Posted on : July 9th 2021
Posted by : Sanjeev Kumar Jain
No data can be the same or can be created equal. Data exists in two main formats— structured and unstructured— and although structured data is straightforward and can be used and reused in several ways, it’s unstructured data which is way more than required and common. According to International Data Corporation (IDC), by 2025, 80% of all enterprise data will be unstructured in nature. Of this, text data will be the largest component. This is going to be a major challenge for businesses.
Data formats matter, as they play a key role in the extraction of valuable insights required to power business decisions. If enterprises fail to utilize both the nature and volume of data to improve business growth and profitability, then there is a need to evaluate the data strategy in hand. Understanding the difference between structured and unstructured data is key to any enterprise unstructured data management strategy, post which necessary investment decisions are made to extract relevant insights from the aggregated data.
Unstructured data does not have a definite format and cannot reside logically in a tabular row and column format. Consider unstructured data as "subjective" data, in the sense that it has data that you need now and which you may need later. It can be generated by both machines and humans and typically does not have a pre-defined data model.
Unstructured data sources include annual reports, press releases, scientific publications, blogs, mobile transactions, customer transcripts, title deeds, and more. Other sources of unstructured data include web pages, images (JPEG, GIF, PNG, etc.), videos, word documents and PowerPoint presentations, survey data, and more. It is not only difficult to analyze unstructured data but is also time-consuming and laborious, with manual processes limiting scalability. Although machines can easily process structured data, it is challenging to build automated tools to analyze unstructured data, as doing so may require the use of machine learning (ML) technologies like natural language processing (NLP). There is only a thin line of difference between structured and unstructured data. That is because data that seem unstructured can be processed in a structured way. That is where Text Intelligence solutions can help.
Text intelligence solutions make extracting data from documents for deep analysis, insights, and business applications easy.
Straive's text intelligence solutions, enabled by its proprietary Straive Data Platform (SDP), helps companies to benefit from these opportunities by bringing a suite of advanced solutions to extract, enrich, and deliver data and actionable insights from text-heavy documents in any formats such as text PDF, emails, word files, scanned PDF’s, and more.
SDP enables organizations to support the knowledge discovery process and text intelligence by ingesting raw source documents and providing curated structured data as output by leveraging cognitive technologies layered with SME intervention to deliver highly accurate datasets. The platform and its modules work well when processing unstructured data — which causes challenges for enterprises of all kinds.
Backed by a machine-learning engine, SDP’s extraction module leverages cognitive models to achieve industry-leading accuracy in extracting unstructured data. The extraction module also uses Straive’s next-generation web harvesting algorithms, powered by AI and NLP accelerators, to extract information and monitor websites for raw, standardized data.
Straive’s text intelligence solution helps you get the most out of your unstructured data analytics by taking on time-consuming, tedious tasks and frees you to focus on more important parts of the business.
With text intelligence, you gain a deeper understanding of your customers so that you can improve the customer experience and listen to your customers at every step of the way.
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