Top 3 Digital Transformation Challenges in Life Sciences

June 27, 2018

4 minute read

Pharmaceutical and life sciences enterprises are under more intense competitive pressure than ever before. According to research from the global consulting firm Bain & Company, the average window from when a new drug hits the market to the launch of competing products has shortened from eight years to four, while nearly half of new product launches underperform analyst expectations.

To increase the probability of success in this demanding climate, organizations must limit costs, reduce time expenditures, and maximize quality. But in the quest to improve these metrics, various digital transformation challenges can often stand in the way, impeding life sciences organizations from leveraging the single most valuable asset they already possess: data.

Better data utilization has the potential to drive blockbuster results for life sciences companies. The McKinsey Global Institute estimates that the U.S. healthcare system could drive an additional $100 billion in value annually simply by employing better strategies to leverage the full value of their data.

Read on to learn how digital transformation can improve key information management challenges that, for many life sciences organizations, may be standing in the way of success.

Challenge #1: Tapping into pools of data

The goal of many activities within the life sciences and pharmaceutical sector is to generate actionable information about developmental or new drugs. But have you ever thought about how much information all of the resulting surveys, documents, tables, and reports actually yield? Terabytes or even petabytes (that’s one-million gigabytes) are created at every stage of product development, according to estimates.

The challenge that follows the creation of such vast amounts of data lies in transforming these massive stores into something from which meaning and insights may be extracted. This requires a four-stage approach:

  • 1. First, all of that data must be standardized into formats that can be readily processed and compared.
  • 2. Next, it must be analyzed by applying rules that help decision-makers better understand exactly what data they have and where it exists.
  • 3. Then that data must be categorized into appropriate groups or buckets and duplicate information must be eliminated.
  • 4. Lastly, it must be optimized, extracting meaning via artificial intelligence technologies, redacting sensitive PII, and compressing files to reduce the cost of storing this content within repositories.

Only after following these four steps are organizations positioned to leverage this huge amount of information to identify risk, extract value, improve business outcomes, and translate vast volumes of information into useable insights.

Challenge #2: Data isn’t useful if it lives in silos

The human body is an incredibly complex network of systems and pathways, leading to a great deal of overlap between seemingly disconnected bodily functions and processes. As such, single drugs can often have multiple applications, sometimes even related to what might seem to be discrete health conditions. These connections also mean that it’s not unlikely there would sometimes be overlap within elements of separate drug trials and other medical research—despite the fact that, on paper, such studies may have little connection, or might even fall under different business units. The challenge for organizations lies in facilitating these connections by improving research visibility across business lines.

There are both tremendous insights and opportunities for cost savings in doing so. A Forbes analysis found that the cost of bringing a single drug to market clocks in at $350 million. However, once you factor in costs related to the 95 percent of experimental drugs that never make it to approval, the actual price tag per new medicine can work out to closer to $5 billion. Imagine how much insightful and cost-saving information exists within all of that research. If it cannot be harnessed, then $4.65 billion of that total investment is wasted.

But if life sciences organizations take a business-wide approach to data management and digital transformation, then all of the insights generated can be leveraged. This entails unifying all aspects of an enterprise’s approach to its data in order to create a centrally accessible repository, and applying file analytics so that valuable information can be extracted regardless of where within a company that data is owned. As such, previous research can be considered earlier in the discovery phase—reducing the risk of failed or redundant trials—or shared more efficiently in order to improve product effectiveness, identify new applications or potential risks, and other beneficial applications.

Challenge #3: The cautious nature of the industry

In January, Johnson & Johnson CEO Alex Gorsky created an uproar when he told investors that it won’t be long before “we won’t be classifying ourselves as just a healthcare or biopharmaceutical industry, but we’ll be a healthcare biopharmaceutical technology industry.”

While Gorsky’s speech speaks to an awareness of power of technology to drive massive efficiencies and growth within the sector, many players within the industry were skeptical about adoption rates. A McKinsey survey of C-level executives at more than 3,000 companies backs up this reluctance to adopt new technologies, finding that only 20 percent of firms consider themselves “adopters.” Of all the industries that participated, adoption within the health sector ranked lowest overall.

But considering the gains and efficiencies better data utilization may bring, perhaps it’s worth using Gorsky’s statement not as merely a prediction, but as a directive. Considering the alternative—data silos and huge volumes of expensive, underutilized research—it’s important to weigh the risks and rewards.

Wrap Up

Digital transformation has the potential to drive billions of dollars in value within the life sciences and pharmaceutical industries. But in order to realize the blockbuster benefits, organizations must first change their approach to data management—decreasing massive volumes of unstructured data, eliminating organizational data silos, and embracing proven solutions.

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