By super.AI
2.5 quintillion bytes of data are created each day, and 90% of the world’s data was created in the last two years. That number is forecast to double every two years for the foreseeable future. But for every 10 pieces of new data, nine are inaccessible due to unstructured data.
Unstructured data is data that lacks any kind of predefined data model or schema. This lack of structure prevents it from being stored in a traditional database. Examples of unstructured data include:
If in doubt whether data is unstructured, check if it has any organized attributes. For example, semi-structured data has some organizational aspects like tags and metadata. This makes it easier to categorize semi-structured data according to a hierarchy. If you’ve ever downloaded a JSON file or viewed videos on your smartphone, you’ve managed semi-structured data.
By contrast, structured data has a strict format that allows it to be easily organized. Let’s take the example of an online purchase. Each purchase has several types of unique data associated with it, from each item purchased to the order’s confirmation number. If you’ve ever wondered how big tech companies serve so many ads tailored to your interests, structured data is a big reason why. It’s easier for algorithms to analyze all of the data associated with purchases because each type of data has a pre-defined purpose.
But between 80 to 90% of data produced today is unstructured. That means you’ll need unstructured data processing software to use the vast majority of available data. This post explores the features, use cases, and things to look for in unstructured data processing software.
Unstructured data processing software performs four main tasks that help you gain value from unstructured data. It:
Unstructured data processing and analysis software uses a range of AI techniques to accomplish the tasks above, from natural language processing (NLP) to machine learning (ML). This software isn’t industry-specific: It helps users in industries ranging from retail to agriculture.
Regardless of sector, UDP software processes unstructured data to build reusable, automated, data processing workflows. It achieves this using some key features that let you integrate, select, extract, and improve unstructured data at scale:
Unstructured data processing software turns data into process-ready insights, no matter how unstructured it is. If you’re curious how it works, learn more using the links below:
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