Data architecture is both complex and constantly changing. How can it support the scaling of AI across an organization?
This ebook provides three key recommendations, including inspiration from the modern data stack, for IT teams looking to build for the future of data democratization.
Ultimately, the modern data stack is about providing a seamless experience for all users, no matter what their data needs are. It:
Even for organizations that have a much more complex existing, legacy setup and therefore can’t fully leverage the simplicity of the modern data stack, the goal of providing a seamless experience for all users to work with data is a valuable takeaway.
Scaling AI from a data architecture perspective requires rethinking the role of IT itself. Business objectives should inform data architecture — not the other way around. People across all lines of business, including those without formal data analysis training, need to be able to access and use data for their day-to-day work. That means providing people with the tools to access and use data is the core of IT’s role in the modern enterprise. To avoid becoming burdened with data processing and integration jobs, AI platforms (like Dataiku) can ease the burden on IT teams
Dataiku was built from the ground up to be one central, controlled environment used by a range of profiles. This includes low-code analysts and no-code contributors on the business side. But it’s not just a low- and no-code solution. Dataiku and offers robust features to give IT teams maximum flexibility yet control over architecture:
For those on the technical side — like data scientists, but also data engineers, architects, and more — Dataiku facilitates quick experimentation and operationalization for machine learning at scale.