Two Essential Steps for Enabling Insight-Driven Decision-Making for Life Sciences Companies

  • mars 08, 2023
two healthcare professionals reviewing data

Life sciences companies today are on a journey towards an agile, unified data-driven business model. This concept explores and capitalizes on data capture, constant communication across stakeholders and data centers and sources, and analytical tools capable of extracting value from the data.

While one may assume that creating such an ecosystem would be a complex endeavor that requires a massive technology investment from life sciences organizations, it is quite the opposite. Leveraging predictive and actionable biomedical insights from data can be summarized into two simple steps:

Data and ecosystem democratization

Given the complexity of biological data and the shift of life sciences companies from the linear blockbuster model to genomic and precision medicine, it would be naïve to assume that a singular life sciences organization would have the panacea for all the ills ailing the human species.

With recent legislations around FAIR use principles of biological data, it is becoming increasingly evident that patients, academia and providers view access to biological datasets and research data as a natural progression toward managing patient health and delivering positive health outcomes. From the life sciences industry perspective, companies have realized that the traditional data silos model has escalated the costs of discovery across the industry to over $2.5 billion per new molecule.

Incidentally, the highest costs in the drug discovery process stem from clinical trials, which are highly regulated processes. The complexity of clinical trials and the upstream preclinical investigations across complex diseases have forced organizations to adopt an “Open Data Framework,” which enables the creation of a public set of deidentified clinical data. This open data culture must permeate all aspects of the life sciences industry, including medical device and biotechnology organizations. This new environment would complement the availability of public human genome and mutation databases alongside other data types across the life sciences value chain.

However, such data democratization requires strict data security measures in place. By operating within a blockchain environment, life sciences companies could use specific datasets to address many use cases, including target identification, patient stratification, patient engagement, therapeutic portfolio demand, sales pipeline management, and production and supply chain optimization.

This is especially important as research facilities, manufacturing units and supply chain facilities are being shared to optimize costs. The success of this resource-sharing approach leans heavily on the ability of an ecosystem of life sciences and healthcare stakeholders to operate in an open data ecosystem environment, with patient centricity at the core of all actions.

Identifying and engaging the right tools to analyze datasets

While data democratization facilitates a connected ecosystem to all shareable datasets across the value chain, the success of a life sciences organization in using such data would depend upon its ability to ask the right questions and gather the right set of insights.

To that end, it is essential for life sciences organizations to determine the use cases they would like to tackle with the available data. It is imperative for life sciences companies to shed the misconception that data strategy is all about investing in analytical tools to wrangle with the immense data flow.

Pharmaceutical and biotechnology companies are typically focused on improving therapeutic efficacy and clinical trial success rates, sustaining “golden batch consistency,” and reducing instances of drug counterfeiting. The redressal of these broad challenge contours necessitates that life sciences companies adopt a reductionist approach that breaks more significant business challenges into actionable questions that can be answered by the data.

For instance, a company looking to improve its chances of identifying a new cancer drug needs to consider the disease's characterization. This consideration would include genomic expression patterns, risks, patient stratification and incidence rates, toxicity outcomes and inhibition points across the appropriate signal transduction pathways. That company could then determine whether the data needed is purely genomic or a mixture of genomic, pharmacological, and clinical/patient data.

Organizations must then leverage open-source analytical tools and AI solutions to clean, classify and analyze the data to deliver insights across the business function or value chain. This approach requires life sciences companies to acknowledge that AI programs succeed when exposed to, and trained around, a wide array of datasets and use cases, highlighting the importance of capturing as many datasets as possible.

However, a common challenge for adopting AI models among life sciences organizations has been the siloed presence of AI models and the lack of scalability of these AI models across the larger data ecosystem of an organization. The solution to that challenge is to identify automation points across the processes of data capture, data scaling, data cleaning and data virtualization.

Using AI and analytics-based insights also requires pre-deployment validation and local adaptation. Pre-deployment validation allows companies to check their AI models to reduce inherent biases and better facilitate the delivery of insights in a uniform, consistent fashion that remains unaffected by scale or scope. Meanwhile, the local adaptation stage of AI deployment ensures that AI models used across the collaborative stakeholders adapt to the changing parameters and conditionalities of the local datasets.

In my next blog post, I will discuss how life sciences companies can take their data strategy further by building a federated data insights ecosystem. In the meantime, visit our Life Sciences practice to learn more about how you can maximize the potential of your data with our wide range of services and solutions.

Subscribe to our blog

Bhuvaneashwar Subramanian

Bhuvaneashwar Subramanian has more than 20 years of experience as a thought leader in the healthcare and life sciences. He has published extensively, including peer-reviewed academic articles on cloud computing in life sciences, digital health and nanobiotechnology commercialization. Bhuvaneashwar is a qualified biotechnologist and holds a master’s degree in molecular and human genetics from Banaras Hindu University India, a diploma in molecular biology from the Hungarian Academy of Sciences and an MBA from Edith Cowan University Australia.

Related Blog Posts