“Start with Business Challenges, Not Solutions,” Says Justin Rister, Microsoft

April 30, 2026

Justin Rister, Senior Cloud and AI Specialist at Microsoft, explains why leaders should start with business pain points, not technology. He shares how Fabric unifies the analytics stack for teams of all skillsets, why Databricks and Fabric are a better-together story, and how an AI layer on unified data empowers business users to ask questions and get answers without waiting on IT. 

• What does it mean to be a strategic partner instead of a product pusher?  
• How do you remove bottlenecks by letting business users access insights directly? 
• Why should leaders think big, start small, and scale fast? 

Watch the whole interview: 

  

Read the podcast as an interview: 

(The interview was shortened and edited using ChatGPT 

Mark Kohout: Hello and welcome to this Adastra podcast. My name is Mark Kohout and I lead the North American Governance Practice for Adastra, a global data, AI, and cloud systems integrator. We're coming to you today from Atlanta at Fabcon 2026. Joining me today is Justin Rister, Senior Cloud and AI Specialist at Microsoft. 

Justin works closely with enterprise leaders to help them simplify complex analytics environments and unlock the full potential of their data through Microsoft's cloud and AI platforms. He helps organizations rethink how their data, analytics, and AI strategies come together, enabling them to move from fragmented insights to a unified 360-degree view of their business. He spends much of his time working directly with executives, guiding conversations around modern analytics architectures, AI adoption, and how platforms like Microsoft Fabric can help organizations move faster and make smarter decisions. 

Justin, great to have you here. Thanks for taking the time and welcome to the podcast. 

Justin Rister: Thanks for having me. Happy to be here. 

Mark Kohout: Let's start with you telling us about yourself and your journey into cloud and AI. 

Justin Rister: My journey started about ten years ago. Prior to Microsoft, I worked for an organization that had, like most organizations, a lot of data scattered all over the place. I actually started on the business side as a business analyst trying to help the organization leverage their data, bring it together, and get meaningful insights. 

Mark Kohout: Understanding the data, no doubt. 

Justin Rister: Absolutely. It's all about helping the business make informed decisions. They had data everywhere, and bringing that together was my focus. Ten years ago, technology was evolving fast. It's a lot different than today. Even a year ago was different than today. I leveraged different tools across the Azure stack to integrate data. 

Mark Kohout: With individual tools like Data Factory, Synapse? 

Justin Rister: Exactly. I was learning the skillset of bringing data together and helping people digest it meaningfully. The fun part is creating a dashboard for the business to make informed decisions. Then about four years ago, I moved into Microsoft and started helping organizations layer AI on top of their data. That's when AI really started to kick off, showing them how to take unified data to the next level with AI for informed decisions and scale. 

Mark Kohout: Which brings us to today. Many organizations have complex analytics environments with siloed data, multiple tools for visualization, integration, various data sources and platforms. When working with executives, what are the biggest challenges leaders face when trying to simplify and optimize their analytics architecture? 

Justin Rister: I break it into a few areas. Number one: data is all over the place. Every organization has multiple data sources, multiple ERPs, especially if they've grown through acquisitions. 

Mark Kohout: Post-merger integration. 

Justin Rister: Exactly. Multiple data sources on-prem, multi-cloud, legacy systems. The challenge is integration. The CIO wants to be a Chief Information Officer, not a Chief Integration Officer. They're trying to figure out the right tool to integrate data. Everyone knows the technology exists, but there are so many options. What's the right one that connects to dozens of different data sources? What's right from a skillset standpoint? Many organizations I work with don't have huge IT teams. Maybe a few people with particular skillsets and no ability to hire more. 

Mark Kohout: That aligns with what we hear from CIOs. Their role is about organizational impact, not just integration. A common goal we hear is creating a 360-degree view of customers, operations, and supply chains. How does bringing data together through platforms like Fabric help organizations achieve that visibility? 

Justin Rister: When we talk about business insights, we immediately think analytics, which usually means dashboards and reports. But that's only a small component. True end-to-end analytics includes integration, transformation, storage, and then visualization. With tools like Fabric, you bring all these components together in one place. 

Mark Kohout: A single pane of glass. 

Justin Rister: Exactly. Before Fabric, when I was doing business analytics, I had to figure out the right tool for each step: ingestion, storage, transformation, visualization. Fabric brings all those steps together. Organizations don't have to purchase multiple tools, integrate them, and create complex architectures. It also solves the skillset challenge. Most organizations are familiar with SQL for data transformations, but data engineers may prefer Python and Spark for machine learning models. Fabric gives flexibility to choose the right tool for the right job. 

