Forget the Format: Deep Learning for Unstructured Content
The way organizations think about data management has changed significantly in recent years. Historically, structured data sources that could be easily saved in spreadsheets were the only assets prioritized. They are easy to search and analyze, thanks to consistent formatting of rows and columns of data consisting of patient health records, financial transactions, customer information, inventory, etc.
But what about the millions of other documents that companies create in a given year?
The majority of documentation created is in the form of unstructured content. In fact, as much as 90% of data generated daily fits this category and this number is only expected to grow. Industry research forecasts unstructured enterprise data to grow at a rate of 55% and 65% per year.
Unless you’re a technical professional in IT, data science or software development, it’s likely you spend the majority of your time interacting with unstructured content that holds critical information for your business. Think emails, images, company memos, sales & marketing strategies, clinician notes, scientific research, market analysis and more. Yet, given the inconsistent structure of this content which makes it hard to easily search, it’s largely been deprioritized, saved deep within data silos.
Some of our most valuable data is often the hardest to identify.
Deep Learning – The Key to Unlocking Unstructured Content
Traditionally, analyzing unstructured content has fallen on research teams — oftentimes tasked with sifting through hundreds of documents that may be equally as long. It’s no surprise that this task could take weeks or months to complete before even starting any analysis. And that’s without factoring in the level of accuracy from manual research.
Advancements in deep learning text analytics are making the challenge around unstructured content an issue of the past. Thanks to advanced algorithms and powerful computing infrastructure, organizations can quickly search and access documents to extract key insights that can fuel business intelligence and strategic decisions.
One of these advanced techniques is Bidirectional Encoder Representations from Transformers, also known as BERT-modeling. BERT-modeled deep learning algorithms are developed for language modeling and next sentence prediction. This combination means BERT models can learn context around groups of words. By better understanding concepts and phrases, BERT models can enable stronger, more accurate search results.
A key capability that is also improving the analysis of unstructured content is named entity recognition (NER). NER leverages deep learning algorithms to identify and categorize terms and phrases from unstructured and semi-structured content. The algorithms can identify related concepts that may not be the exact keyword but are equally as relevant. For example, identifying different names and abbreviations for the same product, varying symptom descriptions for an emerging disease or the individual businesses within a conglomerate.
Deep Learning Insight Extraction in the Real World
Beyond making the lives of researchers and analysts easier, leveraging deep learning for document insight extraction is proving to have a real-world impact for a variety of use cases. At Vyasa, we’re seeing our applications used for:
- Clinical Trial Design: Designing a clinical trial is no simple task. Clinicians can spend months researching protocols around patient selection criteria, investigating drug compounds and past studies all of which can be hundreds of pages long. By leveraging deep learning text analytics, clinicians can ask questions of this content to quickly identify the data that is most relevant to them.
- Emergent Disease Research: A key challenge behind studying emerging diseases is the wide variety of terminology used to describe the same condition. Often there are numerous names for the same disease or disease subtypes, similar symptoms and descriptions that can be difficult to search through without considerable manual effort and time. NER can scan through these documents to identify results with semantically similar names for the same disease. For example, COVID-19, SARS-CoV-2 and coronavirus.
- Competitor Analysis: Staying on top of the competition typically requires tedious tasks like web searches, social media monitoring or sifting through regulatory filings. With access to publicly available data sets like the U.S. Patent Office, deep learning can catalog and collect the most pertinent information released by current and emerging competitors.
Vyasa’s novel approach to deep learning text analytics is revolutionizing how companies collect, access and manage intelligence. Our powerful deep learning analytics can then be applied to content to extract insights from documents such as PDFs, presentation decks, published research and images that are rich in data, but hard to analyze via traditional methods. Insights are made available via highly visual applications such as dynamic knowledge graphs and smart tables that simplify tasks critical to making informed business decisions.
Managing and accessing unstructured content continues to be a key painpoint for many organizational data strategies. Now it’s up to deep learning unlock it.
Interested in seeing the power of Vyasa in action? Access a 7-day free trial here.