4 Biggest challenges to research analysis in the pharmaceutical industry

In recent years, the explosion of data sources has provided unprecedented opportunities for pharmaceutical companies to garner vast sets of information on patients, diseases processes, and treatments.

However, as we have noted elsewhere, around 60 percent of healthcare studies are now scattered across thousands of siloed or incomplete sources, and around 50 percent of historical scientific data is no longer publicly available.

This is valuable information that can help the Medical Affairs departments make better decisions. However, the sheer volume of research now available can present significant obstacles to progress.

Here we look at four of the key challenges facing pharma companies as they look to leverage big data.

1. Need for Skilled Professionals

As the Harvard Business Review (HBR) has noted, “the biggest challenge by far has been talent.” This is because in recent years data is increasingly being generated from sources outside the clinical setting, like EHRs, cohort and retrospective studies and other sources of Real-World Evidence that are proliferating.

Analyzing big data sets requires a different set of skills.

“There really aren’t enough well-trained people who understand how to deal with big data problems,” said Steve Labkoff, executive director at Purdue Pharma, in an interview with Pharmaceutical Executive. “First, you need IT people. But secondly, you need data scientists or informaticians, who are a different breed and who understand the nature of where data comes from and what it actually means at the granular level.”

2. Need for New Technologies and Analytical Methods

Learning to take advantage of the opportunities that large data sets provide is more than just about finding the right talent for the job. It is also a matter of developing new technologies and new analytical methods to properly handle this wealth of new research.

“Adoption of advanced analytical tools, mathematical modelling, and AI-based approaches to analyzing data [by pharma has] been slow compared to other industries, such as finance and IT,” said Slava Akmaev, co-founder of Berg Analytics in an interview with Pharmexec.com.

Yet, new techniques can help to enhance the creation of new and effective medications and treatments, according to McKinsey & Company: “Instead of rigid data silos that are difficult to exploit, data are captured electronically and flow easily between functions, for example, discovery and clinical development, as well as to external partners…This easy flow is essential for powering the real-time and predictive analytics that generate business value.” Despite the difficulties, the current trend is towards the growth of new technologies and analytical methods.

3. Inherent Difficulty in the Data Itself

To make matters more complicated, there can also be challenges that are inherent to large data sets. Problems include missing values, lack of structure, a variety of formats, lack of consistency, and the potential for research biases, just to name a few.

In addition, sources can include everything from formal results of drug discovery programs and clinical trials to less formal data gathered from electronic health records, test results, and even genetic information.

“The underlying big data challenge facing the pharmaceutical industry is very similar to the challenges businesses are tacking across all sectors of the economy: how do they effectively and cost-efficiently manage data volume, velocity, and variety? In order to effectively capture, process and manage data, the first step is to integrate disparate or poorly connected sources of information,” said Murod Vassib, pharmaceutical sector leader at the U.K. data analysis company, Talend, in an interview with Pharmaceutical Technology.

4. Reluctance to Invest

Developing professional talent — and the technologies and analytical methods needed to fully take advantage of the wealth of new information — is, unfortunately, expensive. McKinsey & Company noted that there is a definite reluctance on the part of many pharmaceutical companies to invest in new areas, partly because of a lack of precedent.

There may also be a lack of will and perceived necessity on the part of some pharmaceutical companies to invest. “[Pharmaceutical companies] have been flush with cash. … The more profitable companies are, the less they look for the pennies and the minor tweaks and twists that would boost efficiency and return on investment,” said Scott Evangelista of Deloitte Consulting in an interview with Masters in Data Science.

Challenges and Opportunities

The wealth of information being generated by modern technologies is giving pharmaceutical companies unprecedented opportunities to learn more about disease processes, clinical outcomes, and specific patient populations.

However, there are also unprecedented challenges to overcome, particularly in regards to the development of talented researchers and the necessary tools they need to make quality analysis possible. Inherent problems in the data itself as well as the upfront investment costs to make this happen also present obstacles that the pharmaceutical industry will have to surmount in order to take full advantage of all the new information at their disposal.