Data has been called the new oil of the modern world, as it has become the foundation of many enterprises. As the world transforms into a fully digital society, the amount of data that is being collected is increasing every year.
With more and more companies shifting their priorities to AI, there is a demand for databases that can store this unstructured data so that it can be used in modern applications, such as large learning models. One such database that has proved to be a game changer in the data management industry is the vector database.
This new type of database is being swiftly adopted across many industries because of its versatility and ability to perform different types of searches. A reflection of this popularity is how the vector database market size is rapidly increasing and is expected to grow at a CAGR of 23.7% from 2024 to 2030. In this article, we will examine why vector databases are transforming the data management sector and becoming game changers across industries.
Vector Databases Explained
A vector database differs from relational and NoSQL databases due to how it stores data. Instead of storing data in tables or documents, vector databases store data on vectors, a string of numbers stored in a multidimensional space. Once in the vector database, the vectors with similar attributes or characteristics naturally gravitate toward each other, forming clusters. Vectors are able to do this because they are not just numerical translations, as they encapsulate the deeper semantic essence and the contextual nuances of the original data. This makes them ideal for training AI applications. Mark Hinkle in The New Stack described a vector database as such: “Imagine a vector database as a vast warehouse and the artificial intelligence as the skilled warehouse manager. In this warehouse, every item (data) is stored in a box (vector), organized neatly on shelves in multidimensional space.” Below are three reasons why vector databases are game changers.
Semantic Searches
Compared to traditional databases that find exact matches, the vector database allows you to search for data based on its ‘closeness’ or similarity to your query. This is known as a semantic search.
This search type is becoming increasingly popular because it does not just match keywords but also understands the meaning and context of a user’s query to retrieve more relevant and accurate results. For example, if looking for a color in a massive paint set, a semantic search would find all results related to the color you are looking for because they would be grouped together rather than going through the entire paint set to find the exact color. The ability is a game changer as the vector database can comprehend complex queries and provide accurate results in massive datasets of billions of data points. Semantic searches can be performed across any modality, such as images, video, audio, and social media posts.
Optimized for AI Applications
Vector databases are playing an important role in the training of AI applications. Large language models (LLMs), such as chatbots and content creation applications, use vector databases to provide more context to their dataset by acting as an external memory. Once an LLM is trained, its knowledge is frozen, and to update, it would need to be completely retrained. Vector databases give an LLM state because they can be used to update the LLM with new information and act as external knowledge. The ability to handle massive volumes of data means that they can easily keep up with the data requirements of LLMs. This allows chatbots to be instantly updated with the latest information when communicating with customers.
Wide Range of Use Cases
Outside of training LLMs, vector databases are a game changer in various industries. In healthcare, AI image recognition is playing an increasingly important role as more doctors use AI image analysis to interpret medical images such as X-rays, MRIs, and CT scans.
Vector databases are used in image recognition systems because the semantic search is able to go through large datasets of images for early disease detection. As the world moves to a fintech ecosystem, vector databases are being used for enhanced data analysis, as they can instantly evaluate large datasets of customer profiles, transaction histories, and market data to detect patterns and trends. As more consumers use fintech applications for risk assessment, vector databases are able to provide personalized recommendations based on their previous behavior and the latest financial data.
Vector databases are fast becoming one of the most important data management systems in our digital society. The ability to conduct semantic searches, train AI models, and be used across industries has made them game changes in data management.
