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What You'll Learn
Whether you are a life science research or manufacturing company, you require data for drug development, pre-clinical research, planning production, inventory management, customer relationship management, and more. Now is the time to invest in quality data and its effective management!
The blog will cover three data trends in life science, complemented with best examples and practices.
Interesting Statistics to Note
The Life Science Analytics market had a market value of USD 9.89 billion out of 2023 and is expected to reach USD 18.96 billion by 2031.
Life Sciences companies are making 45% of their investments in popular analytics-related technologies like AI, industrial ML, and cloud & edge computing.
58% of Life Science organizations agree that big data is important for the drug development industry.
The Personalized Medicine market has grown at 11% CAGR under the influence of big data and artificial intelligence.
Trend #1: The Adoption of Cloud Data Platforms is Rising
McKinsey claims that cloud technologies can enable life sciences companies to deliver new services in less than 6 months and cut deployment lead times from days to just a few hours.
Around 49% of life sciences CIOs planned to invest in cloud platforms in 2023.
43% of life science organizations are happy with the cloud outcomes they have achieved. The shift to the cloud is growing by leaps and bounds.
94% of companies are already using cloud as their preferred data platform.
Why Cloud Adoption Makes Sense
Fortune 500 Pharma and MedTech are creating great business value from cloud and could reach $1 trillion EBIDTA by 2030. You are a new-age organization, and you need fast solutions for your data environment. Here’s how cloud data platforms are useful:
Cloud platforms are scalable. They accommodate all your growing data and make sure you have trustworthy and consistent data when you need it the most.
You enable a smooth transition from local to global business processes when it comes to optimizing supply chains and standardizing sales operations.
Your enterprise stays ahead of the competition with advanced data and analytics capabilities.
Modern cloud data platform automates essential compliance with critical industry regulations like HIPAA & IDMP. You are at a lower risk of penalties.
Migrating your enterprise data to the cloud brings all the relevant information together in a secure and easily accessible ecosystem.
You reduce costs associated with physical IT infrastructure. All you need is a few clicks to set up your platform.
Consider Snowflake as Your AI Data Cloud – LumenData can take care of platform implementation
Snowflake is a leading option for data cloud. You get to access unstructured, semi-structured, and structured data from a single platform with near-infinite scale.LumenData is a proud Premier Partner with Snowflake. We have enabled several large customers to migrate to Snowflake and grow their instances.
Trend #2: Enterprises are Befriending Generative AI
A Deloitte report says that life sciences companies can drive around 5-7 billion US dollars by using artificial intelligence. Research & development, manufacturing and supply chain, and commercial are the core functional areas that will derive almost 90% of the stated value.
It is estimated that it will take a 5-year time period for any life science organization to gain the maximum business value from the use of AI. The McKinsey Global Institute predicts that Pharmaceutical and MedTech companies could generate $60 billion – $110 billion annually in economic value from generative AI.
Gartner defines generative AI as “AI techniques that learn a representation of artifacts from data, and use it to generate brand-new, unique artifacts that resemble but don’t repeat the original data. These artifacts can serve benign or nefarious purposes. Generative AI can produce totally novel content (including text, images, video, audio, structures), computer code, synthetic data, workflows and models of physical objects. Generative AI also can be used in art, drug discovery or material design.”
Best Business Outcomes with Generative AI
Speeds Up Drug Discovery & Development
The National Library of Medicine claims that it takes almost 10-15 years for a new drug to be approved. Generative AI fast tracks this lengthy process. Generative AI analyzes important medical information such as real-world patient data, genetic research, clinical trial results, and more to predict drug interactions. Meaning? It helps predict the potential side effects of the drug well in advance to boost drug safety. Generative AI also facilitates the discovery of new drug targets.
Improves Research Methods
You cut through the research noise and get simple summaries of scientific literature. Generative AI enables you to perform the categorization of research data. Now, you have different relevant past documents with you. Gen AI helps you with retrieving that information and automates the drafting process of clinical research report deliverables.
Helps With Advanced Data Analysis
Generative AI is changing the way you look at data analytics. It uses your existing data sets to generate new information and hypotheses. Whether you need data to predict disease outcomes or drug reactions – Gen AI has got it all covered. It helps medical professionals and researchers find and analyze sensitive data when they need it the most.
Facilitates Informed Clinical Decision-Making
Gen AI is capable of quickly analyzing mountains of patient data such as medical history, lab results, and demographics. You will be alerted about potential drug interactions and contraindications. You can even predict a patient's risk of developing certain medical conditions.
Trend #3: Enabling Data For All is Becoming a Priority
You want to launch new treatments and medicinal drugs quickly. But the million-dollar question is how do you empower stakeholders to access and share data that’s vital to develop these innovations?
Enter the concept of data democratization! It makes your data accessible, understandable, and usable to a broader audience, rather than restricting access to a few individuals/departments within your organization.Â
Let’s try to understand this with the help of an example. You have different users on your team when it comes to new treatment development. There are healthcare providers and regulators, medical researchers, and others. Every stakeholder will require data in a different format or way that’s fit for their purpose. With data democratization, you provide all users with self-service and responsible access to trusted internal and external data.
We recommend using Snowflake to enable data access for all. Snowflake’s Data Cloud allows you to share sensitive data internally and externally with built-in security and governance capabilities. The platform supports HIPAA, SOC 1 and 2 Type 2, PCI DSS, and FedRAMP requirements.
On-Demand Webinar: Informatica P360 for Life Sciences
How MDM Prepares Life Sciences Organizations for IDMP Compliance
Need help to understand how to work with data to
enable better business outcomes?
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud, and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Xylem, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
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