Dashboard Software

It's a breeze to set up and works well with our current infrastructure, SQL databases, cloud data warehouses like Snowflake and BigQuery, and even SaaS apps like Salesforce. Review collected by and hosted on G2.com.

Tableau runs on a scalable, n-tier client-server architecture, which includes Tableau Desktop, Server, and Cloud. Its unique VizQL Server converts drag-and-drop operations into a visual query language, and produces interactive visualisations on the fly. The Hyper Data Engine is another high-performance in-memory data engine that further optimises data processing, whether it's live from a variety of data sources or by using in-memory data extracts for speed. Tableau Pulse gives us insights and anomaly alerts that are tailored to the individual, and Einstein Discovery introduces real predictive modelling to our toolbox, making these more than just add-on features. Review collected by and hosted on G2.com.

While Power BI is a great tool, the learning curve for advanced DAX can be really frustrating. Basic measures are fine, but when you need complex time-intelligence or nested filtering, DAX feels way less intuitive than just writing SQL or Python. Debugging those heavy DAX queries takes up too much time, mostly because the error messages are usually pretty vague.

My biggest headache, though, is version control and team collaboration. Coming from a data engineering background where Git integration and CI/CD pipelines are seamless, dealing with standard .pbix files feels outdated. You cannot easily have multiple developers working on the same dashboard without accidentally overwriting someone's work. Microsoft has rolled out newer developer formats to help with this, but the whole deployment process still feels clunky compared to modern coding environments.

Also, the fact that Power BI Desktop is still strictly a Windows application is a constant hassle. Teammates on Macs have to jump through hoops with virtual machines just to build a report. Bringing the web browser authoring experience up to par with the desktop app would make a world of difference. Review collected by and hosted on G2.com.

Before adopting Power BI, our reporting workflow was weighed down by fragmented data sources and static Excel sheets. Every executive request meant manually running SQL queries, exporting CSVs, and stitching multiple spreadsheets together. The result was major bottlenecks, stale data, and frequent version-control issues across departments.

Implementing Power BI completely changed how we manage reporting. By connecting Power BI directly to our centralized cloud lakehouse and configuring automated scheduled refreshes, we removed the need for manual exports. Our reporting cycles used to be slow and disjointed, often taking days to compile; now we can deliver real-time, interactive dashboards to teams across the organization.

The business impact has been immediate and tangible. Our engineering and analytics teams save roughly 10 to 12 hours each week that previously went to manual report upkeep and ad-hoc requests. Executive decision-making is noticeably faster because leaders can self-serve the metrics they need without waiting for our team to run new queries. Overall, it has streamlined our data delivery pipeline end to end. Review collected by and hosted on G2.com.

One downside is that troubleshooting can sometimes be difficult when a pipeline fails, as the error messages aren't always detailed enough and you may need to dig through multiple logs to find the root cause. The platform also has a lot of features, so it can take some time for new users to become comfortable with everything. While serverless is convenient, costs can increase if compute usage isn't monitored properly. I'd also like to see faster UI responsiveness in some areas, especially when navigating large job histories or catalogs. Review collected by and hosted on G2.com.

Databricks has helped me and my team simplify our data engineering workflow by providing a single platform for data ingestion, transformation, governance, and analytics. We use it to process data from Bronze to Silver and Gold layers, which has made our ETL pipelines more reliable and easier to manage. Features like serverless compute, Unity Catalog, and built-in monitoring have reduced operational effort, improved collaboration across teams, and helped us deliver data products faster. 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.

Our biggest challenge was working with large volumes of data without slowing down analysis. Queries used to get expensive and time-consuming, especially when multiple people were exploring data at the same time.

With Kyvos, we’re able to interact with data much more freely. It’s easier to explore trends, compare performance, and answer follow-up questions without worrying about query delays. Review collected by and hosted on G2.com.

One aspect I find challenging about SAS Viya is its tendency to be resource-intensive, often demanding substantial CPU, memory, and storage to operate efficiently, particularly in larger environments. The process of installation and upgrading can also be quite complicated, as it typically involves coordinating multiple services and dependencies. Furthermore, troubleshooting can be a lengthy process, since it sometimes requires in-depth knowledge of both SAS components and the underlying infrastructure. I also think that some users might perceive the interface and integration with third-party tools as less intuitive when compared to more modern, lightweight analytics platforms. Review collected by and hosted on G2.com.

SAS Viya is solving the problem of managing, analyzing, and visualizing large volumes of data efficiently across different environments. It provides a unified platform for data integration, advanced analytics, and machine learning, which helps streamline workflows and reduce the complexity of moving between tools. By enabling collaboration between data scientists, analysts, and business users, SAS Viya improves productivity and decision-making. Its scalability and ability to run on cloud or on-premises infrastructure also allow organizations to handle growing data demands with flexibility and performance. Review collected by and hosted on G2.com.

