Struggling with Agility? Its Time to Move Towards Cloud-Based Data Analytics Approach
By Anand Kumar - Senior Vice President
Oct 27, 2021
We’re in an era where pharmaceutical companies can predict whether an individual will switch to a new prescription drug based on data captured from that person’s Twitter feed. But many life sciences organizations struggle to rapidly analyze the treasure trove of data they now have access to—and it’s holding them back from innovation.
Today, life sciences companies consume massive amounts of data from digital applications and devices, research partnerships and more. This influx of real-world evidence fuels breakthrough discoveries that improve health, personalize care and drive value. But it also puts tremendous pressure on life sciences companies to:
- Catalog their data correctly—from data sets to audio files, video files, images and text dictations—so the data can be used not just in the moment, but a year from now, 10 years from now, and longer. When organizations are unable to find the data they need quickly, that data is no longer useful. Adding to this challenge: the need to digitize massive volumes of legacy data.
- Convert data sources into a form that can be used for analysis. To run data science algorithms, data must be curated and prepped for analysis. One of the biggest stumbling blocks life sciences organizations face is that there isn’t an automated way of performing these tasks. As a result, data scientists spend at least half their time finding, cleaning and preparing data—a process that can take months, given the amount of structured and unstructured data organizations capture.
- Derive meaningful insight from their data quickly. Without the tools to rapidly collect, manage and analyze data at scale, organizations miss vital opportunities to advance clinical discovery.
It’s one reason why more life sciences companies are shifting to a cloud-based data analytics approach.
The Power of Cloud-Based Analytics
Agility depends on bringing cutting-edge insight to market quickly. When data scientists spend months cataloging data and prepping the data for analysis, organizations lose valuable time that could have been spent on innovation—and it threatens their ability to compete.
That’s why leading life sciences organizations are partnering with cloud-based managed services teams to prep and curate their data. At Healthcare Triangle, this process takes just six to eight weeks, compared to six to nine months, on average, when organizations perform this work on their own.
Organizations also are finding value in cloud-based analytic platforms that allow them to pay only for the capabilities they need. At Healthcare Triangle, our HITRUST certified DataEz platform empowers organizations to access advanced analytic solutions—including AI and machine learning—to unlock meaningful insight from large quantities of data quickly. That’s especially valuable in fields like genetics, where public archives for raw sequencing data double in size every 18 months. It’s a practical and cost-effective way for life sciences and healthcare organizations to deliver on their data strategy with advanced analytics and highly agile, highly secure data infrastructure.
Real-World Impact of Cloud-Based Agility
We’re seeing exciting examples of life sciences organizations that are future proofing their data analytics platform with cloud-based capabilities—with outstanding results:
- A top 10 pharmaceutical provider with large amounts of real-world data found that clinical queries were taking days to complete, and with data anticipated to increase tenfold in the immediate future, the pharma company sought a scalable platform that would meet the pharma industry’s high security and compliance requirements. Within three months, Healthcare Triangle’s DataEz platform enabled the pharma company to:
- Ingest data faster while seamlessly adding new data sources for analysis
- Process even the most complex queries in minutes instead of days
- A senior care provider needed an intuitive dashboard that could ingest digital health data from remote monitoring devices and sensors as well as a mobile app that could continually share seniors’ health details with their providers and family members. With DataEz, Healthcare Triangle provided:
- A cloud-based data lake where the data from various devices and sensors are ingested and stored through automated provisioning
- A scalable dashboard capable of monitoring hundreds of thousands of patients at a time
- AI and machine learning for predictive analytics and detection of health anomalies
Find out more about the ways in which a cloud-based approach to data analytics could transform your ability to innovate. Contact us today at email@example.com.
Anand Kumar is senior vice president, Healthcare Triangle.