More data is being put out into the world every year. The volume of data created, captured, copied, and consumed worldwide is predicted to reach 182 zettabytes this year and will increase rapidly to 394 zettabytes in 2028.
The collection and storage of this data in the modern world cannot be underestimated. As we noted in a previous post, data storage has become a key component of IT infrastructure in the current digital era. As the amount of data has increased, so too has the type of data that can be collected and the different ways the data can be used within a database. Conducting a vector search through a vector database has become an increasingly popular method of storing and using different data types that are now available to businesses. As the amount of data increases in 2025, vector searches are becoming more and more critical.
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Vectors and Vector Databases
In order to understand the full potential of vector searches and why they are so critical, we need to know what a vector is and how a vector database stores it. Vector databases store, retrieve, and search for data represented by vectors. A vector is a sequence of numbers that can be broken down into components, and these vectors are stored as points in a multidimensional space. Vector databases can store unstructured data, such as documents, emails, social media posts, images, and videos, and it converts the data to a vector through an embedding model. Once the data has become a vector, it is then stored in the vector database with vectors that are similar to each other, either contextually or semantically, clustering together. It is these clusters that allow the vector database to perform a vector search.
A Vector Search
A vector search operates based on similarity rather than looking for exact matches. This semantic understanding means that even if two pieces of data aren’t identical but have aspects that are similar, then they can be matched. For example, if you have a video collection of different animals in a vector database, with each video represented by a different vector, all the domestic animal vectors would be clustered together, as would all the wild animal vectors.
Within these clusters, the vectors of the cat family – domestic cats, lions, and tigers – would also be clustered close together. If you were just to search for video files on cats, the vector database would return everything from the cat family, as well as other vectors that are closely associated with cats, such as dogs and mice. A vector search can be narrowed down or widened using metadata filtering to apply extra constraints associated with each vector.
Why is a Vector Search Critical in 2025
Vector searches are becoming more critical as more companies and users look to harness the vast volumes of data available and use it to provide a service. Vector searches are used for recommendation systems, natural language processing (NPL), and question-answering (QA) systems, all of which are becoming increasingly prevalent in 2025. Below, we will look at three ways in which vector searches are becoming critical.
Product Recommendation
One of the widely used applications of the vector search is the product recommendation. Ecommerce platforms and streaming services use vector searches to push products and content that they believe their customers will want or be interested in. This is done by conducting a vector search of previous habits and purchases and presenting the results to the customer. This allows for greater personalization, which is one of the key customer service trends for online services in 2025.
Generative AI Integration
Generative AI will be much more widely integrated globally by 2025. NPL applications like chatbots and QA systems like virtual assistants are examples of generative AI that will use vector searches to find relevant contextual data from a database and provide the most accurate results. This is because vector searches let the generative AI receive new information outside of the dataset they were trained on, allowing them to remain up-to-date. With the generative AI market projected to reach $62.72 billion this year, vector searches will become a key function that helps the applications be more effective.
Cybersecurity
Last year, we reported that cybercrimes had increased by 72%, which will increase in 2025. Cybersecurity experts use vector searches to find anomalies by comparing vectors. Vector searches are able to process large volumes of data, which means they can quickly find data points and patterns that deviate from the norm. In finance, a vector search could be used to find unusual transactions that indicate fraud. Vector searches are also increasingly being used in biometric security as they can perform quick similarity searches between new biometric vectors and stored ones, which makes real-time identification possible. As more applications develop to be fully digital in 2025, vector searches will be used to protect institutions and provide extra security.
As technology evolves in 2025 and becomes more integrated into society and our personal lives, vector searches will become more critical across all industries.
Also Read: User Experience, The Future Of Search Engine Marketing