The five biggest data challenges for life sciences
Vimal Venkatram, Country Manager, Snowflake India explains that to be prepared for the future, all types of life sciences organisations from biopharma to medtech companies will need to find new ways to create value along with new metrics that will help them make sense of today’s wealth of data
The life sciences industry is at a turning point. To prepare for the future and remain relevant in the ever-evolving business landscape, biopharma 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 treatments.
In the wake of the COVID-19 pandemic, organisations rushed to analyse unprecedented volumes of data in the race to develop the COVID-19 vaccine. As per Precedence Research, the Life Science Analytics market had a global value of $7.57 billion in 2019 and is projected to reach an estimated value of $18.12 Billion by 2030, expanding at a CAGR of 8.25 per cent.
Precedence Research also states that the rising penetration of big data usage in healthcare has boosted the life science care analytics segment. Data standardisation has become key in life science analytics.
To be prepared for the future, all types of life sciences organisations from biopharma to medtech companies will need to find new ways to create value along with new metrics that will help them make sense of today’s wealth of data. The exploding volume and variety of data pose significant management and security challenges for life sciences companies using outdated legacy on-premises and cloud database systems. Additionally, these legacy systems hinder life sciences organisations from attaining the level of data diversity they need to improve business processes and make critical decisions.
Here are five common challenges life sciences companies face in leveraging data for better therapeutic and business outcomes:
To conduct R&D and clinical trials and manage day-to-day business, life sciences companies need to process a vast amount of real-world data that comes in a wide variety of formats. Life sciences companies futilely spend precious time ingesting, cleaning, and organising the data, but legacy data warehouses cannot deliver data in a way that enables fast accurate analysis and insights. In addition, the data often sits in two silos: commercial, for data such as sales and marketing records, and regulated, for data such as clinical trial and laboratory reports.
To reach actionable insights qui