A feature that stands out for me is the visibility Kyvos semantic layer provides into business definitions and how they are applied across analytics environments. Having transparency into the logic behind metrics and outputs is important as more teams begin using both BI and AI. Review collected by and hosted on G2.com.

The centralized governance approach has made it easier to understand how information is being consumed across different reporting and analytics interfaces. Review collected by and hosted on G2.com.

Kyvos semantic layer provides a business-ready foundation for AI initiatives. Instead of exposing AI systems to raw datasets and technical schemas, we can leverage business definitions, metrics, relationships and business logic that already exist within the semantic layer Review collected by and hosted on G2.com.

No issues have been encountered so far. The same semantic foundation used for analytics can also be leveraged by AI initiatives. Review collected by and hosted on G2.com.

What I appreciate most about Kyvos semantic layer is how fast we can explore data across regions, products and time periods. You can move across different dimensions and drill into metrics without worrying about how the query is being handled in the background. Numbers stay consistent across reports. There’s less back-and-forth on definitions, which makes discussions more focused on insights rather than validating the data. Review collected by and hosted on G2.com.

There’s a bit of a mindset shift in how you approach analysis. Instead of building queries from scratch each time, you rely more on the pre-defined models and structures.

Once you get used to that way of working, it actually makes analysis much more straightforward across the team. Review collected by and hosted on G2.com.

One thing I really appreciate about Kyvos semantic layer is how it helps improve AI accuracy by exposing business-ready data instead of raw technical datasets. The semantic definitions create useful context that AI applications can leverage, which reduces the chances of generating misleading answers. Review collected by and hosted on G2.com.

Using it for several months now and haven't run into any major limitations so far. Review collected by and hosted on G2.com.

One thing I appreciate is that we can represent fairly complex business structures in one place. Previously, we had business logic spread across multiple models and reports. That made updates harder and introduced inconsistencies when metrics changed. With Kyvos, we moved that logic into a single semantic model, which reduced duplication and made it easier to support cross-domain analysis. Review collected by and hosted on G2.com.

Modeling flexibility is strong, so it’s worth spending time upfront to organize definitions properly. Once that structure is in place, though, it’s easier to manage changes and extend the model later. Review collected by and hosted on G2.com.

One of the reasons we selected the Kyvos semantic layer was its flexibility. Our analytics environment keeps evolving but our business definitions have remained in one place. That saved us from constantly rebuilding semantic models every time something changed. Review collected by and hosted on G2.com.

After evaluating several platforms, this was one of the few that fit naturally into our existing architecture. We really haven't encountered any major drawbacks so far. Review collected by and hosted on G2.com.

In our organization, we rely heavily on data during weekly and monthly business reviews. What I like most about Kyvos semantic layer is that it allows teams to get answers without slowing down the conversation. Its also made data more accessible across functions. Teams don’t need deep technical expertise to work with data, which has increased overall adoption Review collected by and hosted on G2.com.

Initially, it can feel limiting that you’re not building everything from scratch and are working within what’s already set up. Over time though, that actually brings more consistency in how teams approach analysis and avoids unnecessary variation across reports. Review collected by and hosted on G2.com.

We work with high-volume data that involves customer behavior, product interactions, time-based trends and we need to explore patterns to make important decisions. Kyvos allows us to move across multiple dimensions, drill into combinations and test different hypotheses. Review collected by and hosted on G2.com.

At first, it feels like you’re working within a defined structure rather than shaping the data freely for every analysis. Over time, that actually speeds things up, because you are not redoing the same preparation steps again and again for similar use cases. Review collected by and hosted on G2.com.

The way that Kyvos allows our teams to interact with data is incredible in how simple it is to just ask a question in plain and simple terms, without having to create a lot of detailed reports. The ability to ask follow-up questions while still in the same discussion without having to re-create the earlier context is huge and helps us hurry up how long we would spend creating these types of conversations. Review collected by and hosted on G2.com.

I think it would be beneficial for more very specific examples of conversational analytics scenarios done as demonstrations to be available to view. Other than that, the process of using Kyvos has been very smooth. Review collected by and hosted on G2.com.

We were using TM1 earlier and one thing I noticed after moving to Kyvos is how much easier it is to work with larger datasets. With Kyvos semantic layer, reports stay responsive even as data size and complexity increase. Review collected by and hosted on G2.com.

Transitioning from an existing TM1 setup required some planning, especially around how models were structured earlier. Once that was sorted, it has been fairly smooth. Review collected by and hosted on G2.com.