Unstructured data is unfiltered information to which a fixed editing policy is not applied. It is often referred to as raw data. Common examples are internet logs, XML, JSON, text documents, images, videos and audio files. Unstructured data is searched and analyzed to extract useful facts. Up to 80% of enterprise data is unstructured. This means that it is the type of big data that is most visible to many people. The size of unstructured data requires scalable analytics to generate insights. Unstructured data is present in most, but not all, data lakes due to low storage costs.
Attention mechanism is a technique that revolutionizes the world of artificial intelligence and deep learning in areas such as language processing, image recognition and even sound analysis.
Feature Engineering is one of the most labor-intensive and creative phases of the machine learning process. This process involves the transformation of raw data into more meaningful and processable properties. The basic principles of Feature Engineering include using domain knowledge, data discovery, understanding the nature of data, and problem-oriented thinking.
Data-Driven Innovation (DDI) refers to the realization of product, service or business model innovation using data analytics and digital technologies. This approach is a critical tool for companies to make better decisions, improve customer experience, and gain competitive advantage.
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