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
  • Emails 
  • Images
  • Presentations
  • PDFs

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.

Healthcare Fireside Chats Episode 3 – Deep Learning Hits its Stride in Healthcare

The healthcare industry’s expectations around deep learning and A.I. have become a reality as organizations undergo digital transformation and regain control of their data.

According to Gartner, healthcare technology leaders are prioritizing data integration, NLP and data annotation in their AI investments.

However, industry analysts also note that managing & preparing data remains the top time investment for deploying AI.

In this installment of our healthcare fireside chats, Steve Lazer, Global Healthcare & Life Sciences CTO at Dell Technologies joins us to discuss the evolution of AI in healthcare and where we’re seeing implementation in the market today.

Watch more below:

Webinar: Accelerating Healthcare Analytics with Intelligent Data Fabric

Organizations only access 20% of the data they have available to them. The other 80% is difficult to access due to silos, unstructured formats and redundant or obsolete content.

This scenario creates immense challenges for organizations looking to deploy A.I. In fact, IDC’s AI InfrastructureView Survey reports that managing & preparing data remains the top time investment for deploying A.I.

Experts from Vyasa, Dell Technologies & NVIDIA discussed how Vyasa’s Layar intelligent data fabric helps solve this challenge during a recent webinar.

Watch below to learn more about how data fabrics are driving healthcare analytics.

Scaling AI in Healthcare & Life Science with Intelligent Data Fabrics – Vyasa at SC22 with Oracle, NVIDIA

According to IDC’s AI InfrastructureView Survey, only one-third of companies surveyed claim to have reached mature AI adoption. The survey notes that infrastructure remains the most consequential, but least mature decision when deploying AI. 

In most cases, companies build custom environments for deploying AI, all of which require massive amounts of data for their workflows to operate. Unfortunately, this scenario poses challenges by deepening data silos and creating redundant or obsolete data as AI deployments become mature. Over time, this negatively impacts the quality of data teams have access to as well as the productivity and enhanced intelligence companies are looking to achieve through these projects.

A Paradigm Shift in Data Analytics

Innovations in data management and deep learning are addressing this issue and presenting a paradigm shift in how companies access and apply analytics to their data. Known as a data fabric, companies can now overcome silos by making data accessible and searchable in a single platform. 

At Vyasa, we’ve combined this approach with advanced deep learning to create our Layar intelligent data fabric. With Layar, companies can connect to disparate data sources regardless of storage location. Layar then applies transformer-based deep learning to the connected data sources, making the content easily searchable and accessible all in natural language. This enables Layar to expand its understanding of both structured and unstructured data, allowing companies to gain new insight and value across their data landscape. 

As a result, companies can scale AI deployments by applying deep learning models across silos via the Layar data fabric.

Accelerated Analytics with Vyasa, Oracle and NVIDIA

Innovative cloud infrastructure from Oracle leveraging NVIDIA accelerated computing provides a high-performance environment for deploying a Layar data fabric.

As an Independent Software Vendor partner in the NVIDIA Partner Network (NPN)  ISV Partner, Vyasa accelerates these capabilities with the NVIDIA AI Enterprise software suite, which includes essential AI workflow processing engines from NVIDIA, including transformer-based deep learning models like NeMo Megatron, NVIDIA Triton Inference Server, NVIDIA RAPIDS data science libraries and more.

Deep Learning Hits its Stride in Healthcare 

Healthcare technology leaders are prioritizing data integration, NLP and data annotation in their AI investments, according to recent reports from Gartner. The Layar data fabric provides a suite of low-code application interfaces that meets many of these priorities, enabling healthcare and life science teams to:

  • Accelerate early research and systematic literature review
  • Enhance clinical trial assessment and enrollment
  • Complement small compound analysis 
  • Improve target discovery
  • Better analyze genomics content
  • Streamline analyses of biomedical images

With Layar, users have experienced a 97% improvement in query accuracy for clinical trial protocol assessment and a 25% increase in productivity when compared to manual literature review.

