Pharmaceutical industry experts agree that the future of pharma depends on the use of Big Data to predict upcoming events and markets, gain insights into new opportunities, and understand the implications of competitors’ activities.
With Big Data comes a big challenge: how can those in commercial, regulatory, and clinical areas successfully navigate the three defining properties of Big Data: volume (the exponentially growing amount of unstructured data), variety (the different forms the data may take), and velocity (the constant creation and dissemination of new data) to produce real insights?
How can biopharma professionals return the useful, data-driven observations and actionable conclusions Big Data can bring, when time and resources for mining, analyzing, and applying such data are in short supply? And how can the industry realize the promise of Big Data and efficiently use it to gain timely, relevant, predictive insights on a specific area of focus, thus significantly reducing costs and time to market?
When looking at today’s pharma markets, it’s clear why organizations are looking for ways to successfully navigate the crush of Big Data in order to realize its advantages. According to the “Pharma R&D Annual Review 2020” by Informa Pharma Intelligence, there are 1,556 more drugs in development in 2020 than there were this time last year – a significant increase in volume. That’s 1,556 additional drugs to track, from patent to market, with data from various sources about everything that occurs every step of the way through the drug development, trials, and marketing processes. It’s not surprising that knowledge workers and researchers are also spending more time each week searching for information to advance the development of new drugs or identifying indicators and signals of new market opportunities. This velocity of continual data translates into a significant number of hours per year that a typical researcher will spend on manual information research and extraction. And, according to Accenture’s Search and Content Analytics blog, 80% of the data researchers find is unstructured, packaged in a wide variety of data forms, not following a standard format – all of which makes the prospect of working with Big Data – despite its obvious advantages – intimidating at best.
For example, GlaxoSmithKline reported that its clinical safety team must monitor medical literature for relevant safety information for an average of 20 marketed products each day. Their daily task uncovers an average of 60 new references every day, with each abstract requiring a manual review. Each of those reviews takes from 72 to 96 seconds to complete, meaning researchers on the team spend an average of about 90 minutes per day reading and reviewing this data, totalling seven and a half hours per week dedicated to this discrete data task. When one considers that there may be an average of ten times as many marketed products in a typical pharma company portfolio, the time required to gather and vet this type of data quickly grows to astronomical proportions that can rapidly become unmanageable for many organizations.
Yet the challenges of working with Big Data can’t diminish its undeniable advantages. The very real benefits of controlling drug development and discovery costs, reducing the overall drug development cycle, and addressing and mitigating high failure rates of drug launches, to name a few examples, can give companies market advantages that far outweigh the disadvantages of grappling with huge amounts of often disparate and unwieldy data. Finding these advantages quickly and early-on (especially in cases where companies are evaluating new opportunities, assets, or technologies) demands a more data-driven approach for smarter – and faster – decision making.
While the prospect of using Big Data to streamline workflows and improve decision-making capabilities can be intimidating, help exists. Finding options tailored to your specific needs that will empower you to easily find answers to your most critical business questions is paramount. An analytics partner should have a firm understanding of both the industry and its data, and be a seasoned expert in knowing how such data applies to real-world situations.
Informa Pharma Intelligence offers Analytic Solutions as a comprehensive means of successfully navigating the volume, variety, and velocity of Big Data. Solutions are comprised of either ready-made tools that help users obtain the information they need to build their strategy, or Custom Analytics, tailored more specifically to the particular specialized needs of some users. These Analytic Solutions draw on the industry-leading data present in Pharma Intelligence tools, including in subscription database solutions like Citeline, Biomedtracker, and Pink Sheet, to name a few. Additionally, separately available API solutions from these data sources allow organizations to streamline their data collecting and analyzing tasks by delivering relevant, reliable, and timely data straight to the client for integration directly into their own databases.
Custom Analytic Solutions are also available, putting the extensive experience of Pharma Intelligence’s database experts and analysts to optimum use in resolving specific issues. With extensive experience in knowing exactly how to apply the salient takeaways across multiple data sources and creating real-world business solutions that address specific concerns, Pharma Intelligence Custom Analytic Solutions provide bespoke consulting, personalized data modelling, and data visualization to address clients’ very specific business questions. Pharma and biomedtech companies are using this combination of gold-standard industry data, coupled with AI-enhanced analytics, to deliver actionable insights into their most complex and pressing business issues.
For biopharma companies looking to transform the daily avalanche of Big Data into real-world solutions that quickly and more efficiently drive their business strategies forward, the Analytic Solutions options from Informa Pharma Intelligence deliver an array of tools designed to make extracting the value of Big Data simpler and more efficient.
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