Adoption of Snowflake in the Pharmaceutical Industry: A Brief Introduction

Traditionally, cloud-based data warehousing technologies presented significant value in healthcare by giving medical service providers a platform to wirelessly collect data for storage, computation, accessibility and sharing. Medical device manufacturers and healthcare providers also offered big data management services to clients without relying on traditional computing databases. In life sciences though, the benefits of cloud-based data management systems were seen mostly in drug discovery and distribution systems in supply chain management. But in recent years, cloud-based data warehousing technologies such as Snowflake have made data modernization easier and more widespread in pharmaceutical companies. Snowflake has ushered in a potent and differentiated cloud transformation by mobilizing, storing and sharing data with near-limited scale, concurrency, and performance with a uniquely architected platform for internal and external pharma data.

Life sciences is a highly data driven industry and supply chain and distribution processes also have massive amounts of information that comes from myriad sources and is typically siloed. This data cannot be analysed in isolation and needs a centralized data management platform. Snowflake Data Cloud allows data from multiple structured and unstructured sources to be integrated on a single platform. Data teams can store and process data for operational efficiency, cut costs and develop a robust drug commercialization and distribution chain. Snowflake Data Cloud also ensures seamless data governance and security across pharma companies. It drives innovations and accelerates company-wide use of information for each function and division within a biotech organization. Pharma data is enormous and complex especially when the whole value chain is considered right from research and development through the marketing of products. Snowflake Data Cloud helps build a data environment for unifying and allowing mutual use of internal data to accelerate innovation. The cloud-based data warehouse drives assimilation and use of data across various divisions within an organization.

Since pharma manufacturers face a competitive landscape, added contest and ongoing pricing pressures, access restrictions and industry consolidation forces companies to re-evaluate their drug launch and sales marketing strategies. Market intelligence and data-driven insights cannot be accessed from traditional data sets and Snowflake outperforms conventional data warehousing and management solutions to offer easier, flexible and more scalable virtual data.

At Chugai Pharmaceuticals, the aim was to enhance and accelerate the creation of innovative new drugs and distribute them in the global market. It needed a safe and speedy analytics environment for a large amount of distributive data that was both sensitive and useful. Snowflake offered that support and provided a platform that integrated data across all centers to further improve delivery of information to healthcare providers. Snowflake Data Cloud helped the company mobilize data and make it less diverse and scattered. It united siloed data and securely shared and executed diverse analytic workloads across the organization. In addition to this, Snowflake also helped the company rely on a single public cloud to offer efficient access.

Chugai integrated the data owned by separate departments and projects on the Snowflake Data Cloud and connected it to its own digital-IT platform known as the Chugai Scientific Infrastructure (CSI) to develop company-wide analytics environment. This strengthened the data governance, reduced data risks, and lowered costs of storing data. At the drug discovery function, Snowflake helped maximize data value and improve productivity including accelerated evaluation of pharmaceutical candidates by simulation based on a large amount of reliable experimental data and hypotheses, and cross-modality analyses. At the value delivery function, Chugai advanced an innovative customer engagement model to deliver value that customers need through optimal communication. The company used Snowflake to integrate and use the internal data for predicting effectiveness and safety to create unique evidence contributing to personalized medicine. This was the first deployment of Snowflake in the Japanese healthcare and life sciences environment and enabled the company to deliver stronger outcomes driven by data for patients and users.

Life Sciences giant Novartis uses Snowflake technology as a part of integrated approach to cloud and data helping the company accelerate the development of medicines. Novartis adopted Snowflake in 2017 as part of a broader effort to digitize every aspect of its operations. The biggest impact has been in the time it takes to develop new drugs. From initial research through manufacturing, trials and distribution of new drugs take about 12 years. The predicament of most drug companies is to allow users to access drugs in a fast and efficient manner. By applying data and artificial intelligence to these processes, Novartis has reduced that time to nine years and lowered cost of new drugs for consumers.

Before the adoption of Snowflake, the company was struggling to meet the demands of business with its teams conducting tests to bring new products to the market. The company needed a fresh cloud-based approach and adopted Snowflake as an ongoing effort known as Formula One to digitize every aspect of the company’s operation to bring data to the heart of its processes. The data cloud is ingrained into Novartis’s business processes. The technology is used for analytics generation across multi-cloud platforms including AWS and Azure to ingest data and prepare it for insight generation. This enabled a sharing of data across departments and the wider health ecosystem. Snowflake has become an integral element of Novartis’s best-of-breed approach to its IT architecture. It is used in the business refinement layer that is all about creating aggregated data to serve analytics generation, insight generation and data-sharing processes. Snowflake Data Cloud not only plays a crucial role in reducing the time to produce new drugs, but it also refines and improves performance across business operations. The company also migrated to the newly launched Healthcare Data Cloud to create a single, integrated cross-cloud data platform and eliminate organization-wide institutional silos.

Besides, there are few other milestones such as dosing of first patient and recruitment completion that indicate overall progress of the competitor’s development program. Trials are often terminated/ suspended due to inability to recruit eligible patients in a timely manner. Monitoring updates in trial locations is also useful as it helps in identifying the key geographies that our competitor may be eyeing.
With strong CI tactics, research and strategy teams can track industry failures and prevent pharma companies from making the same errors in drug development and save precious resources. It helps companies to stay updated about latest breakthroughs in therapies and trials and gives them a good direction to re-establish and scale up drug development plans.
Clinical trial databases help CI professionals gather data wherein they can analyze both the competitor’s high-level objectives, such as the clinical trial endpoints, inclusion - exclusion criteria, cohorts, dosing, enrolment as well as the company’s strategies to analyze how to best benchmark development strategies. Drug companies also keep an eye out for new technologies, treatment processes employed in clinical trials by the competitors, as they could potentially shift the market. This also helps the drug companies plan their production timeline, and marketing strategies accordingly.

Snowflake’s architecture makes it a winner among data warehousing technologies in the market. Its services can be provisioned and deployed in minutes. Business decision-makers have access to vast amounts of information that is not necessarily in a form conducive for insight generation and deep analysis. They face challenges related to structure, granularity, quality of data among other things. Snowflake allows them to set up an environment to interrogate their data and determine whether it has enough value for the prediction of market activities. It allows pharma majors to have a robust infrastructure for data science. Simple provisioning, elastic scalability, and integration with familiar tools shortens the setup time and reduces the effort to mine data. Snowflake allows the agility that traditional data warehousing tools don’t allow. Businesses need to be agile when using data across its operations. Snowflake works outside the traditional data warehousing environments and helps pharma majors stay ahead in data analysis.