Unstructured data analytics or unstructured data analysis refers to the process of extracting insights and knowledge from large amounts of unstructured data, which is data that does not conform to a traditional structured model, such as relational databases (RDBMS). It includes text documents, images, audio and video files, emails, sensor data and other forms of data that do not have a pre-defined format.

Why is unstructured data analysis important?

Unstructured data analytics involves several techniques and technologies to process and analyze the data, such as natural language processing (NLP), machine learning, text mining, image and video analysis, and data visualization. The goal of unstructured data analytics is to discover insights that can inform decisions, improve business processes, and drive innovation.

The importance of unstructured data analytics is growing in many data-heavy industries, including healthcare, finance, retail and government and across many functions, including marketing, engineering, research and development. The right approach to unstructured data analysis can deliver a competitive advantage, help you understand customer behavior, suggest operational improvements and influence R&D initiatives. The challenge of unstructured data analytics is to manage and process large volumes of data in a scalable and efficient manner, and to extract meaningful insights from the data. Data Lakes, Data Lakehouses, and cloud data storage are typically part of an unstructured data analytics IT infrastructure.

According to the annual Komprise State of Unstructured Data Management survey, 65% of IT organizations are delivering unstructured data to big data analytics programs.

Komprise Smart Data Workflows is an automated process for all the steps required to find the right data across your storage assets, tag and enrich the data, and send it to external tools such as a data lakehouse for analysis. Komprise makes it easier and more streamlined to find and prepare the right data for analytics projects.