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Belgium 20-10-2026 Squad Only Virtual English

Deciding What Runs Where: Cloud, Edge, On-Prem, or Hybrid; How to place workloads based on performance, cost, sovereignty, resilience, and AI requirements.

Cloud decisions are becoming more selective. The challenge is deciding where each workload should run based on cost, performance, control, resilience, sovereignty, and business need. Three pressure points usually drive the decision. - Cost visibility becomes harder when usage grows across cloud services, data platforms, and AI workloads. - Performance and latency matter more when systems need fast access to data or support operational processes. - Sovereignty and resilience become critical when data location, continuity, vendor dependency, and exit options enter the discussion. The working question is simple: how do we place workloads where they create the most value while keeping the hybrid landscape manageable? If these trade-offs are on your agenda, let’s compare how others are handling similar choices.

Belgium 10-11-2026 Squad Only Virtual English

Choosing the Right Data Platform Without Creating New Silos; How to balance speed, flexibility, semantics, governance, and long-term maintainability.

Data platforms are meant to simplify access and support better use of data. The challenge is choosing and governing the platform landscape in a way that creates consistency, reuse, and long-term value. Three pressure points usually appear. - Platform choice becomes difficult when teams have different needs for speed, flexibility, cost, and control. - Business meaning needs protection: customer, revenue, product, risk, and FTE need shared definitions across tools. - Ownership and governance need to be clear, so platforms grow with accountability and discipline. The working question is simple: how do we give teams the data capabilities they need while keeping the platform landscape understandable, governed, and reusable? If these questions are familiar, let’s compare approaches with others facing the same data landscape.

Belgium 13-11-2026 Squad Only Physical English

Building the Business Case for New IT Capabilities; How to justify investment when value is strategic, shared, or only visible over time.

Many IT capabilities are essential and difficult to fund through a simple project ROI. The challenge is showing why architecture, governance, resilience, cybersecurity maturity, data foundations, and AI readiness deserve investment before the gap becomes expensive. Three pressure points make the case harder. - Shared value means the benefit often sits across several teams, while the budget sits in one place. - Timing creates friction because the cost is immediate while the value appears over time. - Risk translation is needed so business and finance leaders understand the consequence of delaying the investment. The working question is simple: how do we turn foundational IT capabilities into credible investment cases that link cost, risk, business value, and future flexibility? If you are building these cases internally, let’s compare how others are positioning value, risk, and timing.

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Nordstrom Analytical Platform and Enhancing the Customer Experience

Nordstrom develops the Nordstrom Analytics Platform (NAP), which uses AI-powered analytics to personalize the shopping experience through deeper customer insights and predictions.

Chief Technology Officer, Edmond Mesrobian, recently participated in a conversation with VentureBeat's Hari Sivaraman, discussing the role of data infrastructure to enhance the customer experience. Focusing on AI-powered analytics, Edmond described the journey of building the Nordstrom Analytical Platform (NAP) to leverage deeper insights and predictions for a personalized experience. See highlights from their conversation below.

With our Nordstrom Analytical Platform (NAP), we are improving on our ability to provide personalized product discovery and service.

When we think about analytics as opposed to data reporting, timing is the most precious commodity, and making the timeliest decisions required us to build a platform. Whether it's stores, fulfillment centers or online, we built and translated those signals through models and actions. It's difficult to buy that type of solution off-the-shelf because you need to wire your enterprise. We asked, can we generate a prediction cost-effectively and translate that into a customer benefit and service? That's where we started, and we realized we needed to create it as opposed to buying it.

We embarked on this journey to get the business aligned so that we can capture events and stream them in real-time or near real-time into our analytical platform. We then layered analytical models of a variety of forms to translate into predictions—today's predictions are the new KPIs. Our goal was not to be technology providers for the components, but rather stitch it together to create the kind of analytical platform that we can then robustly drive machine learning. What we call our one-hop engine essentially translates our business events into something that could then be activated through a model. And so that's the special sauce—how we put the business events together, how we transformed them from one hop into our models and then generated predictions on the other end.

Our goal is to make the digital experience personal by offering our customers curated product choices on an individual level.

There are over a hundred AI models that we leverage daily at Nordstrom—a significant portion of them are backward-facing capabilities from inventory control to fulfillment and how to route orders to the nearest store and more. Customers will experience those capabilities indirectly in terms of getting orders shipped and delivered on time. Our mission and holy grail from a digital perspective is delivering the capability of discovery. Taking what we know about our customers we can deliver personalized products and better selection—through improved Looks, style boards and more. We are using AI in a more robust way to drive product discovery and personalization.

We are also focused on what we call our fashion map. The fashion map essentially takes a natural language-based approach with deep learning to interpret images and information from social networks—this allows us so to get to know customers through a natural language conversation, as opposed to keyword searches that are very taxonomy driven and hardcoded. We are continuing to work on achieving AI discovery to get closer to our customers in a personalized way.

See the full conversation below.

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