Article updated October 2024
Health-focused organizations are transforming patient care and other operations with healthcare business intelligence. But how are their counterparts in the life science industry leaders using business intelligence in pharma?
What is business intelligence in pharma?
The term business intelligence originated in the 1960s to describe a system of sharing information across organizations and evolved in the 1980s with computer models for decision-making and turning data into insights. Now, BI combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make more data-driven decisions.
Business intelligence in pharma addresses challenges in new drug development and trials to achieve breakthroughs ahead of competitors. Operational applications of BI in pharma include effecting change, eliminating inefficiency, and enabling the ability to quickly adapt to market changes or supply chain fluctuations.
The breakneck pace with which advanced technologies including artificial intelligence (AI) and its subsets are maturing have high disruption and impact potential to amplify the value of business intelligence in pharma, as well as the potential to effectively help teams act upon it.
However, it’s important to remember that AI is a means to an end. “AI is not a strategy. It’s how you do things,” says Within3 CEO Lance Hill. “The strategy is to affect some business result or change that you are dealing with depending on where you are within the organization and understanding the latest ways that companies are trying to solve that. This is probably going to involve AI in some way.”
Business intelligence and AI in pharma should seek to answer the following questions in three of the industry’s pillars:
- Clinical R&D. Are therapies in development on track to hit the right targets in patient populations? Are our KOLs the right influencers and advisors or are we talking to the STLs: the same 12 leaders? What do we need to know to effectively design clinical trials then efficiently enroll and retain the right patients?
- Commercial. Will there be payer roadblocks to adoption and if so how do we successfully navigate them? Are we really doing an excellent job educating our teams, our prescribers, our patients? Are we learning enough to shift course if needed or attack opportunities for new indications based on post-launch data?
- Medical affairs. Not only do we have the right KOLs on board, but are we giving them what they need, when they need it to quickly generate insights that impact strategic decisions and ultimately patients’ lives and the bottom line especially prior to and at launch?
All of these questions reasonably fall under the guise of business intelligence in the pharma industry. The answers hide in siloed data, including patient data, MSL and field operation observations, and disease-specific data on all levels, from local to global, to name a few.
What are the applications for business intelligence in pharma?
In pharma, the four primary areas where BI is applied are:
Operations
Life science organizations collect and monitor vast amounts of data generated by an ever-growing list of sources. This data is critical to business operations because it can be turned into actionable insights. It can also guide huddles in healthcare to reach data-driven decisions.
“Increased challenges in the pharmaceutical industry dictate that pharmaceutical companies stay ahead of the competition…With all this information integrated from multiple sources, they can operate more efficiently, optimize their competitive edge, and increase revenue.” – ChristianSteven
Currently, data quality and availability remains a challenge. In one industry leader’s words, “I truly believe the biggest challenge [in using AI for real time insights] is data quality and data availability,” says Neeraj Goel Mittal, Global Head of Data, Analytics and AI for GSK. “We forget that insights are based on data and the principles of data lineage in terms of data ingestion, data quality, data transformation and data to action still apply. Sometimes because of the hype, leadership expects the insights to be available [quickly], when the data on which those insights depend might not be available in the format we need it.”
In our insights management survey, 76% of industry leaders told us that the way data and insights are shared and stored within their organization creates separate data silos, which obstructs generating or sharing insights efficiently.
AI’s potential impact … and supporting requirements
“Generative AI is very good with content consumption and content generation,” adds Mittal. “But we still fall short when combining structured data with AI content consumption and generation.”
That can add challenges around the data quality element, where previously teams may have celebrated unlocking an insight. “But as we continue to innovate, that’s no longer sufficient,” says Nathan Lear, Director of Commercial Data Science & AI, US Oncology for AstraZeneca. “We need to engage internally [regarding] risk tolerance. How much risk are we willing to tolerate? At the same time, are there ways where we can be more innovative in how we validate outcomes so that we can continue to build trust in what we’re delivering? There is an increasing bar that we need to meet internally with these solutions to build that trust and to get the kind of adoption that we need with these tools.”
“The biggest benefit is the amount of data AI and gen AI can deal with,” says Mittal. “Not just the visible data for a patient, which is on paper, but also invisible data in terms of social demographic and economic conditions where AI can combine data and give personalized insights.”
“AI tools are now capable of examining data or observations from the field, one-on-one interactions, information from congresses, online, social listening, advisory boards, medical information and so on,” Within3 CEO Lance Hill said. “They can then generate a view of it all in a way humans can understand. The humans then apply their own knowledge and expertise to impact strategy and outcomes.”
Related content: Artificial intelligence holds promise – and potential challenges – for the pharma industry. Here’s what’s important for you to know now about generative AI and ChatGPT in pharma.
Clinical data analysis
Pharmaceutical and medical device companies are under pressure to bring products to market quickly, but at the lowest price possible. For this reason, effectively managing clinical data is a top priority. By gathering data from multiple sources, BI enables pharmaceutical companies to use data analytics to identify trends and inconsistencies. They’re able to understand the risks during product development and launch, monitor adoption and adherence and more closely identify potential for expanded indications.
Marketing
Marketing is a huge cost center for life science companies, and it’s essential to understand how this investment pays off in terms of sales. Tracking sales performance and consumer behavior is essential to improve marketing strategies including better distribution of budget.
“BI allows companies to identify the products that are the most profitable, monitor consumer behavior pertaining to prescription renewals and product purchases, chart the success of marketing campaigns, and analyze profitability by product, customer, demographics, and other factors,” – ChristianSteven
Financial analysis
Financial data is an integral part of any organization, and most businesses closely monitor this information to understand budgets, expenditures, and return on investment. Here, BI solutions help pharmaceutical companies monitor financial transactions and predict requirements and issues. This proactive approach helps companies remain nimble in the event of a disruption, and can make reporting requirements less stressful.
Business intelligence vs. insights management
BI tools can be applied across the entire enterprise – from research and development to finance and supply chain. Data warehouse, visualization, and analysis capabilities handle many aspects of managing and making sense of huge volumes of data.
An insights management platform for life science organizations is built to collect observations and other data obtained during the drug or device development process. Insights management solutions might also feature the ability to show data on a specific disease community, provide insight into online conversations through the use of social media listening, and collect and analyze insights from discussions with healthcare providers.
Insights management is also less technologically disruptive than business intelligence – rather than requiring an overhaul or integration of legacy systems, teams in clinical R&D, medical affairs, and marketing work with their insights management technology vendor to set goals, plan engagements, and formulate next steps.
Dive deeper by learning more about the trends driving the adoption of insights management in our blog post, or find out the three things life science teams can do now to make better, faster decisions. Or connect with our team today to unlock the power of insights.
