The Future of Vector Databases

The future of data management is one of the most important topics being discussed across industries. 

Many industries and companies are turning to the vector database, especially as artificial intelligence (AI), particularly generative AI, is being integrated into the operations of many institutions. Vector databases store data in a unique way compared to SQL and NoSQL databases, which makes them ideal for storing and organizing new forms of data, such as video files and social media posts, and handling massive amounts of data for applications like large  learning models (LLMs). Already, many industries recognize the importance of the vector database, and a report from The Business Research Company details how the vector database market is worth $2.46 billion this year, growing to $5.76 billion in 2028. They attribute this growth to the “rise of digital mapping, telecommunication network planning, GPS and satellite technology, environmental and natural resource management, and transportation and logistics planning.” As technology evolves, vector databases will become central to how we store and process data in the future.

What is a Vector Database?

While most databases store data in rows and columns, vector databases store data on vectors. A vector is a list of numerical values (for example, 3,4,12,15) that represent the location of a floating point along several dimensions in the same way a row and column do on a table database. This allows a vector to correspond to multiple data points related to the same object. MongoDB’s guide to vector databases gives this explanation from Mark Hinkle, “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.” Because data is clustered together in a vector database, they can be used for similarity and semantic searches, to support LLMs and generative AI, and to train machine learning and deep learning models. 

Why Vector Databases are the Future 

Unstructured Data

A vector database not only stores data differently but can also store a wider range of data. While a vector can represent any data, the database’s ability to store unstructured data sets it apart. Unstructured data is data that is more free-form, such as multimedia files, images, sound files, or even unstructured text, such as the body of an email or word processor document. Merrill Lynch estimates that more than 85% of all business information exists as unstructured data – commonly appearing in e-mails, memos, news, user groups, chats, reports, letters, white papers, marketing material, research, presentations, and web pages. The ability to store and sort this data is why companies across industries are increasingly turning to the vector database. 

AI Training 

The influence of AI on the modern workforce is increasing every year, and this rapid growth is leading to an increasing demand for datasets that can be used to train these AI models. Vector databases are primarily being used to train generative AI like LLMs. These LLMs are limited by the data they are released with and can quickly go out of date due to the rapid evolution of industries and their technology. A vector database can continuously train the models beyond their release date by constantly adding to the dataset. Healthcare IT News noted how vector databases are used in healthcare to ensure the LLMs get fed more diverse data. More industries are incorporating generative AI into their structure and public-facing services. For example, Akansha Bisht explored how AI is now used in customer service to reduce handling times and provide omnichannel support and 24/7 customer service. To keep these customer service models up to date, many companies will use a vector database that trains the AI so it can evolve with new information seamlessly. 

Cybersecurity 

As the amount of data that can be collected and used grows, so does the pressing need to protect it. Vector databases are becoming used by cybersecurity firms as an effective tool to discover cyber-attacks. A paper on Utilizing Vector Database Management Systems in Cyber Security outlines how a nearest neighbor similarity search on complex data objects can be used in various cyber security applications such as anomaly, intrusion, malware detection, user behavior analysis, and network flow analysis. One area in which vector databases are proving essential is authentication. As society moves from text-based authentication to biometric authentication, vector databases are becoming one of the most effective tools for storing vectorized fingerprints or iris and retina data. Once in the vector database, the biometric data can be used for real-time authentication across many applications. 

The future of vector databases looks secure as the global requirement for advanced data management systems increases. With AI leading us into a new society where data is vitally important for advancing technology and providing good services, vector databases could become one of the most important tools industries have at their disposal. For more information on the latest Tech Trends, do check out the rest of our site.