The rapid adoption of AI shows no sign of slowing down as more businesses look to develop AI products and services to drive growth and innovation. But delivering data to AI models is not easy, and many will soon discover their data infrastructure may not be adequate to support them on their AI journey.
AI is only as good as the data that it can access and learn from. Which is why enterprise data stacks need to evolve to become AI data stacks capable of supporting the next generation of AI applications. Fortunately, the fundamentals are already in place. Businesses can leverage the data architecture and solutions that were built for data analytics to power AI workflows and feed their AI and ML models.
The truth is that analytics and AI are just two halves of the same data problem. They both turn raw data into insights that solve real business problems, and both rely on strong foundations built using data architecture. However, data never stands still, and today there are new frontiers of value, particularly in relation to AI. It is because of this that businesses are developing a new foundational layer for an AI data architecture to power new applications and services, as well as the underlying infrastructure that supports them.
What can AI do for you?
AI is only as good as the data it can reach, but enterprise data is fragmented, siloed and often outdated. To capitalise on their AI investments, businesses need an AI data architecture that spans their cloud and on-premises environments, accelerates AI innovation, and solves business problems faster.
To achieve this, AI needs a scalable data architecture that accesses data from multiple sources in multiple formats while governing it securely. That’s why businesses are adopting solutions that use the same data lakehouse architecture used for data analytics to feed their generative AI and machine learning (ML) models. These technologies are capable of serving AI workloads to remove bottlenecks in AI data workflows, exactly like it helped to remove them for data analytics. Years of development and evolution have led to the convergence between the needs of data analytics and AI. To the extent that today, answering the question, “What can your data do for you?” increasingly means going beyond analytics, it’s referring to AI.
Optmising the AI data stack
There is no AI without data. Not a single AI model operates without data to train on, and the continued flow of data into models allows them to grow. In this sense, data is the foundation, not just the foundation of analytics but also the foundation of AI. What you build on top of it depends on your ability to build that foundation in a secure, reliable, and predictable way.
Essentially, the platform you used to power your analytics data stack can also be optimised to power your AI or ML data stack. It could power the data stack that drives your business intelligence dashboards, your data applications, or your AI models, acting as both an SQL query engine and an AI query engine. The same things that make an analytics data stack successful also make an AI data stack successful. In both cases, data needs to be accessible, organised, and governed. With AI growing so fast, there is a huge and growing demand to build data foundations in every business, whether they use AI today or are thinking of adopting it tomorrow.
Laying the foundation for AI data
Regardless, businesses need to overcome the inherent challenge of accessing fragmented and siloed data. The fact is that data silos in AI are no different from data silos in data analytics, and they hold back the ability to derive value from your data. The solution lies in adopting a platform that provides you with a single foundation for all your AI data, one that provides you with a strong data stack capable of supporting your AI models.
That platform needs to be built to scale data governance as quickly as it scales data itself. This is already a focus for data analytics, where it creates a secure foundation for your data across multiple environments–cloud, on-premises, and hybrid–ensuring that the right people can access it in the right way. It also needs to be easy to use. This means that as your team works to pull together data sources, identify context, and feed this into an AI model, their energy and efforts create results. Essentially, speeding up your ability to move from development to deployment, to go from AI proof of concept to full-scale productive intelligence. This process will power the foundation of your AI architecture to help you get the most value from your data, whether that means analytics or AI.
Connecting the dots between AI and analytics
Data architecture has always had a huge impact on the success of data analytics, which is exactly why getting this technology right has meant the difference between success and failure throughout the history of big data. With the shift towards AI, data architecture is once again in focus. Just like before, a good foundation for this data architecture is essential. Without data, you don’t have anything. That’s true of data analytics, and it’s also true of AI. And while AI expands the possibility of business value in new and exciting ways, achieving those objectives remains rooted in business value. That’s why it’s essential to have a platform that provides the foundation for all your data, encompassing analytics and AI.
