3 Pharmaceutical Data Strategy & Market Research Tactics to Make Confident Commercial Decisions

Authored by: Gan Tan, Partner, Data Strategy, Analytics & AI Practice Lead, Putnam (Inizio Advisory) and Bilal Babar, Analytics Director, Research Partnership (Inizio Advisory).

Pharmaceutical leaders are ready to capitalize on the meaningful insights that data and AI projects can deliver commercially and for patients. However, innovation is still a learning journey, and leaders must be confident using these insights to make decisions. To gain this confidence, some pharmaceutical companies are pausing projects to reflect on and course-correct the foundations of data strategy and market research.

In this article, Gan Tan, Partner, Data Strategy, Analytics & AI Practice Lead, Putnam (Inizio Advisory), and Bilal Babar, Analytics Director, Research Partnership (Inizio Advisory), together with feedback from industry leaders, discuss the challenges and why hitting ‘pause’ is part of the journey to making strategic decisions with certainty.

What’s Happening Behind the Scenes?

Every department within a pharmaceutical organization is tackling data and AI innovation. However, the age-old issues of data sourcing, data quality, siloed availability, and analytics skill sets remain. In reality, some leaders have had to slow projects down to revisit the basics before they run ahead to find meaningful and actionable insights for a competitive edge.

There are also difficulties in using multiple data sources, new vendors, and separating meaningful AI advancements from overhyped ones. Knowing when to stop and how many pieces of information are required for meaningful insights is also challenging. Market research faces similar pressures, with clients seeking to integrate multiple data sources to demonstrate campaign effectiveness.

Our experience with clients and our expertise leads us to conclude that many organizations are still on a journey with data and AI. We’ve collated a sample of the positive steps they take to overcome challenges as they develop independently or with the expertise of external support.

1. Data Integrity: Certainty in Data Quality

Pharmaceutical leaders face the legacy challenge of data integrity as they develop advanced data and AI capabilities. Addressing foundational data issues before diving into AI and advanced analytics is essential, as poor data quality fails to instill certainty in insights and, therefore, decision-making. To gain certainty in meaningful insights, some leaders are pausing AI initiatives briefly to focus on data cleansing and consolidation.

A reset is also being performed to address significant change management challenges. Large areas of upskilling are required alongside smaller educational shifts from static reports to embracing dynamic visualizations.

2. Data Strategy: Certainty in Data Sources

From a data strategy perspective, integrating data from multiple sources is a big challenge for leaders. Management of such diverse and nuanced datasets is complex, especially when combining clinical data with external commercial sources. For this reason, tokenization is a solution being explored.

Tokenization converts sensitive information into non-identifiable tokens so developers and engineers can securely integrate data from multiple sources – patient records, survey data, or commercial metrics. Using tokenization, an organization has the ability to stand up a custom data platform that combines disparate third party data sets into a single more complete view of data, enabling teams to draw more accurate and actionable insights. A custom data platform enhances data privacy, quality, and consistency – it also reduces the friction of integrating datasets from different vendors and allows for the transition from multichannel to omnichannel strategies. Developing this master view facilitates real-time decision-making, optimizes resource allocation, and drives better outcomes.

3. Market Research: Certainty in Return on Investment

At market research level, it is becoming increasingly important to demonstrate the return on investment of activities – particularly in Medical Affairs initiatives. For this reason, the focus is now on aligning campaign data with commercial metrics and market share. Tracking specific data points over time can connect the dots between activities and performance – calculating the return on investment (ROI).

AI has the ability, when done ethically and with integrity, to support this process by quickly synthesizing social listening and real-time event feedback—it allows for deeper sentiment analysis. More than ROI, it provides agility, turning data-driven insights into actionable path correction for activities. More engaging visualization of these results also brings data to life for stakeholders.

Key takeaways

As all pharmaceutical companies embark on the data and AI journey, it will become increasingly important to know when to pause to address challenges that impact confidence in insights and, therefore, decisions. Whether this is data integrity, change management, finding ways to ethically and safely synthesize data from multiple sources, or developing with ROI metrics from day one, certainty is vital.

Download our playbook to learn about the four pitfalls to avoid when navigating data & AI to drive better commercial and patient outcomes.

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