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Becoming truly data-driven requires more than adopting new tools—it demands clear alignment between business goals and data architecture.
At Data Summit 2026, John O’Brien,principal advisor and industry analyst,Radiant Advisors, led the pre-conference workshop, “How to Evolve Your Data Architecture from BI to AI,” guiding attendees through a proven, four-step methodology for designing modern data platforms that deliver real, scalable business value.
The annualData Summitconference returned to Boston, May 6-7, 2026, with pre-conference workshops on May 5.
“We are taking architecture and really extending it from data science, big data to generative AI and BI architectures,” O’Brien said.
Alignment is key, it’s there to enable “something” in the business, he explained.
“What is strategic and how do we deliver that in a tactical manner?” O’Brien said. “It needs to deliver value and make an impact.”
Participants learned how to convert business priorities into architectural choices and critically assess emerging technologies—from cloud-native platforms to data lakehouses and data fabrics—to build a focused, actionable road map.
“We will figure out how to focus and prioritize so we don’t feel like we’re ‘boiling the ocean,’” O’Brien noted. “Let’s tackle that in an iterative fashion. Speed wins the day, no question.”
And data leaders gained practical frameworks to determine which components of the modern data stack will create the greatest impact for their organization.
The key to starting to approach delivering modern data platforms is to align with the business strategy. It’s imperative to ensure that you understand and deliver on business goals.
“What I hear from some businesses is, ‘I don’t trust our data,’” O’Brien said.
According to O’Brien, the four-step methodology to design successful data platforms includes:
- Business strategy: Identify the business outcomes to achieve.
- Data and analytics strategy: Translate to data and analytics capabilities.
- Modern data architecture: Prioritize the cloud roadmap for analytics.
- Modern data infrastructure: Roadmap to implement technology for optimal ecosystem in the cloud.
“That’s how we move through the alignment,” O’Brien said. “What you manage over the years, you’re just managing your data ecosystem.”
Continuously optimize the modern analytics lifecycle for enterprise scalability. Adopt principles for modern data architecture, integration, and cloud-native.
Business analytics categories to align with include understanding customer behavior, understanding products usage, increasing operational efficiency, and applying business model innovation, O’Brien said.
“If you think about modern architecture…and everything you have to learn…if that is going to take 80% of your attention, that means the architecture should be 20% of your focus,” O’Brien noted.
To be a data-driven company, from an architecture perspective you want to empower people to work with data, O’Brien stressed. A business needs to adopt intuitive tools, empower people, optimize the analytics lifecycle, and set analytics as a strategic priority.
The bi-modal process for BI decouples business urgency from analysis and design. Governance is a big part of this to make sure everything is correct and secure, O’Brien said.
Aligning enterprise analytics and AI capabilities include business intelligence and reporting; self-service data analytics; data science, ML, and AI; and GenAI, LLM, and RAG.
“I’ve seen all the trends, and I was watching and waiting on GenAI to gain traction,” O’Brien said. “What we’re finding is, where the adoption is and where it’s working. In order to trust AI to do something we need data lineage, it’s so important.”
The future of architecture could be the merger of AI and context, he predicted.
“If [AI] could do everything, would you even trust it,” O’Brien said. “That’s what needs to be worked on.”
Many Data Summit 2026 presentations are available for review at https://www.dbta.com/datasummit/2026/presentations.aspx.