Why Do Many Life Sciences Companies Struggle to Harness Their Data Properly?

Many life sciences companies today do not suffer from a lack of data. However, many organizations face the fundamental challenge of extracting maximum value from their troves of stored information. Despite having so much data, they struggle with the key dimensions of data completeness, data scalability, data monetization, and analytics tools.

Traditional pharma-biotech systems typically operate in silos. For instance, a conventional biomanufacturing facility depends on more than 200 parameters to manufacture a biologic or a vaccine. Consequently, the implantation of process analytical technologies (PAT), analytical tools to monitor and evaluate processes, have mostly been unsuccessful in improving overall efficiencies of product yield or helping life sciences organizations replicate the success of a “golden batch” across subsequent batches.

Studies suggest that existing process analytical technologies fail to improve biopharma product yield primarily due to their inability to manage heterogeneous data. While this may be argued as an isolated example, it is essential for life sciences organizations to adopt a holistic view of data in terms of quality, quantity, and typology to derive value from the data.

The traditional life sciences company is challenged by a hazy data strategy or the lack of IT

The traditional life sciences enterprise often has to find solutions to deal with information silos across its various functional units, which were not built to scale towards accommodating information technology as strategic assets.

Further, the degree of data diversity and limited collaboration — often the result of linear design and minimal need for the utility of interdepartmental data assets — has led organizations to sink deep into the labyrinth of data duplication and process redundancies. This condition, by far, remains one of the many reasons for process challenges across the industry.

The parochial approach towards data management in the life sciences ecosystem has continued to influence life sciences organizations that have entered the age of big data biology. However, the data challenges plaguing life sciences enterprises are centered mainly around technology infrastructure, even as cultural challenges around data culture and data appreciation continue to exist.

The core elements that differentiate the success of a data-driven life sciences company from one that struggles in capturing and managing data can be classified into four categories, all equally essential: data retention, data normalization, data digitization, and data standardization.

The importance of a data strategy

A recent study by IQVIA observed that the common reasons for the failure of life sciences organizations building a robust data strategy and data management ecosystem could be encapsulated as:

• An inconsistent and incorrect approach to data governance and business rules
• Inadequate integration and modeling of large datasets
• Lack of investment in the correct type of infrastructure or incongruencies in technology architecture and defined business problems
• Skewed distribution of data management activities and demands on insight generation

Drawing an allegory from recent research by Thomas Davenport on data strategy, most organizations demonstrate a defense versus offense relationship with their data and hence the insights they develop. The defense approach to a data strategy is based on compliance wherein a hospital or a life sciences firm strictly manages datasets and information captured largely from a regulatory standpoint.

The challenge with the defense approach is that it naturally does not align with creating a unified view as expected, given that data collection is given prominence over insights. On the contrary, the data offense approach broadly aligns with drawing insights from data to support business objectives.

While clear demarcation of a pure defense or an offense approach to data strategy in life sciences is hypothetical, the successful enablement of a data strategy is a mix of these approaches wherein data is used to generate insights and maintained to comply with regulations.

For instance, companies engaged in precision medicine are accustomed to the offensive data strategy approach. The focus is on managing and scaling several zettabytes of data to generate sequence reads and identify variants, which have helped them build extensive R&D portfolios to pursue improved therapeutic pipelines.

In my next blog post, I will outline the fundamental elements that life sciences organizations must focus on to handle the growing deluge of data and transition toward the patient-centric discovery and development model. In the meantime, visit our Life Sciences practice to learn more about how you can maximize value from your investments with our cutting-edge services and solutions.