1.Executive summary
2.Research objectives
3.Research methodology
4.Experts interviewed
5.Definitions
6.The growing power of Big Data over pharma's commercial future
7.The commercial potential of Big Data and analytics
7.1 Key insights
7.2 A wealth of information available
7.2.1 Sub-segmenting patients based on genomics and personalised medicine
7.2.2 Segmenting physicians, key opinion leaders and payers
7.2.3 Deep learning about customer experience
7.2.4 Informing R&D, regulatory compliance and market access
7.3 SWOT analysis of Big Data techniques
7.3.1 Strengths
7.3.2 Weaknesses
7.3.3 Opportunities
7.3.4 Threats
8. Modelling and simulation: extracting business insight for commercial
excellence from Big Data
8.1 Key insights
8.2 Appropriate modelling essential
8.3 Data types needed to improve aspects of commercial excellence
8.3.1 Real-world data
8.3.2 Data about a more accurate market share
8.3.3 Data sharing for better decision-making in the field
8.3.4 Data about the patient experience
8.4 Harmonising data from multiple sources
8.5 The tools to use: from knowing which data to use to actioning insights
8.6 From insight to competitive advantage: the human factor
8.7 From insight to customer-centricity to increased sales
9. Structural changes to accommodate Big Data
9.1 Key insights
9.2 Get your house in order
9.3 New capabilities and positions
9.4 A culture of data and innovation
9.5 Data-based training methods
10. Best future opportunities for the use of Big Data in commercial
excellence
10.1 Key insights
10.2 The Internet of Things
10.3 Targeted customer messages
10.4 Precision medicine and personalised patient programmes
10.5 Payer orientation for improved market access
10.6 Improved physician engagement
10.7 Driving patient adherence
11. Overcoming the greatest challenges in Big Data
11.1 Key insights
11.2 Uniformity of data language
11.3 Data in disparate locations
11.4 Magnitude and credibility of data
11.5 Data privacy and security implications
12. The next five years of Big Data
12.1 Key insights
12.2 Machine learning and scaling data
12.3 Investments and partnerships in pharma
12.4 Security and compliance
13. Conclusion
14. Appendix: Experts interviewed