5 Ways Data Cloud Can Help Life Sciences Become Data-Driven
Geoff Soon, Managing Director, South Asia, Snowflake
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. Many companies are looking to leverage new age technologies such as Artificial Intelligence (AI), Machine Learning (ML), and automation to accelerate the discovery and development of drugs and treatments.
However, for life science organisations using outdated legacy on-premises and cloud database systems, the exploding volume of data poses significant management challenges. The need to process real-world data from a wide variety of formats to conduct research and development, run clinical trials, and manage day-to-day business has also raised data security issues. As legacy data warehouses cannot deliver data in a way that enables fast, accurate analysis and insights, companies spend precious time gathering, ingesting, cleaning, and organising the data. Many healthcare organisations experience challenges with safeguarding and protecting patient information, as well as 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 for COVID-19, organisations within the sector are realising the value the Data Cloud has to offer.
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In Singapore, data and technology played critical roles with the government’s contact tracing efforts. Using contact tracing apps, detailed activity maps of patient’s movements and digital footprints such as credit card or automated teller machines (ATMs) activities, authorities and enforcement agencies were able to identify links to confirmed cases and potential sources of transmission. As a result, COVID-19 transmission was successfully contained, helping prevent a large-scale outbreak.
Here are five ways life science companies can leverage the Data Cloud to drive better decision-making with data.
1. Access a diverse set of data: The 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 centralised repository. From there, data scientists can use automated organisation tools to analyse the data more quickly and efficiently. With the 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 commercialisation.
2. Accelerate data performance: The platform can quickly and easily process information from disparate sources and organise 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.
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3. Facilitate data sharing and collaboration: With secure, seamless, and governed exchange of sensitive data at scale, organisations can easily share data and collaborate with other organisations. Data sharing capabilities of cloud platforms are built on top of secure data sharing technology which allows organisations to give internal and external users access to live, ready-to-query data sets without having to move, copy, or transfer data. Companies can also combine public data sets with their own data to gain data diversity that enables deeper insights and better data-driven decisions.
4. Improve data management and scalability: The platform is automated and self-service, 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. The system can support virtually any amount of data, workloads, and concurrent users and applications without requiring data movement or copies.
5. Build a robust data compliance strategy: In the life sciences industry, companies must comply with stringent regulations and quality guidelines that regulate practices in various settings to ensure medical products are safe for consumers. The platform adheres to these guidelines to help organisations validate their workloads and meet the industry’s standards and best practices. In addition, the platform provides an extensive portfolio of security certifications and granular controls that enable secure and governed access to all data.
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To discover, collaborate, and generate value from data regardless of where it resides and turn data into mission-critical insights, life science companies need the compute power and flexibility offered by the Data Cloud. 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, life science organisations can focus on developing and delivering life-saving treatments and devices, which could help address the ever-increasing medical and pharmaceutical costs and improve the quality of care.
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Author: Geoff Soon, Managing Director, South Asia, Snowflake
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