Sudish Mogul, CTO of Healthcare Triangle

Across the life sciences, experts are boldly predicting that data is the new currency and mobilizing data from clinical trials, wearables and even social media will drive innovation in 2022. But mining Data for innovation depends not only on data analysis, but also on access to the right data and timely analysis.

As the amount of data life science experts have at their fingertips explodes, with leaders harnessing real-world evidence from smartphone apps, wearables and even Twitter for discovery clinical practice, more than 66% of healthcare and life sciences executives say their companies use data to drive innovation with data. Meanwhile, half of life science leaders report increased investment in data.

Yet even as digital transformation and advanced analytics propel data-fueled discovery — with experts predicting biopharmaceutical innovation advanced 10 years in the first 10 months of the pandemic alone — science companies from life still face challenges to capture and analyze data in a timely manner. These data breakdowns hinder research. They are also slowing down efforts to get new drugs and products into the hands of consumers.

Facilitating the path to data-powered discovery

How can life sciences companies eliminate the data breakdowns that prevent more effective research and discovery? Here are three approaches to consider.

#1: Resolve delays in data processing and analysis. Quite often, life science organizations think they have to handle data processing internally. They create their own data analytics platforms and rely on employees to aggregate data from disparate sources and extract metadata for analysis. But building a data analytics platform in-house is an expensive proposition. This requires the right skills at a time when there is a shortage of data analysts and data scientists. And, at a time when the amount of real-world evidence available for analysis continues to grow, life science companies need highly modular, scalable, and secure platforms for engineering and analysis. of AI. It’s a combination of attributes that shouldn’t be left to chance.

Instead of building and deploying their data analytics platform, life science teams could boost their competitiveness by focusing on getting the data they need for research and development. Today, large enterprises are investing in data-as-a-service platforms that transfer the responsibility for collecting and managing data and maintaining their data infrastructure to an external partner. This frees data scientists to identify sources of data that will better position their business to compete for innovation. It also gives data analysts a more agile platform to apply artificial intelligence (AI) and machine learning (ML) to their data to advance research that improves health.

No. 2: Make sure your digital platform can handle the continuous increase in real-world data. As the volume of data available for real-world analysis continues to grow, life science organizations should be aware that their data platform may scale as demands for rapid analysis of large amounts of data becomes more intense. The key to this effort: creating an infrastructure flexible enough to handle any data workflow. For example, a cloud-based data platform provides greater total capacity and access to a greater variety of services for ingesting, processing, and gaining insights from data. Such a platform provides a basic foundation for large-scale collaboration. It also supports efforts to reanalyze archived data, including privacy-protected data.

A global life sciences company used to keep its data in a proprietary format, but executives found that this approach strangled its ability to quickly extract data for analysis. The company worked with an external vendor to create an automated and highly secure platform capable of normalizing proprietary datasets, identifying and extracting metadata, and storing the data in a data warehouse for access and easier analysis. The impact: The company can now interrogate its entire dataset for research that powers breakthrough discoveries in clinical diagnostics.

#3: Consider a cloud-based data analysis approach. Today, there are software-as-a-service solutions that allow life science companies to pay only for the data analysis capabilities they need. This gives organizations instant access to state-of-the-art security and advanced analytics that unlock meaningful insights from large amounts of data. This is especially valuable in fields like genetics, where public archives for raw sequencing data double in size every 18 months. It also provides a convenient and cost-effective way for life science organizations to implement their data strategy.

For example, some life science organizations rely on cloud-based analytics to automate data lake management. It’s an approach that frees researchers, data scientists, and IT teams to focus on more strategic initiatives, such as use cases and applications discovered through data analysis.

A more modern method for rapid discovery

Advances in data discovery in the pharmaceutical and biotechnology sector are changing lives around the world, but life science companies need an agile approach to data capture, processing and analysis to stay one step ahead. Eliminating breakdowns in data processing and discovery will strengthen the impact of organizations in 2022 and beyond.


About Southern Mogul

Sudish Mogul is the Chief Technology Officer of Healthcare Triangle. As CTO, Sudish leads the overall strategic vision for Healthcare Triangle and is responsible for product strategies of next-generation applications and solutions for customers in regulated industries such as life sciences, pharmaceutical, healthcare and financial services. .

Prior to Healthcare Triangle, he held several leadership positions at Cisco Systems, leading strategy and product development groups in the areas of cloud-based unified collaboration, network design and management, and corporate video.