shutterstock_675251539-445614-editedArun Jain co-wrote this blog post with Arup Das.

Imagine a world where oncology analytics transform the way that a pharma manufacturer engages with its customer: Sales reps have real-time insight into where patients are being diagnosed. Accurate predictions help them anticipate when to follow up with a customer with a relevant message about their soon-to-relapse patient. Customers give them unencumbered access because they trust that the manufacturer will engage with them through their preferred channels at the right cadence. Does this sound too good to be true? Some oncology companies are already exploring these possibilities today, but according to ZS’s recent benchmarking study of oncology analytics organizations, many are not there yet in their analytics maturity and are missing out on critical opportunities to engage their customers when it matters most.

Analytics groups within an organization support a range of stakeholders with data- and research-driven insights to make business decisions ranging from strategy, planning and execution to understanding daily business performance. A best-in-class analytics group delivers competitive advantage to an organization by making analytics a strategic differentiator. It also delivers richer customer insights in a timely manner, and supports better adoption and decision-making. However, oncology differs from other therapy markets when it comes to analytics needs. Cancer is a progressive disease that requires sophisticated patient data analytics, and a plethora of newer data requires better integration. Moreover, every tumor is a disease area within itself, requiring analytics customization for each indication. Since treatment mostly happens in a hospital setting, execution is still very HCP- or influencer-focused, requiring level normalization between execution and treatment. Lastly, emerging specialized distribution requires more and more KPIs to worry about.

Given the complexity involved in oncology treatments, the constantly evolving market landscape and lack of a “perfect” data source, it’s imperative that pharmaceutical and biotech organizations with oncology portfolios grow the maturity of their analytics to accelerate value creation. However, the industry lacks a holistic perspective on what dimensions define the analytical maturity of oncology organizations. So what makes a company analytically sophisticated, and what constitutes the best-in-class practices for oncology analytics?

Our findings from the benchmarking study, which surveyed commercial analytics and operations groups within nine leading U.S. oncology companies, revealed two key themes for success:

  • Organizations with a defined vision and strategy for oncology analytics make proactive investments to build capabilities around people and processes.
  • Technological capabilities and data investments are precursors for increased analytical sophistication to explore and solve complex problems.

To understand your organization’s current level of analytics maturity, ask yourself the following questions across five dimensions:

  1. Organization design and mindset: Does the organization have the vision and culture to create differentiation or transformation through analytics?
  2. Capabilities: Does the analytics organization have the people, processes and tools in place, and is it empowered to carry out its mission?
  3. Data and information: Does the organization centrally, strategically and purposefully define data requirements, source data and link multiple sources to generate insights?
  4. Sophistication: Is the organization using analytics to provide robust and granular insights to support decision-making?
  5. Technology infrastructure: Are your technology platforms mature enough to deploy centralized data lakes and adopt big data capabilities?

oncology analytics chart

Our study also identified a range of practices from best-in-class companies, and tips for how a company can improve its analytics maturity:

  • Develop an oncology analytics agenda aligned with business needs, and align analytics priorities with resources and capabilities centered around the agenda.
  • Take a programmatic approach to experiment and test new ideas, keeping an eye on the external trends in a fast-changing oncology marketplace.
  • Enable self-serve analytical capabilities to democratize basic, routine analytics and reduce turnaround time.
  • Obtain access to granular data including lab data, patient attributes from physician charts and unstructured patient notes. This will help organizations improve customer characterization, establish patient micro segments, and take a step toward precision medicine.
  • Explore AI, machine learning and process automation techniques for commercial oncology problems by embedding analytics in execution.
  • Make a significant investment in a cloud-based data lake to centrally manage all oncology data and provide analytics-ready, “single truth” data to the organization.

Every oncology company’s level of analytics maturity will depend upon the business needs of its portfolio, the overarching vision for its brands, and the appetite for change. No matter the maturity level of your analytics group today, there’s always an opportunity to improve. Where does your oncology analytics organization lie on the maturity scale? Are you best in class yet?


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Topics: oncology data, oncology analytics, analytics maturity, data and analytics, customer centricity, customer targeting, oncology customer experience, decision making, value