Vyasa, Oracle and NVIDIA at SC22 

Interested in learning more about Vyasa’s deep learning analytics for healthcare & life science? Visit our demo in the Oracle & NVIDIA booth #2406 at SC22 from November 14-17. 

Access a free trial of Vyasa here

Press Release: Vyasa Adds Real-Time Dashboards to Layar Data Fabric 

New application interface provides advanced visualization for identifying trends and anomalies across your data 

BOSTON, September 27, 2022Vyasa, an innovative provider of highly scalable deep learning A.I. analytics software for healthcare, life sciences and business applications, today introduces its latest application interface, Signal. Featuring an intuitive design, Signal enables users to monitor trends and identify anomalies in their Layar data fabric through highly-visual charts and graphs, delivered in a single dashboard.  

According to industry analysts, between 80-90% of organizational data created today is unstructured. While rich in insight, the complexity of this data makes it increasingly difficult to analyze efficiently, requiring many to rely on manual processes to find the information they need. Vyasa leverages proprietary deep learning models to accelerate this process, allowing users to search and identify insights from their data via its Layar intelligent data fabric. Signal is the latest application interface developed by Vyasa to make this data easy to analyze in low code. 

Users of Signal are able to:  

  • Visualize term usage and frequency in your data via line, bar and pie charts. 
  • Monitor terms and concepts of interest across multiple cloud and on-premise data sources without having to move or replicate files. 
  • Discover common concepts that relate to your terms of interest. 
  • Identify emerging terms and trends with named entity recognition. 
  • Receive real-time alerts to events occurring in your data. 
  • Filter charts and graphs to receive dynamic results for comparisons and reporting. 
  • Query across multiple data sources and file types. 

“Today’s organizations face significant data accessibility challenges – from content being stored across multiple silos to managing various structured and unstructured file formats to outdated processes for collecting and analyzing data that are time and resource intensive,” said Vyasa Founder & CEO, Dr. Christopher Bouton. “At Vyasa we’re developing cutting-edge deep learning technologies to address this issue and Signal is the latest example of that. Now users can quickly access the insights they need in charts and graphs which are familiar and easy to analyze without having to worry about finding the data or having the technical acumen to develop a code or query to collect the information they’re seeking.” 

Signal is being used across a variety of industries to help users to monitor trends, identify needs in the market, discover emerging technologies, streamline competitor intelligence and more. It joins Vyasa’s suite of deep learning applications powered by its Layar data fabric, including Axon dynamic knowledge graph capability, Cortex data fabric creation and management, Synapse “smart table” technology, and Retina image management and analysis. Vyasa can be deployed on-premise or in the cloud as a fully containerized application. 

Access a free trial of Vyasa’s suite of deep learning applications at  
For more information, please visit or contact [email protected]   

About Vyasa: 

Founded in 2016, Vyasa Analytics provides highly-scalable deep learning software and analytics for healthcare, life sciences and business applications. Vyasa’s technologies enable organizations to integrate and access data across disparate silos, regardless of location or structure. With Vyasa’s collection of deep learning applications, users can ask complex questions across large-scale data sets and gain critical insights to make faster, more accurate, business decisions. For more information, please visit  

Accelerating Clinical Trial Enrollment – Vyasa at HETT 2022

According to industry research, 85% of clinical trials end with less than expected enrollment, while 19% are terminated or fail to meet accrual goals at all. Considering the millions of dollars and significant time invested into these trials, the ability to identify and enroll viable candidates is key to successful trial completion and driving innovation around cutting-edge therapeutics. 

Clinical Trials’ Data Problem

One of the major challenges associated with clinical trial enrollment is the vast amounts of data required to identify relevant candidates. This data is stored across multiple silos and formats making it difficult to quickly access and analyze. In fact, over half of eligibility criteria for clinical trials is unstructured, and that’s not considering the other critical data points that help influence candidate identification such as patient records, clinician notes, genomics files, images and more. 

Traditionally, trial sponsors have relied on manual processes to identify appropriate candidates – searching through massive databases and multiple resources before gleaning out potentially relevant participants. These processes are time and labor-intensive; in many cases, only a fraction of potential leads are found. 

