Maximizing the Impact of Oncology Analytics and Insights Teams across the Biopharma Organization

Industries Profiled:
Medical Device; Biotech; Biopharmaceutical; Pharmaceutical; Manufacturing; Consumer Products; Diagnostic; Research; Consulting; Health Care; Clinical Research; Laboratories


Companies Profiled:
Alcon; Alkermes; Amgen; Arena Pharmaceuticals; AstraZeneca; Atara Biotherapeutics; Bayer; Bio-Rad Laboratories; Inc.; Biotest AG; Biotronik; Boehringer Ingelheim; B.Braun; Dermira; Edwards Lifesciences; GlaxoSmithKline ; Grünenthal; Janssen; Klosterfrau Healthcare Group; LEO Pharma; Lonza Inc.; MEDiSTRAVA; Merck; Merck Sharp & Dohme; Mitsubishi Tanabe Pharma; Momenta; Novartis; Pfizer; Qiagen; REGENXBIO Inc.; Sanofi; Santen; Scilex Pharmaceuticals; Seqirus; Servier; Sunovion; Takeda Pharmaceuticals; UCB Pharma; Vertex Pharmaceuticals

Study Snapshot

Best Practices, LLC engaged 44 Analytics and Insights professionals from 38 leading biopharma organizations in this research through a benchmark survey. Data is segmented by Oncology vs. Non-oncology companies and mid vs. large pharma companies. Deep-dive interviews were conducted with selected benchmark participants to provide further insights. Over 85% of research participants serve at the director level or above.

Key topics covered in this report include:

• Core responsibilities and clients supported by oncology A&I teams
• Valuable strategic practices, skills and expertise areas
• High-value analytics, methodologies, platforms and outsourcing approaches
• Streamlining market research via analytics
• Internal branding programs
• Innovation centers of excellence: Usage, staffing, services and alignment
• Top A&I obstacles, mistakes and lessons
• Innovative pilots, design thinking and patient engagement
• A&I support during COVID-19 and beyond

• Clients supported: A&I teams spend significant amounts of time supporting many customers beyond brands, including strategy planning, sales, franchises, market access and the C-suite. Oncology A&I teams spend relatively greater time supporting franchises and market access than non-oncology teams.

• Outsourcing levels: Data mining and statistics are primarily in-house processes in oncology, while A&I teams in non-oncology treatment areas collaborate with external sources for these activities.

• Percentage of time spent by Analytics & Insights groups in support of various internal teams – Oncology vs. non-oncology segment
• Percentage of time spent by Analytics & Insights groups in support of various internal teams – Large pharma and mid-pharma
• Voices from the field: Building holistic view for improving business results
• Role played by Analytics & Insights groups in fulfilling the listed organizational capabilities – Oncology vs. non-oncology segment
• Role played by Analytics & Insights groups in fulfilling the listed organizational capabilities – Large pharma and mid-pharma

• Voices from the field: Making consensual and collaborative plans and recommendations to clients
• Strategic practices used by Analytics & Insights groups to provide greater value to stakeholders – Oncology vs. non-oncology segment
• Effective practices used by Analytics & Insights groups to provide greater value to stakeholders – Large pharma and mid-pharma
• Interview narratives around staffing an analytics organization
• Preferred professional experiences and soft skills for Analytics & Insights professionals to have in order to deliver the highest value to internal partners – Oncology vs. non-oncology segment
• Preferred professional experiences and soft skills for Analytics & Insights professionals to have in order to deliver the highest value to internal partners – Large pharma and mid-pharma
• Most critical knowledge areas and technical skills for Analytics & Insights team members to have in order to provide maximum value as a strategy partner to internal partners – Oncology vs. non-oncology segment
• Most critical knowledge areas and technical skills for Analytics & Insights team members to have in order to provide maximum value as a strategy partner to internal partners – Large pharma and mid-pharma
• Interview narratives around what can drive the approach to analytics capabilities
• Interview narrative around analytics and different therapeutic areas
• Top obstacles to the future success of the Analytics & Insights function – Oncology vs. non-oncology segment
• Greatest obstacles to the future success of the Analytics & Insights function – Large pharma and mid-pharma
• Common mistake areas for the Analytics & Insights function
• Innovative ways to foster ongoing innovation in providing greater value to stakeholders

• Interview narrative around determining “Next Best Action” in promotional mix
• Analytic methodologies used by Analytics & Insights groups for different operational purposes – Oncology vs. non-oncology segment
• Analytic methodologies used by Analytics & Insights groups for different operational purposes – Large pharma and mid-pharma
• Interview narrative around predictive analytics in R&D
• Interview narratives around the outputs from new analytics platforms
• Interview narratives around data mining and usage with digitization
• Valuable analytics projects for informing brand, HEOR and other key internal partner teams and driving measurable results in the market – Oncology vs. non-oncology segment
• Valuable analytics projects for informing brand, HEOR and other key internal partner teams and driving measurable results in the market – Large pharma and mid-pharma
• Interview narrative around characterizing the activities that are enabling to connect the dots, in terms of analytic operations and methodologies performed
• Interview narrative around benefits of insourcing analytics
• Insource vs. outsource approach for analytics projects – Oncology vs. non-oncology segment
• Insource vs. outsource approach for analytics projects – Large pharma and mid-pharma
• Insource vs. outsource approach for specific methodologies and processes – Oncology vs. non-oncology segment
• Insource vs. outsource approach for specific methodologies and processes – Large pharma and mid-pharma
• Interview narratives around opportunities for more boutique research methodologies
• Interview narratives around approaches that tend to stifle innovation
• Use of analytics to replace listed market research study types – Oncology vs. non-oncology segment
• Use of analytics to replace listed market research study types – Large pharma and mid-pharma
• Benchmark peer assessments (pros and cons) of leading analytics platforms

• Structural challenges, different data sources, and interview narrative around Analytics & Insights from medical perspective
• Important objectives for Analytics & Insights internal branding program – Large pharma and mid-pharma

• Interview narrative around innovation centers of excellence
• Presence of an innovation CoE to leverage expertise and drive new improvements – Large pharma and mid-pharma
• Relationship between innovation CoE (or similar department) and Analytics & Insights groups – Large pharma and mid-pharma
• Relationship between innovation CoE (or similar department) and Analytics & Insights groups – Oncology vs. non-oncology segment
• Number of FTEs working in innovation CoE or similar departments

• Engaging directly with patients to better understand and meet their lifestyle needs relative to the disease state treated by the company’s medicines – Oncology vs. non-oncology segment
• Examples of patient engagement programs of benchmark partners

• Support provided by Analytics & Insights groups to internal stakeholders during the COVID-19 pandemic
• Key new opportunities and challenges that have emerged for Analytics & Insights groups as a result of COVID-19