In recent years, businesses have started to embrace new ways of leveraging their data than just producing traditional dashboards or reports. There is now an opportunity to have an advantage over competitors by using systems that will be capable of reasoning/recommending/acting increasingly autonomously. The rise of Generative AI and Agentic AI (which is currently emerging) will only continue to push this trend; however, there’s one important point about AI capabilities that these trends highlight: the scalability of an AI’s capabilities is determined by the ability of the underlying data infrastructure to support it.
For most organizations, the underlying data infrastructure required to support this scalability is not the challenge when it comes to implementing machine learning. The challenge is having a modern data infrastructure that is designed for the three key components required for effective machine learning: real-time data (data that can be delivered quickly and accurately), trustworthy data (data that people can trust as accurate and complete), and governable data (data that is stored in a consistent, reliable, repeatable fashion).
Traditional data platforms require use of the following:
Generative AI models and agentic AI systems depend on having current, contextually accurate, and well-governed data. This can mean accessing data from multiple databases or storage systems at once, accessing data in real time when using Generative AI models and agentic AI systems.
If the data accessed has been delayed, is fragmented or poorly governed then results from the query will be inaccurate regardless of the capabilities of the models used.
Nowadays, the term "Modern Data Infrastructure" refers to an architectural model, not just a collection of tools, that supports all three; speed, scalability, and insight.
The fundamental components of a modern data infrastructure are:
The transformation from reporting to operational or decision-making use cases means that data is now seen as an asset rather than a liability.
Generative artificial intelligence now has the ability to create not only textual and visual content; but to create new forms of knowledge from existing knowledge. In order to perform this task, Generative AI requires:
A modern data architecture provides the ability to use retrieval-augmented generation (RAG), create vector-based architectures for data storage, develop scalable training and inference models. Without these abilities of the Data Architecture, enterprises will find it nearly impossible to run their Generative AI projects successfully and egress their current phase of experimentation into mainstream operations.
As Agentic AI becomes even more advanced, it will have the capabilities to create its own decisions for performing work as opposed to just responding. For this reason, Agentic AI will allow for automated responses to incidents, optimizing supply chains, and enhancing customer service.
In order for Agentic AI to work properly and provide a safe means of resolving incidents and performing other functions, the data infrastructure must provide the following:
As a result of this evolution, modern data infrastructures will become the Control layer, not just the Data layer; thereby allowing for autonomous systems to work within specific, predefined Business, Security, and Compliance constraints.
Artificial Intelligence (AI) has made the quality of data a greater business risk and not just a technical issue due to bad quality data leading to hallucinations, bias, or unintentional failures in automation.
Modern data infrastructure embeds data governance within the architecture with:
The approach enables organizations to scale AI while ensuring that trust is retained by all stakeholders including customers, regulators, and internal stakeholders.
Modernizing the cloud and optimizing your data are two parts of the same solution. While most approach cloud modernization by either migrating apps or optimizing infrastructure, the AI world makes data modernization the lead subject.
The following attributes of a modern data infrastructure enable:
A modern data infrastructure also provides the foundation for analytics, machine learning, and AI teams to work together seamlessly.
Generative and agentic artificial intelligence (AI) change many of the ways that businesses run; they show weaknesses with legacy data architectures. Organizations investing early into a modern data architecture can operate more quickly and innovatively without risk, and can realize true value from their use of AI at large scale.
As Ancrew Global Services sees it, modernised data architecture should be seen as a key component of your cloud modernisation strategy rather than just a technology upgrade. The result will be a structure where intelligent systems can operate in a manner that is reliable, ethical and aligned to business objectives both today and into the future.