The growing demand for real-time data to power AI applications is compelling businesses to reevaluate their traditional data architectures. Legacy systems typically rely on separate platforms for transactional and analytical processing, leading to inefficiencies and delayed insights.
Translytical databases are emerging as a critical solution, seamlessly integrating both transactional and analytical workloads into a single, unified platform enabling enterprises to support modern AI-driven applications such as conversational AI, chatbots for customer service, and real-time personalisation. The continuous, consistent, real-time data from translytical databases drives the performance and accuracy of AI applications.
Translytical benefits beyond real-time data
The rapid adoption of translytical databases is driven mainly by their ability to support broader AI use cases. As organisations increasingly seek to harness the full potential of AI, the need for such platforms will further grow. Several key advantages make translytical databases essential for powering these advanced AI-driven use cases: Real-time data for contextual accuracy.
AI agents, large language models (LLMs), and retrieval-augmented generation (RAG) systems thrive on vast amounts of data, and their value is maximised when the data is current. Translytical databases provide access to real-time data, ensuring that AI systems receive the up-to-date context needed to generate accurate responses.
This is critical in applications such as customer service chatbots, which require account or order information, and financial analysis tools, which need real-time market data and customer portfolios.
Optimised data integration for AI. RAG systems frequently need to pull vast amounts of contextual data from multiple sources to improve content accuracy. Translytical databases streamline this by offering a unified platform that combines both transactional and analytical data. This integrated data view enables generative AI models, AI agents, and LLMs to generate more accurate response. Additionally, many translytical databases now incorporate vector capabilities, enhancing data retrieval for RAG applications by identifying similar data quickly.
Centralised data governance to protect sensitive data. With growing concerns over data privacy and security in AI, translytical databases offer robust governance features that control data access and ensure compliance with regulatory standards. By consolidating transactional and analytical data into a single platform, these databases enable organisations to maintain stringent data security measures, protecting sensitive information and fostering trust.
The translytical advantage
Translytical databases are transforming the way that businesses process and analyse data. As organisations strive to harness the full potential of AI, these databases have become crucial for success. To guide enterprises through this evolving landscape, Forrester published The Forrester Wave: Translytical Data Platforms, Q4 2024, which evaluates the top 15 vendors in the translytical database market. This comprehensive analysis highlights the leading providers, offering valuable insights that can help select the most suitable provider.
If your organisation still uses separate systems for transactional and analytical workloads, now is the time to transition to a translytical database. This shift will help reduce issues with AI applications, such as hallucinations, by ensuring that your data is consistent, reliable, and accessible in real time.
Originally posted in Forrester
