Agentic AI Is Ready to Solve Scientific R&D's Hard Problems
The conversation around AI in scientific R&D has shifted dramatically in a short period of time. Fields like chemistry, materials science, and life...
3 min read
Data, Data Everywhere
The digital age has introduced massive amounts of data and automation into the R&D process, irrevocably changing how scientific research is conducted. The recent advent of generative AI and large language models (LLMs) has only exponentially accelerated this shift.
Research today requires handling multi-dimensional datasets, running intricate simulations, and deciphering complex experimental outcomes. R&D data has transformed from being an asset to be managed into the secret sauce of a company’s innovation and competitive advantage. However, despite scientific data holding this massive potential, much of it remains untapped. The reasons why begin with the data itself.
The biggest byproduct of modern R&D, and one of the most challenging and exciting, is the sheer amount of research data available to scientists—historical data, data collected from experiments and instruments, data newly generated from predictive analysis, etc. How to bring it all together so it can be leveraged is complicated and overwhelming—and only resolvable with technology.
The Data Silo Problem
Most labs today operate under the data systems status quo. They have piles of inaccessible and incompatible data stored in a myriad of locations and formats, unusable for robust analysis and collaboration. Frustrating bottlenecks bring workflows to a crawl and hinder discovery and exploration.
The data is scattered and siloed in a multitude of locations: data warehouses, data lakes, data lakehouses, laptops, instruments, public databases, and collaborator’s data sources. The data takes many forms and formats, such as images, graphs, spectra, and genetic sequences, making it challenging for systems to talk to each other for automated analysis. Compounding these challenges is the deluge of new data generated on a daily basis.
Companies who deployed new R&D data management software just a few years ago are already feeling the limits of their tools as the volume, velocity, and variety of their research data continue to grow. Even many platforms currently on the market are not equipped to manage today’s multifaceted scientific data needs efficiently.
Without an agile and flexible data system design to address the data silo problem, R&D organizations fall behind and are unable to take advantage of advanced technologies like machine learning and AI. This widening gap plays out in market success and market share.
The answer? The data fabric.
What is a Data Fabric?
A data fabric is an advanced design that seamlessly integrates disparate data sources and types across various environments—on-premises, cloud, or hybrid systems— into a cohesive and interconnected architecture.
Applicable in all industries, the data fabric architecture is especially relevant for scientific domains and research due to the complexities of scientific data. In practice, a strong data fabric in R&D removes data silos and analysis bottlenecks that scientists experience, allowing them to focus on their science, thereby accelerating discovery and productivity.
The Component Layers of a Data Fabric
A data fabric is not a set of technologies but a set of virtualization layers that securely facilitate data access, ingestion, and sharing across an organization or enterprise. Forrester presents a data fabric being comprised of six component layers:
- Data Management. Central to the architecture, this layer emphasizes the governance and security protocols essential for safeguarding data.
- Data Ingestion: Acting as the integrator, this layer weaves together data from scattered sources and diverse formats.
- Data Processing: This component is dedicated to filtering the data, ensuring that only pertinent information is elevated for subsequent extraction processes.
- Data Orchestration: A pivotal layer, it undertakes crucial tasks such as data transformation, integration, and cleansing, rendering the data usable.
- Data Discovery: This innovative layer uncovers potential integration avenues between disparate data systems.
- Data Access: Serving as the gateway for data consumption, this layer manages permissions in line with regulations and policies, while feeding interactive dashboards for users.
The Path to Modern Research
The benefits of a data fabric in scientific R&D are immense. Enthought has seen customers seamlessly eliminate bottlenecks, leverage previously unused data, and significantly reduce IT burden.
If you share some of these common challenges and pain points, you need a data fabric as a part of your lab’s technology solution set:
- internal data siloed in ELNs, databases, data warehouses, data lakes, and clouds
- massive quantities of data and metadata generated across multiple projects and by different scientists
- data sitting on individual computers in Excel, text docs, and PDF files, unmanaged and unused
- data from public sources that are incompatible with internal software, requiring manual wrangling
- cumbersome and clunky analysis of structured and unstructured data
- technical barriers just to share and iterate on data with collaborators
Scientific research data will only grow in complexity and volume and generative AI and LLMs will continue to amaze. Having a robust, flexible, and efficient data architecture is essential to keep up. By integrating a data fabric, R&D organizations can overcome the challenges of today and have the foundation set for what comes next.
Want to learn more? Contact us to talk to an Enthought expert about integrating data fabric into your lab today.
Check out more resources at enthought.com/resources.
Building Toward Materials by Design in R&D
This content was originally discussed in the webinar, A Technical Framework for Materials by Design in Enterprise R&D.
How Predictive AI Works As An ROI Engine For R&D
This article was originally published on Forbes.