Unstructured Data refers to various types of text data, audio files, images, videos, and any other data type that can't be stored in traditional structured databases. Unstructured data is qualitative and not structured through predefined models, remaining in its native form until it is analyzed.
Aside from images, audio files, and video files, much of the valuable unstructured data come from social media conversations or open-ended survey responses. These are quite text-heavy and require the use of advanced analytics such as data mining to extract any understanding. This type of data provides further insight into customer preferences and company perceptions. Structured and unstructured data can both be housed in the cloud, but unstructured data is stored in its original format within vast data lakes and therefore takes up a lot more space than a structured database.
Machine learning technology can enable automatic data management and analysis of unstructured data, delivering instant, accurate insights that lead to informed, data-driven business decisions. Utilizing technological advancements, such as artificial intelligence (AI) and natural language processing (NLP), machines are empowered to read text as humans do, which eliminates tasks like routing tickets, manually tagging, or poring over countless social media posts.
AI technology automatically extracts keywords, recognizes opinions and intent, and identifies subjects that are central to a specific business. With unstructured data analytics tools, supplied with machine learning and NLP proficiencies, a business can skip the time-consuming practice of manually analyzing unstructured data and automate the search for valuable insights through customer emails or service tickets. Managing unstructured data involves tools that can help a business improve the customer experience and discover gaps in their business or the market. These automated tools are more efficient, accurate, and scalable than human analysts.