Tag: Data Exploration

SMARTER RESEARCH: AI-POWERED SYSTEMATIC LITERATURE REVIEW

Written by vyasa on . Posted in USE CASES.

More than 90% of clinical-trial compounds fail to demonstrate sufficient efficacy and safety. To mitigate this issue, many look to third-parties to conduct extensive research via systematic literature review (SLR) to gather the relevant information needed to validate the investment and approach to a trial. While providing valuable insight, each SLR can cost over $140,000.

Problem

SLRs are largely conducted via manual processes – with researchers needing to compile, review and analyze large sets of unstructured content. As a result, these processes are incredibly time consuming due to the amount of data and grey literature that needs to be consumed and reported on to make SLRs successful. Due to the nature of this work, insights can be missed and timelines can be delayed which hold up productivity for the end customer.

Solution

Advancements in deep learning text analytics are enhancing systematic literature reviews by improving the accessibility and searchability of unstructured content. Vyasa has developed high-performing deep learning models that understand context and can identify key terms that improve the accuracy and time spent on systematic literature reviews. We then take this a step further with our Layar data fabric, a novel data architecture that unifies content sources into a single platform. With the Vyasa platform, users can:

  • Integrate siloed data sets into a single, searchable platform.
  • Search unstructured content in natural language.
  • Explore their data via highly-visual knowledge graphs, dashboards and smart spreadsheets.
  • Export unstructured content into structured formats.

By leveraging Vyasa, users can improve query accuracy by 97% while reducing research times by as much as 90%, leading to smarter, more efficient systematic literature reviews.

ACCELERATING LEGAL RESEARCH & ANALYSIS

Written by vyasa on . Posted in USE CASES.

Law firm associates spend roughly 35% of their time, or 15 hours per-week, conducting research tasks.1 These tasks are largely conducted online across multiple sources and leveraging various research tools.

Problem

Effective research is a critical step of any legal team’s activity – from analyzing cases to identifying laws to determining legal precedent. Collecting these insights requires sifting through large sets of unstructured content, including published reports, legal filings, written case notes and more. Unfortunately, the industry has largely relied on manual processes for conducting this research which is time and labor intensive. As a result, insights are often missed and legal professionals assigned to these tasks often become disengaged or burnout.

Solution

Advancements in data management and deep learning are addressing this issue head on. A new data architecture known as the data fabric, enables legal professionals to catalog all of their research data sources in one place, regardless of file format or storage location. The data fabric then acts as an engine for deep learning models to perform text analytics making content easily searchable.

With Vyasa’s Layar data fabric and novel deep learning applications, these capabilities are combined into a single platform enabling legal professionals to:

  • Make data sources accessible across departments. (No need to duplicate your data or storage and no data lake required.)
  • Easily research large sets of documents via natural language question answering.
  • Explore search outcomes via highly-visual applications including knowledge graphs, tables and dashboards.
  • Improve the discovery of novel insights hidden within unstructured content such as reports and case documents.

With Vyasa, legal professionals can improve query accuracy by 97% while decreasing analysis time by 90% leading to more efficient research and smarter legal insights.

1 Lastres, S. Rebooting Legal Research in a Digital Age https://www.lexisnexis.com/documents/pdf/20130806061418_large.pdf

SMARTER CALL CENTERS – IMPROVING REP RESPONSE

Written by vyasa on . Posted in USE CASES.

Call center representatives are under immense pressure. They must process large amounts of information and quickly answer questions all while managing caller expectations. In most cases, they’re armed with outdated technology and complicated software which doesn’t make their work easier. No wonder call centers have such a high turnover rate.

Problem

To effectively respond to caller requests, representatives typically rely on searching lengthy documents that are in a variety of formats and saved across various locations. Finding the right insights they need is time consuming and in most cases they’re only collecting a small portion of the information available to them. This leads to delays, missed or incorrect information and ultimately, a poor customer experience.

Solution

Advancements in data management and deep learning can improve the way call center representatives gather information and serve customers. Through a new data architecture known as the data fabric, Vyasa can unify all data sources available to a call center into a single platform. Deep learning models built by Vyasa can then be applied to the unified data making it easy to search and access in a matter of seconds.

With Vyasa’s Layar data fabric and deep learning applications, call centers can:

  • Make data sources accessible across departments. (No need to duplicate your data or storage and no data lake required.)
  • Quickly search for and access documents in a single location.
  • Query large sets of documents via smart spreadsheets and dynamic knowledge graphs.
  • Discover related concepts and insights with named entity recognition.
  • Extract information into easily shareable file types.

With Vyasa, call center representatives can improve research accuracy by 97% while decreasing analysis time by 90% leading to enhanced call center response and a more positive customer experience.

Unifying Data. Accelerating Insights.

Applications

Layar

Cortex

Synapse

Axon

Signal