Information overload is real.
Across pharma companies, from medical affairs to commercial and beyond, teams grapple with an overwhelming influx of information. This data deluge, coming from disparate sources, resides in different repositories across the organization. As a result, teams struggle to identify the crucial insights needed to make the best, most strategic decisions.
Many teams face challenges managing data, while others struggle to turn it into insights that can influence strategic business decisions. Some may use tools, including dashboards, but dashboards alone aren’t enough. Superficial data analysis can create the illusion of insights without meaningful substance.
A recent Impatient Insights webinar highlighted these challenges, shedding light on the limitations of current practices and exploring more effective approaches to generating insights. The panel of industry experts included Sonal Bhatia from Pfizer, Justin Molavi from Genentech, Kieran Davey from AstraZeneca, and Lance Hill from Within3, and was moderated by Paul Simms from Impatient Health. They discussed how personalization, intentional AI use, and intra-team collaboration can holistically create strategic insights.
Distinguishing insights from the noise
Pharma organizations looking to uplevel their strategy must understand the difference between “data” and “insights.” In this context, data or observations are points of information we gain from talking to an individual healthcare provider (HCP), analyzing a social post, or ingesting other relevant facts. Insights emerge by analyzing multiple data points to uncover the broader implications for strategy.
Once insights are defined, the next essential step is identifying which insights align with business and organizational goals. This involves sifting through irrelevant data to focus on what’s most impactful.
“Our understanding of insights is becoming more nuanced—not necessarily because the insights themselves are revolutionary, but because of how we engage with them,” said Justin Molavi, Director, Commercial Strategy & Insights at Genentech. “It’s about building the capability to measure, experiment, and distinguish valuable insights from noise, driving decisions that go beyond just ROI or drug sales to truly impact behavior and strategy.”
Dashboards, dashboards, dashboards
In the effort to distill data into insights, dashboards are often praised as essential tools for data visualization. However, they can create a false sense of security. Dashboards provide a snapshot of data, but they often lack the depth and context required for strategic analysis. The danger lies in treating dashboards as the ultimate solution rather than a starting point for deeper exploration.
A webinar attendee summarized it perfectly: “Everyone is so focused on visualizing the insights, and it’s all ‘dashboards dashboards dashboards’ instead of layering intelligence to dive deeper into trends, forecasting, and next-best actions that drive lagging indicators.”
Challenging bias
Moving beyond dashboards to a more sophisticated approach presents other potential pitfalls. When analyzing large datasets, AI can scale operations and address bias. Multiple panelists emphasized the challenge of confirmation bias in large organizations, where insights are selectively used to support existing beliefs—intentionally or unintentionally.
“How do we remove confirmation bias so that we actually get real insights and not ‘what we like to hear’ insights?” asked Sonal Bhatia, SVP, Head of Medical Affairs, Rare Disease, Immunology & Inflammation, Hospital Business at Pfizer.
The solution requires combining AI with human insight. AI integrates disparate data sources and can reduce personal biases, but it’s not infallible. AI itself can introduce biases through human-designed algorithms. While AI generates and synthesizes insights, human oversight and judgment ensure these insights are relevant to the strategic imperative, accurate, actionable, and free from errors and biases.
Measuring the impact
For insights to have maximum value, they must be paired with measurable outcomes. In some organizations, medical affairs teams struggle to influence broader business decisions despite their insights. Kieran Davey, Senior Medical Affairs Leader for Lung Cancer at AstraZeneca, stressed the importance of defining strategic measurements.
“Finding impact measures that align with business goals but are within our capability is one of the key gaps we need to work on,” Davey said.
The need for impactful measurements extends beyond individual teams; commercial teams also face this imperative.
“Despite the great insights we have in commercial, we must build the capability to measure,” said Justin Molavi.
“It’s a new skill for all of us because we’re in hyper-competitive markets. These insights are teaching us about the business in ways we hadn’t known. We need both automated insights, with reduced bias from AI, and a structure to act on them. It’s about making decisions with insights, framing them… and defining the leading and lagging indicators that are truly impactful.”
Intentional Collaboration
Collaboration between medical affairs and commercial is crucial for leveraging insights effectively. However, too much collaboration—or misdirected collaboration—can delay action and impede progress. Building effective teams that are empowered by their solutions helps maintain momentum and avoid bureaucratic delays.
Manually pulling and sharing insights—through tools like SharePoint or slide presentations— hinders effective cross-functional collaboration. Insights platforms that offer single-click reporting save valuable time, allowing experts to focus on interpreting high-quality insights.
AI for Life Sciences
AI-assisted insights gathering, management, and sharing can streamline processes, reduce manual work, and improve the quality and relevance of insights. Lance Hill, CEO of Within3, outlined four criteria for assessing the efficacy of an AI-enabled solution. He encouraged organizations to consider:
- The quality of insights generated
- Time saved compared to manual processes
- The effectiveness of reporting
- The ability to analyze multiple data sources simultaneously
Once an organization considers these, it can appreciate the shift from basic AI tools focused on dashboards and natural language processing to more sophisticated models designed for life sciences. These advanced tools integrate multiple datasets, automate reporting, and provide actionable insights aligned with the organization’s strategy.
To turn insights into actionable strategies, organizations should deepen their analysis by leveraging AI with thoughtful human oversight. This helps prioritize strategic and actionable insights rather than sifting through uncontextualized data.
“A piece of advice I always give to people is, do fewer things but make the things you do more impactful,” Davey said.
Want to hear more from these experts? Watch the on-demand webinar.
