How AI and MongoDB are a game-changer for data insights

Artificial intelligence (AI) is a hot topic across many areas of business, with its popularity and use cases growing by the day. 

The 2024 Artificial Intelligence Report by Financial Information Management (FIMA) found 67% of senior data executives use AI and machine learning for market analysis purposes, while more than half (53%) use it to extract specific information from vast amounts of documents.

Why accurate data insights matter 

To remain competitive organizations need easy access to insights from even the largest data sets. But improvement is still needed, as a report from the Harvard Business Review found just 3% of enterprise data is of an acceptable standard (quality score of 97+). 

As a NoSQL/non-relational database, MongoDB is able to handle both unstructured and semi-structured data, giving it an advantage over traditional relational databases, which can struggle with these data types. This makes it a popular choice for sectors like finance, healthcare, retail and other sectors needing insights from large volumes of data. 

Organizations that use MongoDB to gain actionable insights from their data, should make AI-integration a priority. 

How AI benefits MongoDB querying

One of the areas that AI can prove particularly useful is its ability to remove the barrier to query writing in MongoDB that is often in place for those without advanced coding skills. With AI-powered natural language query tools, people from across the business – from developers to senior leaders – can easily query databases using natural language, increasing access to real-time insights without the need for coding expertise.

These tools allow you to write a question in natural language and turn it into a query ready to run on your MongoDB database, presenting an answer in seconds. Tools such as this are creating new opportunities for organizations to extract valuable insights from their data. They can even write complex queries including aggregations.

Of course, your querying is only as good as the information you have in your database and how it is organized. AI is capable of helping with this by looking at the schema (or lack of) of a MongoDB database, then take action to split it into multiple collections or standardize a particular field.

Features such as vector search, which uses advanced mathematical models to understand the context and meaning behind queries, are also closely associated with AI as its functionality is powered by machine learning. 

Businesses are also benefiting from retrieval-augmented generation (RAG). This pulls in information from diverse data sources from across data warehouses and uses it to generate a relevant and appropriate answer. 

Other benefits of RAG include improved understanding of business performance, consumer behavior patterns and more-informed, data-driven decisions. 

As a non-relational, document-oriented database known for its scalability and flexibility in handling complex, diverse data types, MongoDB is well suited to RAG. With data structured in a flexible, JSON-like format, MongoDB enables rapid development cycles and real-time responsiveness, so it can easily offer the kind of data AI needs. 

MongoDB is also integrated with AI platforms from the likes of AWS and Google Cloud, making it even easier to introduce AI. This integration gives companies a comprehensive, cost-effective way of boosting their data-driven decision-making and analytics strategies.

MongoDB’s AI-ready infrastructure 

As one of the most popular NoSQL databases, MongoDB offers users a range of powerful tools to support AI workflows. MongoDB Atlas offers native integration with cloud AI tools, so businesses can take advantage of machine learning and analytics services directly from their database.

With these AI-ready tools, MongoDB allows organizations to deploy intelligent applications, improve security, and streamline operations. The real-time, scalable nature of MongoDB also means that businesses can deploy AI solutions in production faster and with fewer bottlenecks.

As AI continues to evolve, MongoDB will likely see more integrations with AI-driven analytics and real-time decision support. 

Other uses of AI and MongoDB

The combination of AI and MongoDB also opens up the possibility of businesses benefiting from enterprise chatbots. The FIMA 2024 Artificial Intelligence Report I mentioned earlier found customer service and account management-focused chatbots are a priority for 70% of firms. 

Using a combination of factors including natural language processing, machine learning, and RAG, chatbots are increasingly able to call on large volumes of data to handle complex interactions and give accurate responses. 

Businesses across multiple industries are benefiting from chatbots built on MongoDB and AI to improve customer experiences and interactions and lower operational costs. 

AI-driven predictive models based on MongoDB data are another use of the technology. For example, financial organizations can detect fraud, assess credit risk and offer personalized services to customers. FIMA found 46% of businesses see fraud detection and compliance as a primary use case for AI and ML. 

Ready to try it for yourself? 

With a growing list of benefits to healthcare, finance, retail, and any industry where real-time insights prove useful, AI and MongoDB looks set to have a profound impact in the years to come. 

Business leaders looking to future-proof their data strategy could find an AI-ready database like MongoDB opens up new possibilities.