Some pharma companies spend millions of dollars on advanced analytics, business intelligence platforms and cloud storage and end up with little to show for it.  

The uncertain financial climate should provide an opportunity for pharma companies to reevaluate how they market their products using business intelligence platforms and advanced analytics. 

The pharmaceutical sector has traditionally been slower to adopt business intelligence platforms than many other industries. While some pharma companies, for instance, rolled out BI-based dashboards a few years ago, finance companies had done the same more than a decade ago.  

Part of the reason for the relatively slow adoption of BI platforms in pharma comes down to risk tolerance. Many healthcare organizations are hesitant to embrace dramatic change because of regulatory and legal risks. The pharma industry’s traditionally high profit margins also contribute to its conservatism. 

But the pharmaceutical industry is catching up, said Rohit Vashisht, CEO and co-founder of WhizAI. But business intelligence spending can be a risky investment without careful deliberation. Here are five considerations to keep in mind.  

1. Support for the unique needs of the pharmaceutical industry

Many off-the-shelf BI technologies are ill-suited to meet the needs of pharmaceutical companies, Vashisht said. It is, therefore, vital that pharmaceutical companies consider BI platforms that have industry-specific capabilities. 

BI platforms should also integrate with pharmaceutical companies’ legacy software such as enterprise resource planning (ERP) and customer relationship management (CRM) software. BI software should also accept internal data from commonly used business platforms and external data sources such as market data.   

2. Ability to enable up-to-date insights

Much of the pharmaceutical industry’s sales data is based on weekly intervals. But many pharmaceutical companies encounter a lag when requesting specific sales data. Companies exporting data from a dashboard often run into delays of four to six weeks, Vashisht said.

“It will start an IT project. Somebody will go and check where the data is, pull the data and create a model,” he explained. “Then, they might create a new dashboard with custom calculations. After that, it goes through a QA cycle, then the change management process happens, and finally, it goes to the business team.” 

Offering up-to-date analytics is possible by centralizing data and providing a granular breakdown of data points. It is possible to get “on-the-fly split-second” responses to queries, Vashisht said. 

3. Self-service access to content 

Related to the above point, one potential productivity killer is the practice of requiring an IT or data science gatekeeper for routine data requests. IT teams may create dashboards or reports for sales reps to sift through. Vashisht recalls speaking with a pharmaceutical company that had 27,000 such reports. It’s easy to imagine that “nobody uses 90% of those reports,” he added. 

Pharma sales reps should be able to find answers to common questions without sifting through reports or dashboards. That is, they should be able to self-serve to find the information they need with minimal training and with no code. Even better, pharma companies should look to automatically provide pertinent sales information to reps. “You can get the information you need without even asking for it,” Vashisht said. 

4. Scalability

BI platforms should be scalable and capable of adapting to business changes, whether it is a new product launch or a merger with another drug company. Platforms that use a micro-services-based architecture tend to be modular. 

5. Merging human and machine intelligence

For all of the hype surrounding data science and artificial intelligence, many companies still struggle to use analytics platforms to answer open-ended questions. A pharmaceutical company might want to increase its product penetration for a new drug by 3%. 

“A data science team will likely go on a wild goose chase trying to figure out how to do that,” Vashisht said. “They might find different algorithms and insights and create a new algorithm to increase your penetration,” he added. 

Pharma companies should ensure they have the appropriate data, architecture and business acumen to use BI platforms to answer foundational business questions. Accomplishing that objective requires tapping the capabilities of artificial intelligence as well as relying on experts’ domain expertise — whether they are pharma sales reps or clinicians. Business intelligence platforms should ultimately create a virtuous cycle, where human intelligence yields more effective machine intelligence. That latter then spurs new ideas and stimulates human creativity, causing the cycle to repeat.