Posted on : October 29th 2021
Author : Sundar Rengamani
Good things can come from unstructured data—if you can automate data acquisition, enrichment, and delivery operations in a way that requires minimal manual intervention. Defining and leveraging unstructured data are notoriously difficult. What is the story behind converting them into usable information and insights? The volume of unstructured data is growing by 55 to 65 percent each year. Moreover, unstructured data are projected to account for approximately 80 percent of the data enterprises will process daily by 2025. Unstructured data, coming from diverse sources and in various formats, lack consistent definition for data (Exhibit 1).
Unstructured data provide a layer of insights that fill the gaps in the big picture. Combining unstructured data with structured data improves business decisions, making them better informed and more robust. Unstructured data go mostly unused; industry analysts at International Data Corporation (IDC) note that more than 90 percent of unstructured data are never examined. Large portions of business data float around unsecured and underutilized. We must learn to understand from where unstructured data have been sourced. Why are they so hard to pin down? What are the risks of unsecured, unstructured data? What are the rewards of bringing that data into a structured environment?
Unstructured data cannot be stored in a traditional column-row database or a Microsoft Excel spreadsheet. Until recently, challenges in analyzing and searching unstructured data have made them useless. Straive’s data platform, powered by artificial intelligence (AI), can extract and enrich unstructured data to provide insights. Unstructured data can come from almost any source, nearly every asset or piece of content created or shared by a device in the cloud carries unstructured data, making data loss prevention (DLP) critical.
How are unstructured data converted into usable information and insights? Straive’s text intelligence solution enables enterprises to turn unstructured textual data into actionable insights (Exhibit 2).
The Straive Data Platform (SDP) innovatively tackles these challenges. Using a three-step approach involving AI and machine learning (ML), this sophisticated platform discovers, classifies, and reads unstructured data for downstream consumption. Our solution makes sense of unstructured data, whereas traditional security solutions rely solely on users to help categorize data through conventional methods such as regular expressions (regex). These solutions have limited accuracy in unstructured environments.
Straive advocates for a platform-led approach with AI/ML to transform unstructured data into usable and meaningful insights. AI/ML-led platforms interface with enterprise applications to process massive amounts of unstructured data at scale, leading to smart automation (Exhibit 3).
SDP automates the data acquisition, enrichment, and delivery operations in a way that scales with minimal manual intervention. SDP's autoextraction feature uses both a rules-based and an ML engine to deal with the data variability, sources, and volume while maintaining quality. The platform-led AI/ML approach underpins Straive’s specialized data solutions. We solve complex data intelligence problems in the unstructured data domain for our customers.
¹ EMC Digital Universe with research and analysis by IDC, “The digital universe of opportunities: Rich data and the increasing value of the Internet of Things,” April 2014; International Data Corporation, “IDC iView: Extracting value from chaos,” 2011,
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
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