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21-Dec-2020

What Average BI Tools Don’t Deliver for the Pharmaceutical Industry

What Average BI Tools Don’t Deliver for the Pharmaceutical Industry

Summary

Companies in the pharmaceutical industry have reached a turning point with business intelligence (BI) technology.
Editor: PharmiWeb Editor Last Updated: 22-Dec-2020

Companies in the pharmaceutical industry have reached a turning point with business intelligence (BI) technology. The data-rich pharmaceutical industry today is not only tasked with solving critical and complex problems to determine brand success but also how company commercial activities eventually affect patient health and economic outcomes. For this, they need data analysis tools that provide them with vital insights, such as how their activity impacts patient health, where the most lucrative opportunities are, and whether trends indicate market shifts. Unfortunately, even after investing millions of dollars into generic BI tools, companies find that they aren’t up to the task of delivering insights in a timeframe or to a level of detail that makes them truly valuable.

The challenge that pharma companies face is primarily due to these five shortcomings with common BI tools:

1 Scalability

The most significant problem with legacy BI tools used in pharmaceutical applications is their inability to scale. Average BI tools cannot analyze the volumes of data that pharmaceutical companies collect and produce. One therapeutic area alone can require analyzing billions of records, and, for leading companies that focus on multiple therapeutic areas, you need to multiply that number by 10 or 100. Furthermore, as more systems, devices, wearables, and sensors generate more data, pharma companies need to plan on analyzing several more powers of 10 worth of data in the future.

Average BI tools simply cannot scale to this volume of data. As a result, pharmaceutical companies resort to piecemealing data analysis, utilizing disparate applications, multiple dashboards, and standalone reports that the sales rep or other employees must interpret to decide on next steps. Then, if employees need to drill down for more granular insights, the process may take them the entire way back to square one. 

2 Complexity

In general, legacy BI tools are too complex for all members of a pharmaceutical company’s commercial team to use easily and effectively. Employees whose core competency is sales, brand success, or patient services, for example, often require hours of training to use these solutions. Moreover, these tools continue to use these team members’ valuable time to configure dashboards, comb through results – or wait on their IT or data team for assistance when a project is beyond their capabilities.

This problem manifests itself in low user adoption. Given the choice of investing time into using a BI tool and finding a workaround, employees will choose the option that allows them to do their job most expediently, even if it means going into a meeting unprepared. Our research shows pharma companies see less than 50% adoption -- some as low as 10% -- with legacy BI tools. Subsequently, these companies learn the hard way that technology investments do not produce ROI if no one uses them.  

3 Automation

Pharmaceutical companies, like many others across the spectrum of industries, are advancing their digital transformation roadmaps to operate more efficiently with greater automation. Automation frees employees from repetitive tasks so that they can focus on higher-value activities, such as engaging with customers or patients. Business intelligence remains as perhaps the most pivotal area of these business operations without automated processes.

In legacy BI, pulling data, creating analytics and delivering analytics is expensive, time-consuming and laborious. It takes a whole machinery of people, process and technology to not only set it up but continue to deliver small and incremental changes which could take weeks- this both costly and time-consuming which compromises the agility of the workforce on the frontline.

Companies, however, are not content with the status quo. A 2020 survey during a Gartner webinar with Donna Medeiros and Frank Buytendijk revealed that 40% of business leaders in data and analytics roles consider a top priority is finding a solution that automatically delivers insights from data analytics.

4 Access

Another problem with average BI tools is limited access to insights. For example, if one user uses multiple dashboards and then manual methods to draw conclusions about top market opportunities, that information may not be accessible to any other team members or company leadership. 

Furthermore, insights from data analytics are often only available from a PC, and if a tool offers a mobile app, the user experience is most often lacking. Average BI tools also rarely enable the user to access it through a CRM, communication and collaboration platform or other business application they’re using, requiring multiple screens and added workflow complexity.

5 Industry-specificity

Also, average BI tools aren’t specifically trained for pharmaceutical companies. The BI tools that many businesses use today don’t understand the industry’s vernacular, including scientific nomenclature and brand names for drugs, and industry shorthand, such as 4x4, 13x13, , NRx, TRx, HCPs, HCOs and much more. The negative outcome of a tool that isn’t designed for the industry is that users have to learn to use the tool, adapting to its limitations, rather than the tool supporting how the user works.

 

The Downfalls of Average BI Tools Are Driving AI Adoption 

Pharmaceutical companies ready to overcome the handicap of using an average BI tool are discovering the benefits of moving to a next-generation BI tool that leverages artificial intelligence.

An AI-powered cognitive insights platform that is built ground up for life sciences are designed to analyze massive amounts of data with a sub-second response. They also eliminate training so that any member of a pharmaceutical company’s team can ask or type a question in natural language and get an answer – just as easily as asking a virtual assistant for information. Additionally, if the user needs to drill down for more specific insights, it only takes asking a second question.

AI models pre-trained for the pharmaceutical industry also streamline and automate processes. The tools themselves can configure dashboards and even learn user preferences so that it displays relevant information before the user even asks and issues alerts when it detects changes in accounts or the market share. Automating data analysis enables team members to focus on their core responsibilities, not on how to make a complex BI tool work, which naturally results in maximum user adoption and less intervention from IT or data specialists.

As more pharmaceutical companies discover that they have an alternative that immediately overcomes all of the downfalls of legacy BI solutions, they face a new challenge: how soon to act. Early adopters are gaining a significant advantage by quickly homing in on opportunities and elevating patient care. The balance of companies in the industry would be wise not to wait too long to join them.