Things that is the best is the drill down features on the dashboard of GoodData.AI. It's something our team genuinely uses in day-to-day work. We can start from high-level numbers and keep clicking deeper into the data without needing to build a separate report for each level. If our region's numbers look off, we can drill straight into the specifics, like which reps, product, or time period contributed to that. This feature is really valuable for us because it makes things clear, easy to search, and work with. Review collected by and hosted on G2.com.

We had some problem with slowdowns noticeably when working with large datasets. For smaller reports, it's fine, but when pulling a heavy report, you end up waiting longer than you should. A few times, we had to export raw data to another tool just to get the analysis done faster, which defeats the purpose a bit. The actual connection to our data source is not the hard part, but getting our data model and metrics defined correctly takes time so that everything makes sense across the platform. If you skip that step or rush it, the reports start giving inconsistent results, and tracking down the source of the problem takes forever. It takes at least three or four weeks before your team is fully comfortable using it as a day-to-day tool. Review collected by and hosted on G2.com.

I really enjoy using GoodData.AI's semantic layer because it sets it apart from simpler BI tools. You define metrics and business logic once, and every dashboard across every client environment pulls from that same definition, ensuring consistency. This is especially important when dealing with multiple client accounts at scale, as no team calculates revenue differently from another. The AI smart search feature has been surprisingly useful; our internal analyst can type questions in plain language and pull up the right dashboard without having to hunt through folders. I also appreciate the well-thought-out multi-tenancy setup. Each client gets their own isolated environment, but we manage everything from a single backend, which keeps operational overhead manageable. Review collected by and hosted on G2.com.

The initial learning curve is steep, especially if your team isn't already comfortable with the modeling concept. Getting the semantic layer set up correctly on the first try takes a real effort, and rushing it can lead to issues with metrics definitions later. Pricing also isn't the friendliest for smaller teams; it's clearly designed for mid-sized ones. Honestly, the setup takes time. The API-first architecture is flexible, but that flexibility means making more decisions upfront. Plan for a proper implementation phase, not a quick onboarding. Our dev team spent a few weeks getting the data models right and connecting our data warehouse before anything was visible. Once the groundwork was done, rolling out new dashboards became faster. It's not a platform you hand to a nontechnical person and ask them to configure from scratch. Review collected by and hosted on G2.com.

It is really impressive that the analytics code workflow with VS Code and GitHub integration; it's been incredible and valuable for us. We treat our reporting structure like actual software code, allowing our development team to write data models, use version control through GitHub, and deploy dashboard updates across hundreds of separate client workspaces simultaneously using automated scripts instead of manually clicking around a visual interface. Review collected by and hosted on G2.com.

What the app needs is improvement in how it handles changes in the database. If a database engineer renames or drops a data column in our underlying cloud data warehouse, the platform instantly deletes any downstream metric formulas or chats connected to that field, rather than flagging them with a broken warning status. They need to add an alert state that lets us manually remap change fields instead of completely deleting our work. Review collected by and hosted on G2.com.

I really happy with my experience using the AI-powered flex query assistant in GoodData.AI because it's a standout feature. It has simplified my work, allowing nontechnical stakeholders to build their own complex data visualizations using natural language prompts. This significantly reduces the number of report requests hitting our data team's queue, giving me more free time. It's also really reliable for our business to sustain. Additionally, our analytics integration was a highlight during setup, as we can manage our entire dashboard using the same version control and CICD pipelines as our main software product. This makes the initial rollout predictable, auditable, and easy to roll back if needed. Review collected by and hosted on G2.com.

Some technical improvement that I think they need to do is the SQL based data modeling interface. It can feel a bit complex for rapid prototyping. I would love to see more drag and drop sandbox environment for testing new data joints before committing them to the formal semantic layer. This would speed up our initial exploration pace. Review collected by and hosted on G2.com.

The actual reason we stayed using GoodData was because of the Drag and Drop Workflow Builder; you plug in tools (like Legos) onto a canvas and even non-coding personnel can build and run their own workflows without bothering Engineering personnel. I had some non-technical personnel train on it in under a couple of weeks and they were able to do ETL Jobs independently which really surprised me. Review collected by and hosted on G2.com.

Performance slows down significantly when dealing with very large datasets, because it loads into memory instead of processing in-place, therefore, if you do not have sufficient hardware, you will notice it. There is no version control built-in, which in today's world, is frankly unbelievable; Collaborating on Workflows feels like it is stuck in 2012. Minor pet peeve, but annoying nonetheless: it does not allow for Notepad or Word Files to be used as input; you need to reformat the file prior to even starting. Review collected by and hosted on G2.com.

I am Happy howGoodData.AI has automated my weeks. The automated Slack and email digest engine makes everything clear and easy. Instead of forcing executives to log into another platform, I can set up a schedule that automatically broadcasts targeted high-level performance snapshots into specific team Slack channels every Monday morning, so I don't have to do it manually. Everything is launched automatically by the software. Review collected by and hosted on G2.com.

I think they have a design capability limitation because the chat widget styling facility is somehow quite restrictive. While the default chat looks clean, trying to modify specific access levels and apply custom room gradients or adjust padding for unique display is unnecessarily locked down compared to the legacy visualization. Review collected by and hosted on G2.com.

I love the wide range of connectors—it really simplifies the process and lets us reconcile data across platforms. It also gives greater visibility into key data and metrics, and I find it incredibly intuitive and easy to use. On top of that, I really appreciate Domo’s ability to automate processes. Review collected by and hosted on G2.com.

Some of the training resources feel outdated at times, and it can be a bit complicated to figure out how to create dashboards. I also think a few of the visualization features could be improved to make them easier to use. Review collected by and hosted on G2.com.

This platform lets us view all data analyses in one place, and the database is well structured and easy to understand. It also offers a wide range of visualization options, so we can take advantage of many useful GoodData features. Review collected by and hosted on G2.com.

There should be a better way to view connections, since some of the more advanced BI capabilities don’t feel very robust. The educational resources, like tutorials, could also be improved to make learning easier. Overall, there’s still room for stronger visualization options and a more polished experience in these areas. Review collected by and hosted on G2.com.

What I love is it enhances my communication of results throughout the company. I can also deliver formatted dashboards pointing out major points, rather than using spreadsheets. I can customize what I see to different teams by the number of features which makes things relevant. This has enabled my work to be more effective, as individuals will be more dependent on the data that I present. Review collected by and hosted on G2.com.

One negative thing I have observed is that, developing more detailed dashboards in GoodData may be time consuming. Some rigs are more demanding and can be difficult to make quick adjustments. This impacts my job whenever I am required to act quickly to business demands. It may also cause delays as multiple requests are processed simultaneously. Review collected by and hosted on G2.com.

The strength of GoodData is in its strong data integration features, which enables us to easily get data of different sources. I find its built-in analytics especially appealing; we can add our own dashboards to our apps and have end-users access data without leaving our platform. This offers a customized, branded experience to our clients, which is priceless to their self-service requirements. Review collected by and hosted on G2.com.

The first installation needs a lot of technical skills. Report generation can be a slow process with very big datasets sometimes which can affect on instant decisions. The user interface, though practical, is not necessarily the most user-friendly one to non-technical members of the team. Review collected by and hosted on G2.com.