Artificial intelligence (AI) has the potential to fundamentally change healthcare and the life sciences that underpin it. Digital twins and computer vision are already proving better than humans at the prediction of disease or acute medical events, diagnosis of existing diseases and injuries, and design of potential treatments. There are two substantial roadblocks to the… Read More
In the rapidly evolving life sciences industry, access to historical data is crucial for operations from research to drug development, manufacturing, and clinical trials. However, the necessary data often resides in disparate systems, formats, and locations, making it challenging (and expensive) to harness its full potential. This is where ETL (Extract, Transform, Load) processes come… Read More
Decoupling data inputs from their isolated sources continues to be challenging for organizations. Data silos not only hinder organizational collaboration and decision making but also undermine an organization’s ability to unlock the potential of its most precious resource—its data. Advancements like cloud computing, platforms, and blockchain are giving most organizations the tools to transition away from… Read More
Businesses in the life sciences sector are navigating complex decisions regarding their laboratory data management. They face demands for data accuracy, compliance, and integrity that drive decisions about data platform use. A platform serves as a bespoke hub for data regardless of which or how many data sources are involved. Your platform choice can be… Read More
Today, it seems data analytics is being used everywhere. In this rapidly evolving business landscape, data analytics has emerged as a powerful tool for organizations to gain valuable insights, make informed decisions, and achieve strategic objectives. Data applications are no longer limited to just the realms of marketing and finance; they have permeated various sectors,… Read More