One thing I don’t like about Alteryx is that it can be expensive, especially for smaller teams. It also takes time to learn all of its more advanced features, and when workflows get large, they can sometimes become difficult to manage. Aside from those drawbacks, it has been a reliable tool for data preparation and analytics. Review collected by and hosted on G2.com.

Alteryx helps me solve the problem of manual, time-consuming data preparation. It makes it straightforward to clean, combine, and transform data from different sources all in one place. This saves me a lot of time, reduces errors, and lets me get to the analysis much faster. As a result, I can spend more time finding insights instead of spending hours just preparing the data. Review collected by and hosted on G2.com.

The biggest drawback is performance with large-scale analyses. While Hex handles most workflows well, resource-intensive notebooks and large queries can occasionally lead to slower execution and require optimization. Some advanced collaboration, governance, and enterprise features are only available on higher-tier plans. Customizing highly interactive dashboards is not as flexible as in dedicated BI tools. Review collected by and hosted on G2.com.

Hex solves the challenge of bringing data exploration, analysis, visualization, and collaboration into a single platform. Instead of switching between SQL editors, Jupyter notebooks, BI tools, and presentation software, Hex provides one workspace where technical and business users can work together. Combines SQL, Python, visualizations, and dashboards in one environment. Simplifies collaboration by allowing multiple team members to work on the same project. Makes it easy to create interactive reports that stakeholders can explore without technical knowledge. Connects directly to cloud data warehouses and databases for real-time analysis. Reduces the effort required to move from raw data to shareable business insights. In my workflow, Hex helps streamline data analysis and reporting by keeping everything in one place. I can query data, perform Python-based analysis, build visualizations, and share interactive dashboards without exporting results across multiple tools. The biggest benefit is improved collaboration and faster decision-making. Hex shortens the analytics workflow, making it easier to deliver insights, collaborate across teams, and turn data into actionable outcomes more efficiently. 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.

We are using it to bring many disparate data sources together into one Governed Platform instead of having to deal with Large Excel Downloads. In essence, it provides us with a Single Source Of Truth for Reporting, so everyone sees the same Numbers when making Decisions. Review collected by and hosted on G2.com.

Some corners of the UI/UX still have room to improve. A few of the admin and settings screens feel denser than they need to be, and there’s a learning curve in places. That said, they’ve taken big, visible steps over the last year—a refreshed dashboard UI, themes, and a much cleaner AI chat experience—and the trajectory is clearly moving in the right direction.

With AI, you still need to double-check the results, since it can sometimes get things wrong. However, they make it easy to validate during conversations that the AI has computed the right results. Review collected by and hosted on G2.com.

We moved to Knowi from a well-known cloud BI platform. The previous tool required pushing all of our data into its own store before we could visualize anything, which meant ETL pipelines to maintain, sync lag, duplicate storage costs, and our NoSQL data getting flattened along the way.

With Knowi, we unified analytics across all of our datastores without moving the data and launched customer-facing embedded dashboards much faster. Row-level security and secure embeds ensure each customer sees only their own data. The semantic layer keeps metrics consistent across every surface. And with the new AI and agentic capabilities embedded in our product, customers can self-serve answers that used to come in as support tickets and custom report requests, so our team can spend its time on the product rather than on one-off reporting. Review collected by and hosted on G2.com.

Honestly I don't see any so far: If at all, it requires relatively high technical skill. It cannot be used effectively by a "plug-n-play" BI Analyst.

If the price is an issue, one can consider self-hosting. Review collected by and hosted on G2.com.

I started off liking Tableau as a BI tool better than PowerBI.

Then, I could see the compute downsides and missing git repo in Tableau compared to Looker.

Then, I can see the issue that Looker is very expensive and that Looker is not dbt integrated, i.e. you constantly need to maintain two disconnected repos and make changes in two places.

Lightdash is by far the best BI tool out there today. Review collected by and hosted on G2.com.

Looker can feel complex for new users and takes time to learn. It could improve with simpler setup steps and more beginner-friendly guidance. Review collected by and hosted on G2.com.

I use Looker to analyze data and create reports. It helps me turn raw data into clear insights quickly, automate dashboards and queries, and make faster, better decisions with real-time insights. Review collected by and hosted on G2.com.

local data agent doesen't necessarily supoort the lateset JRE builds Review collected by and hosted on G2.com.

It helps us create a single source of truth for all of our disparate information sources. Review collected by and hosted on G2.com.

One thing I have noticed is that when many people execute Jaspersoft reports at the same time, especially those that take historical data, it can lead to out of memory issues. Review collected by and hosted on G2.com.

I use Jaspersoft for centralized reporting, solving our reporting mechanism issues. I like the Domain feature, allowing clients to create reports without developer help, easing our workload. Review collected by and hosted on G2.com.

Gets a bit laggy when workbooks have a lot going on lots of elements, big datasets, that kind of thing. Some of the advanced features aren't super intuitive at first, you kind of have to figure them out on your own. Not a dealbreaker but could be smoother. Review collected by and hosted on G2.com.

