Unstructured Data Analysis Techniques
FAQs
Unstructured data analysis is the process of finding useful insights in unstructured big data using various tools and techniques like Natural Language Processing (NLP), machine learning, visualizations, and many more. Unstructured data analysis requires many stages like data preparation, cleaning, processing, analysis, and visualization.
Unstructured data analysis is done using a combination of techniques and tools. Unstructured data should be integrated, cleaned, and prepared to make it suitable for analysis. Then, techniques like Natural Language Processing, machine learning algorithms, statistical and mathematical techniques, visualization, and more can be applied. Some popular tools are MongoDB, Python, R, Tableau, and Power BI.
Yes. In fact, AI-based technologies have made unstructured data analysis much easier. Deep learning and AI techniques like Natural Language Processing, artificial neural networks, image analysis, and text mining are extensively used for unstructured data analytics.
Unstructured data has no particular format. It is usually text-heavy and can contain numbers, dates, and other data. Google search results, healthcare data, emails, social media comments, videos, images, chats, and survey data are all examples of unstructured data.
The first step to structure unstructured data is to clean the data by removing duplicates, outliers, and other non-relevant entries. The next step is to identify the features that will help solve the business problem at hand and organize these features into a format. You can then apply the different data preparation techniques.
For example, if you want to structure lots of text data, categorize and structure the data with techniques like tokenization, stemming, lemmatization, etc.
Similarly, if you have an image in hand, you can structure the data based on features like image size, pixels, face description, color, quality, etc.
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