Merger of biotech and infotech to show the way

Today, solutions are emerging that are attempting to facilitate this balance of requirements at the data, code, and procedural level. Information technology and biotechnology.

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Information technology and biotechnology originated under two different scientific contexts, and their areas of investigation also differed greatly. However, these two biggest disciplines are now merging at the application level more than ever.


Pharmaceuticals and life sciences companies are embarking on initiatives with a moonshot goal of changing the way drugs are developed. This goal is pursued through endeavours such as personalised health care, precision medicine, digital health and real-world evidence, inter alia.

The common thread among the initiatives is the need to process and analyse big data—massive amounts of complex data that is being continuously generated across different collaborating entities with unique regulatory restrictions and analytic capabilities. This requires state-of-the-art information technology that can scale, evolve, and protect the data and the intellectual property of the analytics service providers.

This is igniting the already charged marketplace of data analysis and management tools. The inadvertent result of this rapid growth in tools, is the increasing confusion and complexity for the customer to adopt relevant tools and services for their data use cases to accomplish the initiatives above.


The proliferation of expensive data transformation, data analytics, data storage and data science tools, as well as the need to secure the “data of the data providers,” the IP of analytics service providers, and conforming to multiple regulatory standards cripple the above initiatives by mandating large budgets, both in terms of tool licensing as well as niche skill sets and expertise.

The most effective way for a healthcare and life sciences organisation to mitigate these challenges is to adopt a flexible, cost-efficient approach that leverages cloud technology and collaboration between multiple entities, leveraging individual strengths without compromising on data loss or IP leakage.

For instance, precision medicine drives us towards the ability to synthesise any drug compound for individuals given their unique predisposition. This goal involves strategic collaborations across drug developers, drug distributors, laboratories, research centres and healthcare organisations. Each of these participating entities have specific regulatory requirements and security considerations, as well as analytic capabilities that have to be individually met to achieve the goal.


The analytics service providers, data owners and the insight consumers all have proprietary and sensitive information which is protected to satisfy regulatory requirements as well as to ensure effective drug development and testing. The data owner needs to protect the data, the analytics capability provider needs to be able to protect the IP around the analytics code and algorithms while being agnostic to the data. The data consumers need to be able to look at various perspectives of the data while maintaining the integrity of the source data and insights.

Today, solutions are emerging that are attempting to facilitate this balance of requirements at the data, code, and procedural level. Information technology and biotechnology though started with unique paradigms and requirements are merging at the atomic level of data, infrastructure, and processes.

The author is Sudish Mogli, Chief Technology Officer and Gautham Gurumurthi, Principal Technical Program Manager, Healthcare Triangle, a subsidiary of SecureKloud Technologies.