In today’s digital world, the ability to collect, analyse and process data is one of the most principal factors for any organization, due to the vital role that data plays in day-to-day company decisions. With that, the amount of data generated keeps increasing, and with it the challenges of processing such amounts of data.
What is unstructured data?
Simply put, “unstructured data” is data that is stored in its native format, without any type of structure. It’s therefore more difficult to process, extract and analyse its information. While there might be some kind of internal structure within it, it will be considered unstructured if it doesn’t lend itself to the kind of formatting required by a database. A few examples would be text-heavy data (like business documents, spreadsheets, and emails), video, audio, or images. It’s by far the amplest type of data, and it keeps growing as time passes.
Is unstructured data important?
Because unstructured data encompasses most of the data that organizations hold, it is one of the most important pieces of data available… of curse, after a proper processing. Unstructured data can hold a lot of types of information and can be quite useful to the company internal processes and workflows. For example, to analyse customer experience and sentiment and to optimize your marketing intelligence processes.
How to analyse, process and extract information from unstructured data automatically through AI?
AI tools make it possible to analyse all unstructured data in a fast and automatic way. These solutions are made possible using machine learning, enabling speech-to-text and pattern recognition to automatically structure it. Through these methods, AI is capable of extracting keywords, phone numbers, names, and more. All of which is data that is enormously valuable to any organization. By being able to process substantial amounts of data in mere seconds, organizations can manage to become more optimized, thus saving time and resources.
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