Serverless Architecture vs. Monolithic Architecture: What’s the Difference?
Serverless For Unstructured Data Problems in Life Sciences
Many industries, including life sciences, require structured data housed in their database for corporate operations and offers. Data from external sources may already be available. In many circumstances, a single source suffices. However, many data sources from various vendors must frequently be combined. Each data source may have a different structure for the same data field, which is troublesome. A classic data mining task is achieving a unified structure from multiple sources. In this blog, let’s look at how serverless addresses the challenge of unstructured data in the health sciences.
Challenges of Unstructured Data in Life Sciences
1. Mining of Manufacturing Data
More than any other industry, the success of the life science industry is dependent on the gathering, processing, and utilisation of data. However, as data storage and mining techniques make significant improvements in other industries, the life science business must adapt to fully capitalise on this potential competitive edge in product discovery, development, and marketing.
Data mining is a process that finds patterns and relationships in data by employing various analysis and modelling tools. These patterns can produce reliable predictions that aid problem-solving across the drug development spectrum, including R&D, clinical trials, and marketing. However, some issues include supply chain disruptions, data security, and a lack of transparency.
2. Data Produced During Drug Discovery
Over the years, drug discovery has resulted in numerous breakthroughs. New technology has been a primary driver of such advancements, with discoveries in assay technology, automation, imaging, nanofluidics, and software assisting in considerably improving the drug development process. While significant breakthroughs in drug discovery have reduced the process’s timings, complexity, and expense and improved its precision, there is still a long way to go. These facts continue to pose difficulties for modern medication research.
3. Molecule Research
Molecular life science is one of the fastest-growing disciplines of scientific and technological innovation. In addition, biotechnology has far-reaching implications in many parts of daily life---often with serious ethical implications.
At the same time, the content is extremely complicated and highly conceptual. It is deeply rooted in a wide range of disciplines, from pure sciences such as math, chemistry, and physics to applied sciences such as medicine and agriculture, to subjects traditionally associated with the humanities, most notably philosophy and ethics. These characteristics, taken together, represent various crucial and fascinating challenges for tomorrow’s instructors and educational institutions.
4. Experiments & Clinical Trials
Conducting a clinical trial necessitates the collaboration of a wide collection of stakeholders, including research sponsors from academia, industry, government, and non-profit organisations, as well as patients, clinical investigators, clinicians, and regulators. Clinical research infrastructure, medical supplies, an informatics support system, and labour are required for trial completion.
Clinical specialists working for contract research organisations (CROs), pharmaceutical corporations, and biotechnology firms are focused on overcoming various obstacles in this industry. Numerous obstacles exist in the healthcare industry, including patient recruitment, technology acceptance, spiralling prices, and regulatory constraints.
How Serverless is Helping Life Sciences Solve Unstructured Data Problems
Healthcare digitisation has been a major game changer in recent years, spurred by the increasing expansion of medical devices and point-of-care software. The fundamental advantage of serverless technology over conventional cloud-based or server-centric architecture is that it relieves developers of the burden of purchasing, installing, and managing backend servers, resulting in faster release time and less ongoing maintenance. Additionally, under the appropriate conditions, serverless can typically lower cloud prices because charging is purely based on resources used, with no overheads for keeping unused capacity.
Serverless designs will lower expenses for applications that experience erratic usage, with peak periods interspersed with periods of little to no traffic. Purchasing a server or a block of continually running servers and always being available, even when inactive, could be a waste of resources for such applications. When not in use, a serverless configuration responds promptly and incurs no expenditures.
Bottom Line
Organisations in the life sciences now have the means and technologies to bring new and improved products to market faster than ever. There is an urgent need to upgrade data management procedures with the adoption of serverless systems. Chasing unstructured data, in particular, is slowing the process of exploiting new types of clinical data in research and creating a data analytics blind spot. Using serverless data management strategies to leverage clinical and laboratory imaging, patient files containing telehealth video, sensor data via gadgets, research files, and other data is, thus, a must-have skill for life sciences leaders.
