Organisations have always tried to gain a comprehensive view across all of their vast troves of data, but the growth in both the amount and the variety of that data has always frustrated their attempts. The desire for a unified perspective has gained a new prominence with the rise of Generative AI, which thrives on data.
In our recent webinar, Collaborative Data and AI Engineering Using AI-Assisted Data Fabric, I had the pleasure of discussing this topic with Mike Ferguson, CEO of Intelligent Business Strategies and one of the foremost experts on modern data architecture.
One approach gaining traction is AI-assisted data fabric, which uses AI to help teams simplify integration, break down silos, and build reusable data products faster. While not a one-size-fits-all solution, it offers practical ways to ease bottlenecks and give engineering teams the flexibility they need to support rapidly-evolving business demands.
Here are four key takeaways from our conversation with Mike Ferguson on how engineering teams can work smarter and deliver value faster.
1. Start with “why”: anchor your data fabric in business outcomes
Mike emphasized that, even more than other technology architecture models, data fabric is not just a set of technical capabilities, but is focused specifically on delivering faster time-to-value from data and AI initiatives, whose success criteria are determined by the business and by end-users. Before jumping into tooling or architecture diagrams, therefore, it is crucial to align with business stakeholders on the specific outcomes you’re trying to achieve:
- Are you aiming to speed up analytics for smarter decision-making?
- Do you need to automate manual data flows for operational efficiency?
- Or is your focus on enabling AI/ML models with cleaner, real-time data?
Strategy: Build a value map that connects data fabric capabilities to specific business priorities. This will help you gain buy-in and keep the project grounded in impact.
2. Prioritize self-service and collaboration across teams
One common bottleneck in data initiatives is the handoff between data engineers, data scientists, and business users. Data fabric can break down these silos by providing low-code/no-code interfaces, metadata-driven automation, and governed access to trusted data.
Mike called this the “democratization of data engineering”—a shift from centralized control to collaborative data operations where multiple roles can participate. This change in mindset is critical to avoid the failure mode of perfect architectural models that nevertheless fail to achieve success and adoption in the real world.
Strategy: Invest in platforms that support both technical extensibility (for engineers) and ease-of-use (for analysts and citizen integrators). At SnapLogic, we’ve seen customers succeed when they empower more people to build and manage their own data flows, without sacrificing governance.
3. Automate the heavy lifting with AI-assisted data fabric
Manual integration and data preparation are too slow for today’s pace of business. This is where AI and automation come into play. Mike highlighted how AI-assisted data fabric can:
- Recommend data pipelines based on patterns.
- Automatically detect and fix schema mismatches.
- Orchestrate complex workflows across hybrid and multi-cloud environments.
Strategy: Leverage platforms that bring AI-powered suggestions and automation into your integration workflows. SnapLogic’s AI capabilities, for example, help teams accelerate development and reduce errors while freeing up engineers for higher-value tasks.
4. Design for scalability and future-proofing
Finally, a data fabric isn’t a one-and-done project, it’s an evolving architecture. Mike urged organizations to design with modularity and composability in mind so they can adapt to new data sources, applications, and AI workloads as they emerge — and as, inevitably, new business requirements come along.
Strategy: Choose an integration platform that can easily connect all your systems and data, whether they’re on-premises, in the cloud, or both. This makes it simple to add new tools and scale as your business grows.
Building your data fabric strategy
To thrive in the AI era, enterprises need to move beyond siloed tools and fractured architectures. However, the rapid pace of change in both technological capabilities and business circumstances means that there is no time for massive platform migration and re-architecture projects. Enterprise Architects need a modern data fabric that connects all of the systems they are dealing with today and whatever might be added to the mix tomorrow, wherever those systems may run—on-premises, in the cloud, and at the edge. It must empower teams to collaborate without chaos, use AI to automate and accelerate delivery, and adapt as quickly as the business demands.
At SnapLogic, we’re proud to deliver the unified platform that makes this vision a reality.
You can watch the full webinar on demand to hear Mike Ferguson’s insights firsthand, or download the white paper AI-Assisted Data Fabric for a deeper dive into the architecture and strategies behind successful implementations.
