Microsoft Power BI is a visualization and data discovery tool from Microsoft. It allows users to convert data into visuals and graphics, visually explore and analyze data, collaborate on interactive dashboards and reports, and scale across their organization with built-in governance and security.
$10
per month per user
Posit
Score 10.0 out of 10
N/A
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.
Even though Power BI is not the only tool we use, is the most powerful one! Its easiness to integrate information and directly import queries makes it super easy to set up and start using it. Also, it's much faster handling a lot of data compared to other products like microstra…
[Microsoft Power BI] is extremely easy to set up even without any technical background. That makes fast delivery of dashboards possible. Furthermore, the graphs and charts available are customizable to the customers needs and are very easy to set up. Finally, [Microsoft Power …
There are loads of people in the BI (Business Intelligence) space, of course... but I wouldn't touch any of them because none of them offer anything like the R and Python support that RStudio does. RStudio publishes open-source, they're a public benefit corporation, and they …
RStudio is great tool with cross language integration with minimal changes. RStudio support team is very prompt and informative. Simple installation setup.
RStudio offers less out-of-the-box point and click solutions than other products, but it allows for custom solution development and its integration with the Shiny package in particular allows for the custom development of point and click solutions.
Power BI is an easy to build visualization tool widely used in our organization to make data accessible to non-data people. This is really great tool when we want to create a dashboard for trends and track some metrics. But it become very difficult when we want to enable high …
Compared to Power BI, it is easier to create custom analysis and visualizations in RStudio. In terms of enterprise integration, it seems so far that Power BI is easier because of its tighter integration with our Microsoft suite of tools and services.
Python is a major competitor, but Python is lacking when it comes to statistical modeling. Also, people with less programming experience can use RStudio better than Python.
Has significantly improved collation of data and visualisation especially with business across Europe. Has given me the ability to see the Site availability at the click of a button to see which Site is in the "money" and seize opportunities based on Market data
In my humble opinion, if you are working on something related to Statistics, RStudio is your go-to tool. But if you are looking for something in Machine Learning, look out for Python. The beauty is that there are packages now by which you can write Python/SQL in R. Cross-platform functionality like such makes RStudio way ahead of its competition. A couple of chinks in RStudio armor are very small and can be considered as nagging just for the sake of argument. Other than completely based on programming language, I couldn't find significant drawbacks to using RStudio. It is one of the best free software available in the market at present.
Options for data source connections are immense. Not just which sources, but your options for *how* the data is brought in.
Constant updates (this is both good and bad at times).
User friendliness. I can get the data connections set up and draft some quick visuals, then release to the target audience and let them expand on it how they want to.
The support is incredibly professional and helpful, and they often go out of their way to help me when something doesn't work.
The one-click publishing from RStudio Connect is absolutely amazing, and I really like the way that it deploys your exact package versions, because otherwise, you can get in a terrible mess.
Python doesn't feel quite as native as R at the moment but I have definitely deployed stuff in R and Python that works beautifully which is really nice indeed.
Python integration is newer and still can be rough, especially with when using virtual environments.
RStudio Connect pricing feels very department focused, not quite an enterprise perspective.
Some of the RStudio packages don't follow conventional development guidelines (API breaking changes with minor version numbers) which can make supporting larger projects over longer timeframes difficult.
Microsoft Power BI is an excellent and scalable tool. It has a learning curve, but once you get past that, the sky is the limit and you can build from the most simple to the most complex dashboards. I have built everything from simple reports with only a few data points to complex reports with many pages and advanced filtering.
There is no viable alternative right now. The toolset is good and the functionality is increasing with every release. It is backed by regular releases and ongoing development by the RStudio team. There is good engagement with RStudio directly when support is required. Also there's a strong and growing community of developers who provide additional support and sample code.
Automating reporting has reduced manual data processing by 50-70%, freeing up analysts for higher-value tasks. A finance team that previously spent 20+ hours per week on Excel-based reports now does it in minutes with Microsoft Power BI's automated Real-time dashboards have shortened decision cycles by 30-40%, enabling leadership to react quickly to sales trends, operational bottlenecks, and customer behavior.
For someone who learns how to use the software and picks up on the "language" of R, it's very easy to use. For beginners, it can be hard and might require a course, as well as the appropriate statistical training to understand what packages to use and when
RStudio is very available and cheap to use. It needs to be updated every once in a while, but the updates tend to be quick and they do not hinder my ability to make progress. I have not experienced any RStudio outages, and I have used the application quite a bit for a variety of statistical analyses
It is a fantastic tool, you can do almost everything related with data and reports, it is a perfect substitutive of Power Point and Excel with a high evolution and flexibility, and also it is very friendly and easy to share. I think all companies should have Power BI (or other BI tool) in their software package and if they are in the MS Suite, for sure Power BI should be the one due to all the benefits of the MS ecosystem.
Since R is trendy among statisticians, you can find lots of help from the data science/ stats communities. If you need help with anything related to RStudio or R, google it or search on StackOverflow, you might easily find the solution that you are looking for.
Microsoft Power BI is free. If I didn't want to create a custom platform (i.e. my organization insisted on an existing platform that I *had* to use), I'd use Microsoft Power BI. For any start-up or SMB, I'd just use Claude & Grok to build it quickly, also for free. Would not pay for Tableau or Sigma anymore. Not worth it at all.
RStudio was provided as the most customizable. It was also strictly the most feature-rich as far as enabling our organization to script, run, and make use of R open-source packages in our data analysis workstreams. It also provided some support for python, which was useful when we had R heavy code with some python threaded in. Overall we picked Rstudio for the features it provided for our data analysis needs and the ability to interface with our existing resources.
RStudio is very scalable as a product. The issue I have is that it doesn't necessarily fit in nicely with the mainly Microsoft environment that everybody else is using. Having RStudio for us means dedicated servers and recruiting staff who know how to manage the environment. This isn't a fault of the product at all, it's just part of the data science landscape that we all have to put up with. Having said that RStudio is absolutely great for running on low spec servers and there are loads of options to handle concurrency, memory use, etc.
Using it for data science in a very big and old company, the most positive impact, from my point of view, has been the ability of spreading data culture across the group. Shortening the path from data to value.
Still it's hard to quantify economic benefits, we are struggling and it's a great point of attention, since splitting out the contribution of the single aspects of a project (and getting the RStudio pie) is complicated.
What is sure is that, in the long run, RStudio is boosting productivity and making the process in which is embedded more efficient (cost reduction).