The mad rush to develop safe and tested vaccines in one-fifth the time has highlighted the need for cloud-driven data harnessing.

The life sciences industry is at a turning point. To prepare for the future and remain relevant in the ever-evolving business landscape, biopharmaceutical companies and medical technology businesses are looking for new ways to create value and make sense of today’s wealth of data.

Geoff Soon, Managing Director (South Asia), Snowflake

To accelerate the discovery and development of drugs and treatments, many of these firms are already using AI, Machine Learning (ML), and automation. However, for life science organizations using outdated legacy on-premises and cloud database systems, the exploding volume of data poses significant management challenges going forward.

According to Geoff Soon, Managing Director (South Asia) of data cloud platform Snowflake, legacy data warehouses cannot deliver data in a way that enables fast, accurate analyses and insights. This leads to life sciences companies expending precious time gathering, ingesting, cleaning, and organizing the data to make use of AI and ML: “Many healthcare organizations experience challenges with safeguarding and protecting patient information, and maintaining interoperability in the exchange of information from multiple sources. At the time when the world is battling a public health crisis and racing to develop a vaccine, organizations within the sector are realizing the value that a data cloud has to offer.”

Soon in turn offers five ways that life sciences companies can leverage a data cloud platform to drive better decision-making with data.

Easily access diverse sets of data

A data cloud can integrate structured and semi-structured data from a variety of sources, including online transaction processing (OLTP) databases, clinical applications and Internet of Medical Things (IoMT) devices, into a centralized repository. From there, data scientists can use automated organization tools to analyze the data more quickly and efficiently.

With a data cloud, data scientists and analytics teams can unlock the insights needed to accelerate innovation at every stage of the product life cycle, from discovery and development to manufacturing and commercialization.

Accelerate data performance

A data cloud platform allows quick and easy processing of information from disparate sources and organizes it into a single location. It has the ability to concurrently run extract-transform-load (ETL) processes and data workloads while servicing data requests from multiple users. This allows teams to have readily available access to self-service analytics and real-time data to make well-informed decisions. Fewer performance lags translate to accelerated innovation and shorter time to market for life-saving products.

Facilitate enhanced data sharing and collaboration

With secure, seamless, and governed exchange of sensitive data at scale, organizations can easily share data and collaborate with other organizations. The data sharing capabilities of cloud platforms are built on top of secure data sharing technology that allows organizations to give internal and external users access to live, ready-to-query data sets without having to move, copy, or transfer data. Organizations can also quickly combine public data sets with their own data to gain data diversity that enables deeper insights and better data-driven decisions.

Improve data management and scalability

A data cloud platform is automated and based on self-service, thus enabling life science companies to focus on their core business instead of IT management. With near-zero maintenance, the platform provides a simple-to-use and cost-efficient solution to increase productivity. With multi-cluster and shared data architecture that separates storage and compute, businesses can scale instantly and near-infinitely, without downtime or disruption. Such a platform can support virtually any amount of data, workloads, and concurrent users and applications without requiring data movement or copies.

Build a robust data compliance strategy

Stringent regulations and quality guidelines regulate life sciences organizations to ensure medical products are safe for consumers. With a cloud data platform, such businesses can adhere to guidelines and industry standards and best practices with automation and vigilance. Additionally, such a platform provides an extensive portfolio of security certifications and granular controls that enable secure and governed access to all data.

Soon also asserts that life science organizations need the compute power and flexibility offered by the data cloud to discover, collaborate, and generate value from data regardless of where it resides and turn data into mission-critical insights.

“Moreover, with accessibility and ease of data integration, life science companies can forge new partnerships and tighter data connections across business ecosystems. With the help of technology and through a truly data-driven approach, these organizations can focus on developing and delivering life-saving treatments and devices, which could help address ever-increasing medical and pharmaceutical costs and improve the quality of care,” said Soon.