Luka Jasionyte, from the marketing team at Snap Analytics, catches up with Jan Van Ansem, Co-founder and Head of SAP at Snap Analytics, to get the lowdown on his journey through data and analytics, and to share insights on the transformative power of Generative AI, the shift towards real-time data warehousing, and strategies for overcoming enterprise data integration and governance challenges.
What inspired you to get into data and analytics, Jan?
My passion for coding started early – I was just 12 when I bought my first home computer, eager to dive into programming with Basic. That initial curiosity soon turned into a career in software development, where I honed my skills in building applications and systems. At the time, data & analytics wasn’t recognised as a distinct field; rather, it was an underlying component of software engineering and database management.
The landscape shifted dramatically when Ralph Kimball published The Data Warehouse Toolkit, introducing methodologies that sparked widespread discussions about data modelling and architecture. This led me to explore the contrasting philosophies of Kimball vs. Inmon, each offering unique approaches to structuring enterprise data. That debate ignited a deep interest in understanding how businesses could best harness their data and analytics to drive decision-making.
Since then, I’ve remained captivated by the challenge of designing optimal enterprise data models, ones that not only store information efficiently but also empower users with actionable insights. Whether it’s crafting intuitive data warehouses, refining business intelligence strategies, or integrating modern analytics tools, I’ve always been driven by the pursuit of solutions that transform raw data into meaningful knowledge.
What is the most exciting trend right now?
AI is the obvious one. It’s completely reshaping how we work and live. From automation to predictive analytics, personalised experiences, and smarter decision-making, it’s changing the game across industries. And the exciting part? It’s not some distant futuristic concept anymore. AI is already making businesses more efficient and innovative every day.
But for me, the real breakthrough is something that has been talked about forever but never fully realised at an enterprise data scale. Real-time data warehouses. For years, this has been the holy grail of data warehousing. Something that’s always been promised but never quite delivered in a way that works seamlessly for large businesses. The problem has always been the reliance on batch processing, which means companies are stuck making decisions based on outdated data instead of seeing what’s happening right now.
Now though, we’re finally seeing real-time analytics become a reality. Thanks to advancements in cloud computing, AI-powered data processing, and cutting-edge architectures, enterprises can move beyond traditional reporting and actually act on insights as they happen. Whether it’s fraud detection in finance, predicting stock levels in retail, or optimising supply chains in real time, these innovations are making data warehouses more powerful than ever.
We’re not quite at full-scale adoption yet, but we’re closer than ever. And soon, real-time enterprise data won’t just be a competitive edge. It’ll be the standard for businesses that want to stay ahead.
What are some common data-related challenges that large enterprises typically face?
One of the biggest challenges is integrating missing pieces of data quickly. Despite our best efforts, business users almost always require more detail than what is readily available in a data warehouse. Closing the gap from 90 per cent complete to 100 per cent often demands disproportionate effort, consuming significant time and resources.
A major reason for this difficulty is that enterprise data is typically spread across multiple platforms, legacy systems, and departmental silos, making it difficult to locate and consolidate. Additionally, data governance policies and security restrictions can further delay the process, adding layers of complexity when accessing specific datasets.
More agile tools and processes, along with improved visibility of where data resides, can help enterprises tackle this issue more effectively. Solutions such as real-time data integration, AI-driven analytics, and self-service data platforms are making it easier for business users to retrieve insights on demand without needing to rely entirely on IT teams. As technology progresses, organisations that prioritise agility and seamless data access will gain a competitive advantage in leveraging their information for strategic decision-making.
What are the top client priorities for those looking to drive successful outcomes in data and analytics?
One of the biggest priorities for clients is improving customer engagement, understanding what truly drives customer behaviour through data. Businesses don’t just want surface-level insights; they need a deep understanding of how customers interact with their products, services, and digital channels. Customers now expect personalised experiences, but delivering them at scale is only possible with smart enterprise data systems that can identify patterns and preferences in real time. Whether it’s AI-driven recommendations, predictive analytics, or automated insights, businesses are looking for ways to refine their offerings and strengthen customer relationships.
A major challenge is connecting scattered data sources. Many organisations struggle with fragmented data spread across multiple platforms, making it difficult to get a complete picture of customer behaviour. By integrating data from different systems and applying machine learning models, companies can go from reactive decision-making to proactive, tailored engagement.
Jan, why Snap Analytics?
At Snap, delivering great solutions isn’t just about technical expertise, it’s about teamwork, learning, and making a real impact. Our consultants work closely with customers’ teams, sharing knowledge and refining strategies to create the best possible outcomes.
We also have strong relationships with vendors, helping shape product roadmaps and drive innovation that benefits our clients. But beyond the work itself, what really sets us apart is the people. We have a talented, forward-thinking team that’s not only skilled but also genuinely passionate about problem-solving. And most importantly, we create an environment where people actually enjoy working together, making Snap a great place to collaborate and grow.
