GoodData has always been incredibly useful to me. I love being able to access information and create dashboards, which is a huge help—especially for decision-making. Since we implemented it, I’ve felt that the platform has really solid logic for working with metrics and the suggested results. That’s been especially useful, and it’s what I’ve liked most about it so far. I hope it continues to do well. I’d recommend this type of solution to anyone because it’s working well, at least for now.

I love the workspace label access control feature the most because it isolates every customer data by design, automatically enforcing permissions based on who is logged in, eliminating the chance of the wrong customer seeing someone else's dashboard. The anomaly detection is incredibly valuable, helping us detect issues in customer usage data before the customer even notices, enabling us to proactively reach out and address potential problems before frustration sets in. I also appreciate how our analytical assets, such as data models and metric definitions, are managed with version control just like our application code, allowing us to see exactly what changes have been made and roll back if necessary.

What I like most about GoodData.AI is the tools that govern the semantic layer. Those features have delivered the most measurable impact for how we operate at scale. The flexible dashboards and robust data integration allow for seamless visualization and analysis of large datasets from multiple sources. Beyond that, defining our key metrics once in the semantic layer and having every client dashboard inherit those definitions automatically has been the single biggest time saver since we went live. Before GoodData.AI, we had client-facing reports recalculating metrics independently, and discrepancies between what a client saw and what our internal team saw came up roughly once a month on average, always at the worst possible moment during a client review call. Since deploying the semantic layer, those discrepancies have dropped to zero in the eight months since we went live, because there is now only one version of every metric definition and everything pulls from it. We save around ten hours per week after using it. It also lets our engineering team focus on the core platform rather than maintaining a reporting layer, and powers everything client-facing without us building an analytics engine from scratch internally.