovative growth businesses. In the absence of objective analyses, local management may fall into the trap of overfunding the mature business as it is the revenue driver today and, historically, it required significant resourcing to support customers. Instead, following a data-driven approach that leverages market and customer data to quantify the accessible potential, combined with insights on customer coverage and support needs, allows local leaders to make better decisions in how they allocate resourcing and enables regional leadership to understand which parts of the portfolio are underperforming in some markets while excelling in others. This insight can already trigger a valuable debate on reprioritization of resources or building an investment case for growth. 
  2. Forecasting/demand planning: This tends to be a highly manual and resource-intense process in many medtech organizations today, especially in Europe where it follows an iterative back-and-forth with countries yet still doesn’t provide consistency and confidence to region leaders. Establishing the data infrastructure and systems that enable standardized, transparent forecasting processes based on historical trends, pipeline insights and market dynamics helps significantly reduce the time that’s spent across many levels on forecasting today while at the same time increasing accuracy and consistency. 
  3. Pricing: While historically a pricing issue may have been described broadly as a margin challenge, a data-driven leader will strive for mapping out the potential sources of price leakage and conduct product- and country-specific analyses to quantify the impact of each of these areas to eventually pinpoint very tangible pricing improvement opportunities. In addition to addressing a specific pain point after the fact, the insights can then also help in building a case for stronger pricing guardrails during product launches, standardized tender bidding processes and support tools, or for implementing a system to monitor contract compliance to avoid unjustified rebates. 

I believe that we’ve arrived at a point where the analytic depth and rigor in how decisions are made should evolve. Part of this needs to happen by investing in systems and infrastructure to enable actionable insights, while at the same time a more fundamental change is required for how organizations set up their commercial excellence functions to become strategic partners to the businesses. I’m convinced that in a medtech world where data is abundant but still underutilized, those companies that can instill a data culture in their leadership team and build the infrastructure to capitalize on it will be at a significant strategic advantage.