Mark Kohout: Does Fabric play well with others? We hear about "better together" concepts. 

Justin Rister: I get that question a lot. Many of my customers are on Databricks, which is a great analytics platform for optimized data ingestion and transformation. They ask why they'd look at Fabric when they're already mature in Databricks. I love that conversation because it's a better-together story. You don't need to rebuild everything. Databricks is great for data engineers who write code. Fabric can be an extension. You can shortcut directly to Databricks storage, get near real-time data replication, and enable fast reporting. Business users can create dashboards from already-curated semantic models built by data engineers in Databricks. 

Mark Kohout: So "better together" with Databricks and Fabric means meeting different constituencies where they're at. Citizen analysts might be more comfortable in the Fabric ecosystem. 

Justin Rister: Exactly. Whether you're using Databricks, Snowflake, or another data warehouse, Power BI is often where visualization happens. People are used to it. That's what makes Fabric appealing. It's inside an environment many people are already comfortable with. 

Mark Kohout: Once organizations have their data foundation in place, the conversation often turns to AI. How do you see organizations moving from simply collecting data to using AI to drive smarter decisions? 

Justin Rister: That's the exciting part. Once your data is unified in a true foundation layer, you can have the AI conversation. Think about natural language. You want business users to ask questions the same way they'd type into a search engine and get a response. That's what you can do with unified data and an AI layer on top. Many organizations don't have huge IT teams. When the business needs an ad-hoc report quickly, IT struggles with bandwidth. Empowering business users to ask questions in a chat and get responses without building a full dashboard is priceless. 

Mark Kohout: You're removing a bottleneck. 

Justin Rister: Exactly. 

Mark Kohout: When executives begin exploring AI and data platforms like Fabric, what key questions do they ask first? What are their main concerns? 

Justin Rister: "How do I get started?" is common. They want to see business value. If it's not increasing revenue or reducing costs, why do it? I like to challenge them: give me your top three pain points and let's dive in. Don't start with solutioning or technology. Start with business challenges. 

Mark Kohout: I hear the early days of the business analyst coming out. 

Justin Rister: That's how you show value. Being empathetic with their challenges and showing a roadmap to solve them. 

Mark Kohout: It's not about the technology; it's about the business conversation. 

Justin Rister: Leaders don't come in asking about Fabric. If they do, I ask why. What are you trying to solve? We know the technology is there, but I want to understand the challenge first. 

Mark Kohout: Do they have challenges well-defined, or are you helping with visioning the art of the possible? 

Justin Rister: Not always. They know there's a problem, but defining it is half the battle. That's why I position myself as a strategic partner, not someone just talking about products. We bring in solution engineers for architectural design sessions, but the foundation is understanding what we're trying to solve. We also bring in other customers with similar challenges to share best practices. I rarely hear a completely unique problem. 

Mark Kohout: At Adastra, we find many clients understand the importance of data and AI potential, including building agentic workforces, but struggle with moving from strategy to implementation. What helps companies bridge that gap and see real business impact fast? 

Justin Rister: I always ask leaders to think big first. The art of the possible. If you had a magic wand to solve all your problems, what would that look like? That gets creative juices flowing. Then I draw an imaginary line: think big, but start small. Start with something narrow-scoped. If we solve this one problem, you'll see business value. It's like a pilot. Nothing you have to throw away, something you can scale. Then you can move fast understanding you've proven value. Tools like Fabric and technology advisors help scale quickly. Many leaders think they have to do everything in a big-bang approach, but you shouldn't. Think big, start small, scale fast. 

Mark Kohout: The proverbial crawl, walk, run. Justin, we're almost out of time. One last lightning round question: for leaders trying to modernize their analytics environment and prepare for AI, what advice would you give as they begin their journey? 

Justin Rister: Don't become overwhelmed with all the different technologies. Don't start with solutioning. Start with understanding your true pain points. I'm not throwing solutions at the wall to see what sticks. I want to be seen as a strategic partner on the business side. Don't start with "which LLM do I use?" We'll have that conversation later. Start with business challenges. 

Mark Kohout: Thank you, Justin. This has been a very insightful conversation. I'm taking away the value of data platforms meeting organizations where they're at, their ability to be a force multiplier even for small IT teams, and the importance of business focus. Thanks for taking the time. I know it's been a busy week. 

Justin Rister: Thanks for having me. 

Mark Kohout: And to our audience, if you've enjoyed today's podcast, be sure to like and subscribe for more insights on getting results with data and AI. Until next time, thanks for listening and so long for now from Fabcon 2026 in Atlanta.