iStock_000007985247SmallRecent launches have proven that having a great product isn’t enough. To succeed in the oncology market, pharmaceutical and biotech companies must be able to understand performance and opportunities, and explain the difference for increasingly specific patient populations. Data has come a long way, and integrated data analytics is a powerful way to surface and interpret an oncology product’s opportunity amid brutal competition.

In 2013, 19 oncology products were released; today, over 2,000 are in development. We often see two or three products entering the same market. And with precisely targeted therapies, new oncology products must often identify and serve smaller markets.

ZS Associates is empowering our oncology clients to work with their market’s data across a range of business and infrastructure needs. Four examples from the past year:

  • Defining how “big data” can give insight into the context of patients, physicians and institutions
  • Integrating primary and secondary sources for enhanced patient type and treatment pathway insights
  • Using significant data advances to better prioritize resources for oncology products
  • Deepening existing data infrastructure to capture and explain the nuances of oncology

Such examples underscore our perspective that oncology data is improving, with greater degrees of subtlety, and now requires more situational adaptation and integration. True, it may never provide the holistic view of the primary-care market, but advanced analytic techniques are providing a more robust picture.

To provide a framework for organizations aiming to evaluate different types of data sources in oncology today, ZS recently released the white paper “Oncology Data: From Uphill Battle to Decision Driver.” In it, we advocate for an advanced data perspective and proactive plan to tap its full potential.

A comparison of primary-care and oncology markets demonstrates how much more complex oncology’s data situation is. For example, a primary-care cholesterol drug is typically used as a single agent, administered orally and refilled at the pharmacy.

Conversely, an oncology product might have multiple indications; be used in combination with other medications; be discontinued quickly because of side effects; be used off-label; or have other application complexities. Oncology drugs can be administered orally and infused in office, and have a more variable distribution.

As a result, the oncology market’s data needs are deep, often deeper than the individual sources. In practice, some organizations have achieved less-than-ideal execution. Targeting and market share calculations are two examples of common business analyses that can and should be looked at more holistically using multiple sources and lenses to optimize the results. With increasing competition, resting at the surface of what data can really do is no longer an option.

Given such challenges, “Oncology Data: From Uphill Battle to Decision Driver” explains how oncology data is improving, while acknowledging the difficulties in keeping up with the changes. Organizations must not only establish the infrastructure to evaluate, store and use the data, but develop and quickly apply the right analytical techniques to stay ahead of the competition.

For the full picture, download the complete white paper.

Topics: big data, data management, oncology, Analytics, Maria Whitman