Webinar replay | Setting up a modern data foundation: A data leader’s guide to making the right choices
How do you build the right data infrastructure and team for your business? Learn how to create a future-proof data foundation, move from basic analytics to AI, and avoid common pitfalls when growing your team!
Building a modern data foundation is more crucial than ever to keep pace with the possibilities of data and AI, as well as the ever-growing demands of today's businesses.
In a 40-minute webinar we recently released, I explain the essence of a modern data foundation, which boils down to two key topics: how to enhance your data infrastructure and develop your data team as a data & analytics leader.
The lessons learned come from years of helping companies navigate the complexities of data infrastructure, cloud solutions, and data-driven insights. These lessons can be used as a guide to help make your decisions as you gradually build your own modern data foundation.
Why Your Data Foundation Matters: The Crucial Role of Modern Data Infrastructure
In today’s data-driven world, we’re collecting more information than ever before. Be it in retail, e-commerce, leisure or other industry. But raw data by itself is just noise. It’s the foundation that determines how well we can make sense of it, and how quickly we can turn it into actionable insights.
Without the right infrastructure, businesses risk falling behind. Worse, they risk missing out on key opportunities that only the right data, structured properly, can reveal.
For many companies, legacy systems and outdated architectures are holding them back. I’ve seen it countless times—teams struggling to wrangle data from disparate systems, stuck with tools that weren’t built to scale. This is where having a modern data foundation comes into play.

What Does a Good Data Foundation Look Like?
At its core, a modern data foundation is flexible, scalable, and built to grow with your business. But let’s break that down a bit more.
- Cloud-Native Infrastructure: If you’re still relying on on-premise systems, you’re putting limits on your scalability. A cloud-native approach gives you the flexibility to grow and adapt as your data needs evolve. In my work at Crunch Analytics, I’ve seen firsthand how companies benefit from using platforms like Google Cloud, AWS, and Azure. By leveraging cloud-native services, you not only reduce the overhead of maintaining physical hardware but also unlock a range of tools specifically designed for modern data processing.
- Multi-Cloud Strategies: The days of being locked into a single cloud provider are over. Companies today benefit from using multiple cloud platforms to get the best out of each one. Whether it's leveraging AWS for its data lakes, Google Cloud for AI services, or Azure for its robust enterprise integrations, a multi-cloud approach gives you the best of all worlds.
- Data Processing Power: You need an infrastructure that can handle large volumes of both structured and unstructured data. The rise of AI and machine learning means businesses need data foundations that can process these new types of data efficiently. This is where modern tools come in—streaming technologies, data lakes, and warehousing solutions that can handle diverse and complex data sets.
Building the Right Data Team
No matter how advanced your infrastructure is, it won’t amount to much if you don’t have the right team in place to work with it. One of the key points I touched on during the webinar is that people are just as important as the technology. You need a team that can manage your infrastructure, but also one that knows how to extract the value hidden within your data.
In my experience, a successful data team is cross-functional. You need engineers who can build and maintain the data pipelines, but you also need analysts who can work closely with business stakeholders to derive insights. This is the bridge that turns data into decisions. When your team can speak the language of both technology and business, that’s when you truly start to see results.
I’ve been part of projects where this cross-functional approach made all the difference. For example, working with teams that combined data engineering with domain expertise allowed us to not only build the right infrastructure but also ensure that the insights we were delivering were relevant and actionable.

The Future is AI, But You Need the Right Foundation
It’s impossible to talk about the future of data without mentioning AI. In the webinar, I touched on the growing interest in AI technologies—and it’s clear that it will remain a hot topic for years to come. However, AI is not a silver bullet. It can only perform as well as the data it’s trained on, and the systems that feed it.
That’s why having a solid data foundation is critical. If your infrastructure is fragmented or unreliable, AI won’t be able to deliver meaningful insights. That’s something I stress to all the clients I work with—before you invest heavily in AI tools, make sure your data foundation is solid, flexible, and ready to scale.
Final Thoughts
Building a modern data foundation is not a one-time project—it’s a long-term investment in the future of your business. As data grows and new technologies emerge, your infrastructure needs to be ready to evolve with them. You will need a team that is no longer merely ‘part of the IT department. The companies that succeed will be the ones that can adapt quickly, extract insights efficiently, and make data-driven decisions confidently.
If you’re a data leader looking to scale your organization’s data capabilities, start by evaluating your infrastructure. Is it flexible enough to grow with your business? Is your team equipped to handle both the technology and the insights?
In a future webinar, we’ll focus on that specific question. We’ll share a guide on how to perform your own data infrastructure audit. Keep track of our socials to learn more!
Replay the webinar here:
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