We were spending way too much time building one-off reports for people. Now with Sigma, teams can build their own dashboards and trackers we use it for migration tracking, client project reporting, and internal operations tracking. The Snowflake connection was easy to set up and stays reliable, so everyone has access to real-time data whenever they need it. People don't need to come to us for every little thing anymore, saves a ton of time. Review collected by and hosted on G2.com.

The biggest drawback is performance on resource-intensive analyses. While Count works well for most day-to-day analytics tasks, large queries and complex notebooks may require optimization to maintain a smooth experience. Dashboard customization options are not as extensive as those offered by dedicated business intelligence tools. Review collected by and hosted on G2.com.

Count solves the problem of fragmented data analysis workflows by bringing SQL, data exploration, visualization, and collaboration into a single workspace. Instead of using separate tools for querying data, creating dashboards, and sharing insights, Count enables teams to do everything in one collaborative environment. Enables collaborative data analysis through shared, live notebooks. Combines SQL, visualizations, and documentation in a single workspace. Creates interactive reports that update automatically as data changes. Makes it easier to share insights with both technical and non-technical stakeholders. Reduces context switching between data querying, reporting, and collaboration tools. In my workflow, Count helps streamline exploratory data analysis and reporting. I can query data, build visualizations, document findings, and share interactive reports with teammates without exporting data or moving between multiple platforms. The biggest benefit is better collaboration and faster insights. Count shortens the time from raw data to actionable reports, helping teams work together more effectively and make data-driven decisions with greater confidence. Review collected by and hosted on G2.com.

The design side has a few limitations. The basic charts and cards look clean, but getting everything to align with our exact brand guidelines or achieving tighter control over layouts, can be frustrating. I often end up making tweaks in Figma and then going back to adjust things again in Domo. Also, once you have a lot of DataFlows and datasets (we’re over 70 now), the workspace can start to feel cluttered unless you’re very disciplined about naming conventions and archiving. Review collected by and hosted on G2.com.

It’s really helped bridge the gap between the design and data teams. I no longer feel like I’m constantly waiting on someone else to make the data usable, and that’s made my design iterations faster and let me work more independently. Review collected by and hosted on G2.com.

Very little at this point. We experienced some initial performance challenges with dataset linking at large scale, but the Luzmo team responded proactively with a comprehensive solution in development (expected Q1 2026). The communication and support throughout has been excellent. Review collected by and hosted on G2.com.

Luzmo solves the challenge of providing embedded, white-labeled analytics dashboards for our multi-tenant SaaS platform serving enterprise supply chain clients. Rather than building a custom BI solution from scratch, Luzmo provides production-ready embedded dashboards that integrate seamlessly into our application while maintaining strict data isolation between clients through account overrides.

The multi-tenancy architecture allows us to deploy the same dashboard templates across multiple enterprise customers while ensuring each client sees only their data. This significantly accelerates our time-to-market for new client onboarding and reduces development overhead.

Luzmo enables us to deliver sophisticated forecast analytics visualizations—including complex metrics like wMAPE, Bias, and FVA calculations—without requiring our data engineering team to build and maintain a full front-end visualization layer. The Databricks connectivity allows us to leverage our existing data infrastructure while providing interactive dashboards.

The Luzmo IQ natural language querying feature adds significant value by allowing non-technical users to ask questions about their forecast performance data conversationally, reducing the need for custom reports and training.

Overall, Luzmo allows our small team to deliver enterprise-grade embedded analytics to multiple Fortune 500 clients without the engineering resources required to build a proprietary BI platform. Their team has been highly responsive to our scaling needs, with a new Databricks connector in development to further improve performance. Review collected by and hosted on G2.com.

Only constructive feedback I have is I wish there was a way to have data update immediately, but I don't foresee a workaround since this platform pulls data from outside software, and response times between the two will never be fully succinc - just the nature of the beast. Review collected by and hosted on G2.com.

Reporting, data analytics, real-time audit of employee budget, time entries, etc. Review collected by and hosted on G2.com.

Yellowfin BI has a performance lag when there is complex data sets large data structures

Some customization and advanced analytics configuration needs some technical knowledge and facilitation Review collected by and hosted on G2.com.

Yellowfin BI supports companies in turning raw data to actionable and understandable dashboards

The program eliminates the manual spreadsheets and reporting, offering automated and accurate reporting mechanisms

The program foster embedded analytics more so on customer portals and SaaS products

Yellowfin is brilliant in fostering collaboration, more so through shared information, comments and this ensures team can analyze and discuss reports together

The program is open in connecting with multiple data sources, and this supports centralized analytics

The app gives non technical business users the ability to learn, interpret and understand different reports and trends

The insights generation and reports are automated and this saves on time Review collected by and hosted on G2.com.

I haven't ran into an issue with the system as of yet. All seems to be working fine. Review collected by and hosted on G2.com.

It centralizes all of our analytical reports, which in turn makes it easier for review. Review collected by and hosted on G2.com.