Our stance has always been that, in addition to revamping its business strategy, medtech needs to become analytically-driven. Medtech companies must leverage their data and analytics to optimize their business strategies and enable growth in ways that are consistent, continuous and agile.


It’s with this theme that we organized this year’s Medtech Connect, a one-day event that annually brings together decision-makers from commercial operations, analytics and IT to engage in several discussions conducted by ZS experts on topics that speak to becoming analytically driven. This year’s event was replicated at Boston, Chicago and San Francisco to foster local communities of commercial analytics practitioners from the medtech industry.


As an organizer and presenter, it was great for me to witness the high level of engagement, the humility and the growth mindset of the clients who attended the event. As I listened to the discussions and presentations, I took four things away from this event:

  1. Becoming analytically driven requires clarity of purpose, outcome and approach. While everyone across healthcare is talking about becoming analytically driven to advance innovation and better patient outcomes, only 2% of companies had yielded transformative impact when ZS recently studied the issue. Successfully transforming into an analytically driven organization requires clarity around why you want this change, what it means to be driven by analytics and how you can make it happen. Why become analytically driven? Products and traditional sales boosting alone won’t adequately differentiate you as the market continues to shift. You need analytics to give your organization a competitive advantage. What does it mean to be analytically driven? It’s about making micro decisions rapidly with reasonable accuracy based on data-driven insights, even if that means going against your intuition. Multiple micro-adjustments add value quickly, and over time can add up to much more value than a “big bang” approach. How do you make this change happen? It requires more than just data and new capabilities. It requires a change in organizational mindset, business capabilities, operating model and business processes, and change management. Organizations need the capability to act on the meaningful insights they develop by believing the data that they see, knowing what to do with the insights and finally acting on what they know.
  2. Analytics isn’t just about finding a solution; it’s about defining the problem. Typically, the biggest challenges leaders face to optimizing their analytics capabilities are not with the analytical execution, but with problem definition, the solution approach and adopting and acting on insights. In choosing a problem to solve, it’s critical to understand how important the problem is for the organization and its leadership. It’s also important to determine the value of solving a given problem and whether its complexity is in line with current analytics maturity to avoid disappointing setbacks in implementation.

    Pursuing a solution entails that you understand the problem category, whether it’s to enrich insights, optimize decisions or enable actions. Then, choose the right set of AI and machine learning (AI/ML) algorithms, and finally, choose the right data sets to train your algorithms. Keeping and building an algorithm library over time is a critical success factor for any analytics organization and watching out for many known solutions available from open source libraries, industry blogs and AI/ML providers speeds up the process tremendously.
  3. There is no dearth of analytics use cases for proving value. There were many successful, real-life case studies shared during the sessions. These organizations followed the above approaches and saw revenue improvements. Some of the examples, such as predicting customer churn, improving targeting based on patient referral patterns, prescribing a set of next best actions for the sales reps to improve sales and predicting sales rep attrition were very well-received. One use case that stood out to me was about improving profitability through better pricing and contracting analytics. Organizations that have leveraged advanced analytics in this way can create optimal deals and proactively monitor compliance. Another that stood out was about optimizing sales force deployments on an ongoing basis to reduce operating expenses. Both use cases positively impact the bottom line.
  4. Medtech needs to think of data as a product and manage it as one. Many healthcare leaders have alleged that data and analytics will be the single most important source of medtech innovation and differentiation in the next five to 10 years. About 2.3 zettabytes (2.3 trillion gigabytes) of healthcare data will be produced and managed by 2020. This focus on data in the coming years, claims Stanford school of medicine, has the potential to make healthcare more preventive, predictive and personalized. It will meaningfully reduce healthcare costs and lead to better patient outcomes. A vast array of evolving data sources is available for medtech, which creates possibilities of novel commercial interventions. This ecosystem has given rise to a new term called  “plecosystem” a multi-platform healthcare ecosystem consisting of platforms, data generation, APIs, user experience and financial and socio-cultural data. Medtech is directly or indirectly trying to influence many stakeholders. Its decisions in a complex healthcare ecosystem while relying solely on internal data can lead to missed opportunities to influence stakeholder decisions across this complex transaction model. So, how can you remove this blindfold and what are these opportunities that you stand to capitalize on if you can successfully harness the power of both internal and external data? This can happen when medtech starts to think about data as a product, and includes sourcing guidance, standards, application, and rules and life cycle management. A product mindset is not new for medtech, however, applying that mindset to data is new and requires new strategies and capabilities. To solve for these challenges and drive business impact, organizations must think about data strategically, including designing a data charter, a data acquisition strategy, data governance processes and new roles and skill sets beyond those in technology and infrastructure.

Every presentation ended with the speakers urging their peers to act. And I understand why. It’s clear that changes in the market necessitate an analytics-driven approach. It’s also clear that there’s a learning curve involved in making this transformation, with some common pitfalls to overcome along the way.  It’s also clear that the benefits are many and even small changes that can be made quickly will have a large impact. One of the presenters had an equation that really drove this point home. They showed how even a 1% improvement daily can represent a substantial improvement over the previous year. To illustrate this point, they shared two alternative formulas: (1.00)365=1.00 and (1.01)365= 37.7. Enough said! All these facts add up to one simple conclusion: the time for us to become analytically driven is now.