Examining Vector Databases’ Effect on Modern Business Development

In today’s business landscape, data has become one of the most important commodities. As society continues to become increasingly digital, businesses are finding new ways to collect consumer data and use it to promote and improve their operations.

As a result, this has led to a demand for databases that can store the increasing amount of data. One such database is the vector database, which has experienced robust growth in recent years. EIN Presswire reported how the vector database market expanded from $1.98 billion in 2023 to $2.46 billion in 2024 at a compound annual growth rate (CAGR) of 24.3%. This rapid growth is expected to continue, with the market valued at $5.76 billion in 2028.

The reason for this growth is the increasing integration of vector databases into industries as they move to adapt to modern business developments. It is crucial to know how vector databases affect modern business growth as they depend increasingly on data.

 

The Vector Database

Vector databases now play a big role in modern business development due to the unique way they store and sort information. Unlike traditional SQL databases, which store data in tables and columns, and NoSQL databases, which store data on flexible schema models, vector databases store information on vectors in a multi-dimensional space. MongoDB explains how this allows users to find results by grouping vectors together if they are contextually or semantically similar. This is known as a vector search. 

A vector is a piece of data represented as a string of numbers that can be broken down into different components. For example, if a business document is converted into a vector using an embedding model, then each part of that document, such as the title, the subject, and even individual words, will all be represented within that string of numbers. This allows users to find documents that are similar in content but do not necessarily have the exact same features. A key advantage of the vector database is that it can store and sort unstructured data, including video, audio, image, and document files.

 

Key Contributions of Vector Databases to Business Development

The way vector databases can store data allows them to contribute to modern businesses across all industries.

 

Data Management and Storage

As we outlined in our feature ‘Examining Data Science’s Effect on Modern Business Development’, data-driven decision-making has become the heart of successful business strategies. Modern businesses collect lots of data to improve their services and remain competitive. A key advantage of the vector database is the ability to hold and manage vast amounts of data, even up to a billion data points, and scale as more data is collected. 

This is because they can scale horizontally by adding more nodes to the system, which allows a vector database to expand its storage capability easily. Tradition databases are more limited in that they often need more resources to be externally added in order to scale. This makes vector databases ideal for businesses that harvest vast amounts of data and use modern applications that require large datasets to function.

 

Improved Customer Services

One of the key use cases of a vector database is the ability to provide recommendations based on search queries. When a customer uses a platform with a recommendation system, the platform will store data points such as browsing and purchasing history to automatically recommend products that are similar to previous queries. This increases customer engagement, which in turn leads to more conversions.

An example of this was provided by Reshape Digital, which uses vector databases to find similar hotels for potential guests based on various specific fields. By embedding the features of different hotels as vectors, they were able to find the closest hotel matches to any given query, which “improved our ability to provide accurate recommendations and enhanced the overall user experience by ensuring that the hotels suggested were closely aligned with the users’ preferences and search criteria.”

 

LLM Applications

For most modern businesses, AI has become a vital part of their development, with reports showing that up to 77% of businesses are either using or considering AI. Vector databases are fast becoming a key component for AI applications. The ability to conduct similarity searches across large datasets allows these AI applications to respond accurately to queries in large learning models (LLMs) such as chatbots and content generation programs. 

LLM models are limited in that they are stateless, which means they are frozen with the knowledge they have been trained on. Vector databases can act as external memory and can continuously update the LLMS with the latest relevant information because they can easily be expanded with new data. This allows modern businesses to easily integrate LLMs into their public-facing operations without building new LLMs from scratch as the business develops.

Conclusion

As modern businesses become more reliant on data, vector databases will continue to have a big effect on their development. How we can use and store data is rapidly evolving, and vector databases are at the forefront of this change.