What I like best about GoodData.AI is that I can quickly connect complex data sources and seamlessly create interactive dashboards. The drag-and-drop interface is intuitive and saves me several hours each week on reporting. Review collected by and hosted on G2.com.

What bothers me a bit is the performance with very large datasets. Especially for new users without a technical background, this can be frustrating at first and requires a lot of training time until everything runs smoothly. Review collected by and hosted on G2.com.

The Mosaic and Semantic Layer do a great job of bringing a large volume of Data together as one place to go versus jumping to multiple systems. Junior Analysts will really benefit from the AI Tool Suggestions in building visually appealing and user-friendly dashboards that meet their customers expectations. It's very loud in this room (with the espresso machine) however, the Automated Report Scheduling does seem to be working and has not failed me...yet. Review collected by and hosted on G2.com.

The speed issues in this application can be frustrating when trying to open Cube Data or Add Columns to Reports because it takes a long time to Refresh. We moved from On-Prem to Cloud last year, and trying to resolve some of these "small" items such as Masking Dashboard URLs or Importing Images Next To Links is taking a lot longer than expected. They are also getting rid of Command Manager, which means I need to Learn Python in order to perform things that were much easier on the backend. Review collected by and hosted on G2.com.

GoodData.AI’s biggest advantage, in my view, is its ability to bring key business metrics together in one central, easy-to-access place. As a Data Analyst Manager, I really value being able to build standard reports that can be reused consistently across different clients. The platform also offers intuitive visualization tools along with flexible scripting options, which makes it much easier and faster to locate the specific data I need. In addition, connecting it to our existing systems—such as SAP for automated financial data—has saved our team a significant amount of time that used to be spent on manual reconciliation. On top of that, it includes plenty of ready-to-use configuration options that help streamline our day-to-day work. Review collected by and hosted on G2.com.

Overall, the product is highly flexible and has often exceeded our operational expectations. That said, one clear drawback is that setting up the more advanced dashboards can be quite time-consuming. Because the platform includes such a broad range of features, there’s a modest learning curve when it comes to finding the most effective KPI configurations, often through trial and error. This upfront complexity can sometimes slow down our ability to deliver critical insights to our internal operations and finance teams. Review collected by and hosted on G2.com.

I enjoy the ease of creating good looking, presentable dashboards for clients/management, because I can connect to various data sources, whereas previously I needed an IT person to help. This software has a genuine "drag and drop" report creation feature. The AI generated reports are also nice. It will look at your dataset and provide suggested visualizations automatically. And I like that the data connectors available are fairly comprehensive, as well as being able to hook into a number of data source providers. Review collected by and hosted on G2.com.

The basic functions are okay, but the minute you try to get fancy with custom metrics or data blending you will be either attending webinars or waiting on customer support. A small pet peeve is that the pre-made chart templates don't offer much variety (especially when working with an industry specific template). Also, it takes significantly longer to load datasets and it will literally sit there trying to process the data and you will be sitting there waiting. Pricing also jumps rather dramatically after you move past the base level. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

When something in a dashboard looks wrong, tracing it back to the exact metric definition or data source takes more digging than it should. I've spent time going through several layers of the semantic models just to find where numbers went off. A visual line showing exactly which data source and calculation fits into a specific chart would fix this and cut down the troubleshooting time. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

The thing I'd most like to see improved is performance with large datasets. Around 90% of users say they perform slowly when processing large amounts of data for bigger reports, and they end up forced to export the raw data as a CSV file to use in another application. I hit this directly when one of our enterprise clients asked for a dashboard covering three years of transaction history across multiple product lines. The load time was long enough that the client mentioned it during our review call, which was not the impression we wanted to make. The workaround we currently use is pre-aggregating heavy datasets before they hit GoodData.AI, rather than querying raw data for large date ranges. That has improved load time noticeably, but it adds engineering work that shouldn't be necessary on a platform positioned for enterprise scale. GoodData.AI should prioritize native query optimization for large historical datasets rather than leaving users to architect their own runs outside the platform. Review collected by and hosted on G2.com.

So GoodData has saved me time in that it puts all my information into one place so i don’t have to search through multiple websites for my numbers. After a long day at work i really come back to the dashboard as i am able to view kpi’s without having to create a complex query each time. I’m happy to say that dragging & dropping the chart function works well and did not fail me when i needed to put together a presentation for the executive team last week. The non-technical people on my team are able to utilize this system, which was a major goal of ours. Review collected by and hosted on G2.com.

It becomes slow (noticeably) when trying to process larger than normal datasets which is very annoying. When exporting excel files they always appear unprofessional (sub-totals and grand totals all stacked up) and i would never send an exported file to a client without having to re-format it myself. Also, every single time you make a query you must wrap the column name(s) in quotes which may be a small pet peeve but it is certainly frustrating when doing it over and over again. Review collected by and hosted on G2.com.

What I like most about GoodData is how well it balances flexibility with ease of use. The platform makes it straightforward to build and customize dashboards without needing constant support from our data team, which has saved us a lot of time on ad hoc reporting.Dashboards and reports load quicly even as our data volumes has grown.compared to other enterprise BI plateform,GoodData's pricing has left reasonable for the value we get. Review collected by and hosted on G2.com.

There is not much I'd count as major drawback,but a coule of major things stand out .The learning curve can be a bit steep at first,especally for less technical team members trying to build their own dashboards from scracth. Review collected by and hosted on G2.com.

The drag and drop interface enables users to create sophisticated dashboards easily. GoodData offers some of the best data visualization options that allow stakeholders to engage with data in an exciting way. The cloud infrastructure provides high-speed performance even when dealing with data from a large number of sources. Our customer facing portals are easily integrated with analytics with lots of customizations possible. Review collected by and hosted on G2.com.

The initial setup process is unduly complicated and demands a lot of developer resources. There is little documentation of advanced API functions, and it is continually out of date. Review collected by and hosted on G2.com.

I'm actually happy with the ability to version control metric definitions. It lets codes and changes go through a review before going live, which is great for catching errors before they reach the dashboard instead of after a client is complaining. And with data lineage, it's impressive how it has cut the time to debug questions like 'Why does this number look wrong?' from hours of reading down to just a few clicks. Review collected by and hosted on G2.com.

It's hard since the talent pool is much smaller than using Tableau or Power BI, so onboarding for a new analyst still takes longer than ideal, even with better documentation. The platform's strength in shared, governed metrics makes quick ad hoc exploration feel more cumbersome than tools meant for fast messy analysis. Review collected by and hosted on G2.com.