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.
Data matching is the process of linking a data field from one source to a data field from another source.
The data lake is where long-term data containers gather that capture, clean, and explore any raw data format at scale. Data subsets are powered by low-cost technologies that many downstream possibilities can benefit from, including data warehouses, and recommendation engines.
Data anonymization techniques are the modification of data in systems in such a way as to prevent the data from pointing to a specific individual while maintaining the format and consistency of the data.
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