Why Vector Databases Matter for Unstructured Data

Learn What, How and Why of Vector Databases

As the amount of data exponentially grows in the information age, unstructured data is exploding. Images, videos, texts, medical data, and housing data are different kinds of unstructured data experiencing massive growth today. In addition, smartphones, IoT devices, and social media contribute to the rapid rise of unstructured data. IDC predicts that 80% of data will be unstructured by 2025, and per IDC's Global DataSphere 2022, unstructured data growth from 2021 to 2022 is forecast to grow more than 9x the volume of structured data. Machine learning techniques can transform unstructured data into feature vectors. This technique makes it possible to analyze and manage the unstructured data so many current technologies produce and rely on.

These vectors are often huge and can vary from tens to hundreds of dimensions. A vector database needs to be able to handle vectors of that size and to be flexible. In addition, the amount of vector data in the world is growing as the amount of unstructured data grows, and the need for a scalable, dynamic vector database is high. This paper will explain unstructured data, popular use cases for unstructured data, and how Milvus differs from other vector data management systems.