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How Much Insights You Can Uncover from Unstructured Data

Text documents, photos, audio and video recordings, and social media posts are all examples of unstructured data.
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If you have any data that is not categorized or easily searchable in the database, then you might be struggling with unstructured data. Text documents, photos, audio and video recordings, and social media posts are all examples of unstructured data.

This unstructured data can be analyzed and understood using AI methods like machine learning (ML) and natural language processing (NLP). NLP algorithms help to analyze and comprehend human language, making it feasible to derive insights and meaning from textual data. Large data sets, like photographs and audio recordings, can be analyzed using ML algorithms to find patterns.

With today’s Big Data, it has become important to understand that:

Unstructured data need to be analyzed

Compared to structured data, unstructured data can be significantly more cumbersome and challenging to work with. This is because unstructured data is more difficult to analyze and extract insights from because it is sometimes substantially greater in quantity and not arranged in a set way.

The comparison between credit card transactions and human genome data serves to highlight the vastness and complexity of the two sets of information. A single sample of the data produced by a sequencer for the human genome can be approximately 200 GB in size, yet a single credit card transaction might only produce a few bytes of data. This is because the information included in the human genome is highly complicated and contains a tremendous amount of data that might be challenging to read and analyze.

Adopting AI is crucial

AI is being utilized more and more to evaluate unstructured data to find new opportunities and address business challenges. To properly employ AI with unstructured data, there are still issues that need to be resolved.

The requirement for flexibility across different system architectures, storage and analytical services, programming paradigms, and user experience is one difficulty. Unstructured data frequently calls for a different strategy than structured data, which may call for modifications to current technology and procedures.

Building effective data management and processing strategies should be considered

Larger and more complicated than structured data, unstructured data may necessitate specific methods for data storage, retrieval, and analysis. For instance, some firms are using cloud-based analytics and storage services to manage and process enormous amounts of unstructured data.

Focusing on effective user experience is necessary

To make AI-powered technologies more usable and intuitive, effective user experience and design are required. This could entail creating user-friendly interfaces that make it easier for users to interact with and evaluate unstructured data.

About the author

Aiswarya MR

With an experience in the field of writing for over 5 years, I find my passion in writing for various topics including technology, business, creativity, and leadership. I have contributed content to hospitality websites and magazines. Currently looking forward to improving my horizon in technical and creative writing.