Research firm IDC reported that 64.2 ZB of data was created in 2020, with forecasts expecting this number to grow in the years to come. The value this data brings to the world is well-documented, but of lesser focus is the wasted time and intellectual resources spent by organizations to make this data usable in the first place. 

The reality is, data scientists and researchers are spending too much time finding the data they need when they should be spending time doing what they do best: the actual data science or analysis. This process can take weeks or months and in most cases, critical information will still be missing due to data silos and redundant, obsolete or trivial content, AKA data ROT that buries relevant information. This hidden “dark” data is often in the form of unstructured content. Think PDFs, PowerPoint decks, published research, RSS feeds, etc. which all hold valuable information and context, but are hard to analyze, catalog and manage through traditional practices.

Unfortunately, this challenge isn’t going away, especially as data storage becomes more accessible via the cloud and the creation of data continues to skyrocket thanks to the proliferation of the internet of things and our increasingly digital worlds.

So are we doomed for a world where rising data volumes continue to plague companies with missed insights, delayed project timelines and wasted resources? 

Fortunately not.  

Next Steps in Analytics & Data Management

Advancements in deep learning and data management are making dark data an issue of the past. Deep learning algorithms are being trained on large data sets to identify patterns and context allowing users to “see” into their data. These algorithms can automatically pull insights from data, turning a task that could normally take hours into seconds.  

But this doesn’t solve the issue of where the data is stored.

New data management architectures are changing the way we think about storing and accessing data. Known as a data fabric, this architecture creates an index of metadata without creating copies or moving data from its source. These indexes are weaved together like a fabric, blanketing your data and making it accessible regardless of where it resides or what format it’s in. All without requiring a data lake.  

The Vyasa Layar Deep Learning Data Fabric

The Deep Learning Data Fabric

Vyasa’s Layar is a next-generation, deep-learning data fabric. Through connectors to various data sources, Layar pulls together data from throughout your organization and from external sources into a consolidated view. Deep learning algorithms built within Layar create an index of metadata from unstructured files making the data infinitely more accessible while enabling the ability to quickly search data sets via simple natural language queries. Over time, Layar fine-tunes its understanding of the data adding more context to improve outcome accuracy and enhanced insights. 

With access to unified data via Vyasa Layar, users can:

  • Uncover novel relationships within data sets
  • Answer questions about the integrated data 
  • Visualize data, making it accessible across skillsets

Layar removes the requirement of setting up a framework, thus lowering the barrier to accessing your data. Simply ask a question, and Layar will give you the answer without you needing to tell the machine where to look or how to find the answer. It is like an “easy button” that lets you derive insights from your data.

This accessibility opens the door for users of all skillsets to access Vyasa’s suite of deep learning applications to pull even further value from data:

  • Axon: A visual approach to data exploration using natural language queries to find answers from structured and unstructured data sources via a dynamic knowledge graph.
  • Synapse: “Smart Table Technology” that leverages natural language queries to populate spreadsheets with answers from structured and unstructured data sources.
  • Retina: A scalable image analytics and model management platform that offers a wide range of deep learning tasks to streamline your imaging workflow.
  • Trace: Visual data patterns with geospatial analysis add deeper context to data with the addition of timing and location information.

With increased data accessibility through an intuitive platform, intelligence is enhanced throughout the organization – leading to faster project timelines, more accurate decision making and greater value to the business.

See how Vyasa is fueling faster insights in the life sciences by clicking here