How Biotech Companies Can Wrangle the Onslaught of Data with AI to Shape the Industry’s Future

AI for wrangling data promises exciting opportunities for enhancing healthcare and driving scientific breakthroughs in biotech.

AI algorithms can swiftly and accurately identify potential drug targets, speeding up the development of life-saving medications. Predictive modeling can help detect diseases early, leading to better treatments and improved patient outcomes.

Beyond scientific breakthroughs, those working in the field yield tangible benefits. Researchers can make smarter decisions, refine their methods, and speed up the pace of discovery.

AI isn’t just shaping the future of biotech—it’s transforming it.

The Explosion of Data in Biotech

The explosion of data in biotechnology, particularly in genomics, proteomics, and pharmacogenomics, has been monumental. The European Bioinformatics Institute (EMBL-EBI) reports that data types such as nucleotide and proteomics are doubling roughly every year, contributing to a “data deluge” that challenges even the most seasoned researchers​​.

It is not enough to simply have a lot of data, however. Leveraging AI is about discovering valuable insights hidden within those massive amounts of data. Using advanced analytics tools, researchers can uncover patterns, correlations, and trends within complex datasets, leading to more informed decision-making.

In areas such as pharmacology, toxicology, and pharmaceutics, the use of AI has enhanced drug discovery, safety testing, and personalized medicine. Researchers can quickly sift through data to predict how well drugs will work, understand their effects on the body, and speed up the process of developing new drugs. They can revolutionize the way drugs are developed, tested, and tailored to individual patients’ needs.

While the rapid growth in data presents exciting opportunities, it also poses significant challenges in terms of data storage, management, and analysis. Researchers are continuously seeking innovative solutions to effectively manage, analyze, and leverage the vast amounts of data for R&D.

That’s where AI-based technologies come into the picture.

Machine learning is instrumental in predictive modeling and simulation studies. Supervised machine learning in particular shows promise in uncovering new knowledge. This involves extracting general principles from labeled instances, such as distinguishing between cancerous and normal cells based on gene expression patterns or other characteristics. These principles can then be applied to new, unlabeled data to predict outcomes or classify new samples, accelerating the drug discovery process and leading to more effective treatments.

The Internet of Things has had an impact on the way companies manage and collect data in the field. For example, IoT implementation involves using advanced architectures supported by Big Data (BD) technologies like Apache HBase and Apache Pig. This allows for real-time monitoring of patient progress and response to treatment plans, optimizing bioprocesses and driving the development of new products. This streamlined approach not only improves research methods but also boosts the overall efficiency and effectiveness of biotech operations.

With the support of AI, companies can also automate data collection, analysis, and interpretation, minimizing the risk of errors. This shift toward automation not only boosts productivity but also speeds up the pace of scientific discovery and innovation in the biotech sector.

What’s Holding Biotech Companies Back?

Big Data holds enormous promise for transforming the biotech landscape, opening doors to innovative drug development, deeper disease insights, and enhanced patient care. Yet, hurdles must be cleared before harnessing Big Data’s full potential.

1. Specialized Skills and Infrastructure Needs

Recent research highlights the expanding role of Big Data across sub-disciplines such as pharmacology, toxicology, and pharmaceutics. The core of this growth lies in the sector’s capacity to efficiently manage and analyze increasingly large datasets.

This requires a workforce proficient in Big Data methodologies and technologies, coupled with a strong computational infrastructure to facilitate data-heavy operations.

2. Data Security and Privacy Concerns

The wealth of sensitive information stored within biotech databases, including patient records and genomic data, necessitates stringent security measures to protect against unauthorized access and breaches.

These concerns are amplified by the complexity and scale of Big Data, where data from diverse sources are integrated and analyzed, potentially exposing vulnerabilities in data handling and storage practices​​. Biotech companies must employ comprehensive technological and organizational security measures.

3. Lack of Standardization and Interoperability

Recent findings underline the struggle in integrating and analyzing data from different sources because of differing formats and standards. As the biotech industry increasingly relies on data from diverse sources like electronic health records (EHRs), genomic databases, and clinical trials, the importance of common data standards and systems that can work together becomes paramount. Resolving these issues ensures smooth sharing and analysis across platforms.

4. The Overwhelming Nature of Big Data

The sheer volume of data generated in the biotech sector, estimated at 2.5 zettabytes globally, presents a daunting task in data curation, analysis, and interpretation​​, even for the most seasoned researchers. The challenge lies not only in managing this data deluge but also in extracting meaningful insights. Effective data visualization tools and analytics can make Big Data more accessible and actionable for biotech researchers and practitioners.

Looking to the Future

The promise of these advancements lies not only in their capacity to drive innovation but also in their potential to improve patient outcomes globally. The biotech sector is poised to unlock new discoveries, streamline drug development, and enhance patient care.

Enter: NetSuite. NetSuite offers a comprehensive cloud-based platform that meets the diverse needs of the biotech sector, enabling firms to effectively harness data to move toward this vision. It has embedded AI capabilities across the suite to boost productivity and help leaders analyze data faster.

NetSuite, as a business unit within Oracle, has access to the leading AI technology, partnerships, and infrastructure capabilities of one of the world’s largest software companies. The platform runs on Oracle Cloud Infrastructure (OCI) data centers and Oracle Autonomous Database, as well, providing the performance and prebuilt machine learning models needed to power AI-driven strategies.

NetSuite’s ability to integrate with specialized biotech software tools further enhances operational efficiency.

Discover the transformative impact NetSuite can have on your goals by reaching out today. Together, we can unlock the potential of advanced software solutions to propel the industry towards a brighter, more innovative future.