AI-powered analytics that work in daily business

Data only creates value when it quickly leads to confident decisions. That was the focus of our “From Data to Action” webinar with our partner Omni. In 30 minutes, we showed how AI-supported analytics and a well-curated semantic layer translate business questions into auditable BI queries, significantly accelerating data-driven decision-making.

Tobias Lanzl of Hopmann Marketing Analytics outlined the pragmatic Analytics-to-Action framework with a focus on relevance, data quality, and ownership. Liam McCarthy of Omni demonstrated in the live session how a natural-language question becomes a meaningful visualization in just a few steps.

This recap distills the key takeaways from the webinar and includes a complete FAQ with all questions and answers from the session.

• Starting point: Many companies have plenty of data and dashboards but make decisions too rarely based on data. The reasons are data silos, poor data quality, and BI tools that are too complex for teams that don’t work with analytics regularly.
• Framework: Start decision-first, broaden data access, experiment iteratively, and assign clear ownership.
• Omni in action: AI-supported queries via a semantic layer that precisely translates natural language into validated queries. You can switch to the classic Explore mode at any time.
• FAQ: Questions ranged from “Will AI replace analysts?” to “How long does setup take?” and “How accurate are the results?” You’ll find the detailed answers below.

In our projects we often see this challenge: the data is being collected, but decisions don’t follow. The reasons range from scattered sources and limited data literacy to tool-driven complexity that slows down business teams in Marketing, Sales, Finance, HR, and beyond.

The results of this are overloaded BI teams, slow answers, and frustration on both sides.

Our recommended approach is Analytics to Action: a pragmatic way of working that enables teams to reach results faster. It’s the only way for companies to stay competitive today and win.

The goal is not more data but better decisions in less time. This framework ties together clear decision questions, trusted data, a business-friendly semantic layer, and short experiment cycles to move teams from the first use case to scalable routines without getting lost in data silos and tools.

Omni combines a conversational chat with a well-defined semantic layer. Natural language is translated into auditable, versioned queries with clear definitions, synonyms, and implicit filters you can inspect and adjust.

The result is correct, lineage-backed answers instead of a black box. You can jump into Explore mode at any time to refine visuals or tweak metrics.

For teams, that means fewer ad-hoc pulls, faster iterations, and decision-ready outcomes without overloading the BI function.

Short answer: No; bute the role evolves.
Why: AI cuts routine pulls and lowers the barrier for end users. Analysts become context engineers who own semantics, governance, and quality. Human-in-the-loop stays essential.

Yes. Request a meeting via the website. The team will walk you through the product and process.

Teams can sketch answers fast: ask a question in chat, review results, then jump into Explore to refine visuals. This avoids over-engineering dashboards that miss the real question.

It depends.

• With a mature stack and curated models, Omni can be near plug-and-play.
• Otherwise, take an iterative path: co-model data, build semantics, get quick wins, then refine. The goal is time-to-value, not perfection on day one.

Start “decision-first” with one concrete question (e.g., “Which campaign performs best?”). Pick the few metrics that truly matter, stand up one minimal use case, add lightweight monitoring, and iterate. Small start, compounding wins.

Accuracy is iterative. Without context, AI approximates. With a semantic layer, query transparency, and feedback loops, quality improves continuously. The key is the capability to raise precision over time, not perfection out of the box.

• Pick one quick win: one question, one team, one dataset
• Sketch your semantic layer: terms, synonyms, filters, owners
• Run rapid experiments: 1–2 weeks of testing, measuring, learning, adjusting

Want to discuss a concrete use case in 30 minutes? Let’s talk.

We’ll bring close to 20 years of hands-on marketing analytics experience to help you move from question to decision.