Tackling Your Data with Deep Learning

Deep learning is revolutionizing how the healthcare and life sciences industries can search and access data. Known as transformer-based deep learning, these advanced models use a novel approach called self-attention to train on large sets of data. Unlike traditional deep learning models that process each term separately and without context, self-attention allows transformers to build rich representations of the data 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 the context within unstructured data like large documents of natural language text.

At Vyasa, we leverage this novel approach to deep learning as part of our Layar intelligent data fabric. By applying deep learning to disparate data sources, Layar is able to make the contents of those sources searchable and accessible in a single platform, or data fabric. We then take this a step further by providing a suite of low code application interfaces to help users simultaneously explore and gain access to key insights in their data regardless of its format or location it’s stored in. 

Successful Candidate Identification with Vyasa  

While data fabrics provide benefits across healthcare and life science, they hold significant power when applied to research tasks including clinical trial design and candidate recruitment. 

For example, healthcare organizations are turning to Vyasa to enhance their understanding of their patient population and improve clinical trial enrollment as a whole. By connecting their data silos into a single data fabric they can have all of the relevant information they need in a single platform without having to collect, move or replicate data. The addition of deep learning on top of this data enables them to uncover new insights with peak accuracy and speed. As a result, these organizations can leverage Vyasa Layar to create a unified data platform to search and identify relationships between clinical trials and their patient population to improve trial enrollment and viability. 

Working with Dell Technologies, we can provide this solution on innovative compute and storage hardware, allowing organizations to access and operate these capabilities in a familiar server environment.

Smarter Trial Analysis – HETT 2022

Vyasa will be presenting our Layar intelligent data fabric and clinical trial analysis capabilities at Dell Technologies stand #E30 from September 27-28 at HETT 2022. 

Interested in meeting? Contact us here

Access a 7-day free trial of Vyasa Layar here

Healthcare Fireside Chats Episode 2 – Operationalizing Your Data with Deep Learning

The industry estimates that nearly 80% of organizational data is unstructured.

While rich in insight, unstructured data is incredibly challenging to search and analyze due to the complexity of content such as scientific research, scanned documents, clinician notes, pathology reports and more.

Fortunately, advancements in deep learning are creating a paradigm shift in how we can approach this data through transformer-based models.

Unlike a traditional deep learning model that processes each term separately and outside the context of the sequence, transformers 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.

In the next installation of our Healthcare Fireside Chats, we’re joined by Dell Technologies‘ Global & Federal Healthcare CTO, Michael Giannopoulos. We’ll discuss the rise of unstructured content in healthcare and how Vyasa is applying transformers to improve how the industry can access and gain insight from this valuable data.

Watch more below:

Healthcare Fireside Chats – Episode 1

On average, hospitals create up to 50 petabytes of data on an annual basis. Unfortunately, as much as 97% of this information goes unused.

In our new series with Dell Technologies, we’ll unpack the biggest data challenges facing the healthcare industry – from data silos to research to understanding patient need to magnet reporting.

In our first episode, we’re joined by Kevin Crosby, Dell Technologies’ healthcare field director where we discuss the impact of data silos in healthcare and the innovations addressing this issue.

Watch more below.

Video: Overcoming Data Silos – Vyasa & NVIDIA at Dell Technologies World

Industry estimates are that nearly 80% of organizational data is dark and siloed. Vyasa helps organizations overcome this challenge with its intelligent data fabric, Layar. With Layar, organizations can logically connect disparate data silos without moving or replicating files. Unified data can then be queried through a suite of low code interfaces which accelerate data exploration and insight discovery with the power of deep learning.

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Operationalizing Manufacturing Data for Industry 4.0 with Vyasa and NVIDIA

Industry 4.0 is revolutionizing manufacturing – from production on the factory floor to operations in corporate offices. While conversations continue to focus on how robotics and automation tools are presenting new levels of productivity and safety to the industry, the data that manufacturers hold is truly the engine behind making these possibilities a reality. As a result, the market for big data analytics in manufacturing is projected to reach 4.55B by 2026 as the sector continues to adopt “smart industry” initiatives.

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