Applying Structure to Your Unstructured Data
Today’s data landscapes are more dispersed and diverse than ever before. For example, 97% of enterprises use at least one type of cloud deployment, with the majority deploying hybrid or multi-cloud environments. Further, industry analysts estimate that 80-90% of the new data being stored in these environments is unstructured.
This scenario creates a number of complexities for organizations looking to harness this valuable data. First, this data needs to be identified and collected across multiple storage locations and once this process is complete the work has just begun. Then teams have to sift through and prepare the data – a process that can be a significant burden when considering the complexities of these data types such as:
- Published research
- Market analyses
- Quarterly reports
- Lab notes
Traditionally, analyzing this data requires manually searching through and extracting relevant data points – a task that can take days, weeks or months to complete depending on the size of the desired data set. For example, life science researchers undergoing systematic literature reviews anticipate each review to take months to complete, with each project costing roughly $140,000.
A New Way Forward with Deep Learning
Recent advancements in deep learning, known as large language models (LLMs), have presented a paradigm shift in how organizations can access, analyze and harness new value from this data.
Unlike a traditional model that processes each term separately and outside the context of the sequence, LLMs use self-attention to build rich representations of each constituent in the data span, allowing these models to understand the relevance of the location of a term, the relation of one term to the next (even if far away from each other) and more. When trained on larger datasets, these models reach remarkable accuracy and recall for understanding unstructured data like large documents of natural language text.
LLMs are being applied to a variety of use cases to help address unstructured data challenges. For example, Vyasa’s Synapse smart spreadsheet technology applies LLMs to sets of unstructured documents and allows organizations to autonomously extract key data points into structured spreadsheets.
At Vyasa, we’ve helped life science consultancies leverage these models to improve productivity for systematic literature reviews, pharmaceutical companies improve early research, medical research centers enhance clinical trial enrollment, healthcare providers reduce time spent reporting and organizations improve call center transcript analysis.
Watch our video below with SAP to learn about how we’re improving unstructured text analysis for a joint customer.