Which data analysis software offers the most comprehensive data visualization features?

Esta resposta pode ser um tanto quanto desafiadora, pois a maioria das ferramentas de analises fornecem os recursos essenciais aqui sugeridos. Segundo a Gartner, em 2023 os lideres foram as plataformas Microsoft (Power BI), SalesForce (Tableau) e Qlik. No entanto n?o diria que essas s?o as únicas ferramentas que possibilitam a abrangência de recursos. Temos ferramentas open source, como Matplotlib e Superset, que nos possibilitam quase ou nenhuma limita??o para personalizar os elementos dos objetos de análises. diferente das ferramentas das plataformas que lideram o ranking supracitado.

So it depends on the size of data you're managing, PowerBi gives you good visualization if you do not have a very large data, I also like that it's Microsoft enabled and works well with data collection apps, finally using Google spread sheet, you can manage dynamic data very well. For Tablue I think it does well with large data but the down side of it is that it hardly links to many data collection app..

Tableau ?? is renowned for its user-friendly interface and diverse visualization options, empowering users to create interactive dashboards and compelling data stories. Microsoft Power BI ?? offers robust visualization features, especially beneficial for organizations already utilizing Microsoft products. Google Data Studio ?? is cloud-based, enabling seamless integration with other Google services and collaborative dashboard creation. Python libraries like Matplotlib, Seaborn, and Plotly ?? provide extensive customization options for those comfortable with programming.

Tableau and Power BI both have good options for making charts and graphs. I think Tableau is a bit better because it's easier to use and customize. It facilitates storytelling with data in an easier way, allowing users to emphasize critical data points and annotations with greater ease. The downside of Tableau is that it's more expensive and takes longer to learn. Therefore, if an organization's primary needs are straightforward data exports and report generation with fewer visual demands and a tighter budget, Power BI is an excellent choice. But, for those looking to delve deeper into data storytelling and extract nuanced insights, Tableau is a superior tool despite its higher cost.

1. Tableau has flexible filtering 2. Tableau filtering function can be used across Dashboard. 3. Helps in drill down into more granular data and it helps to identify the meaningful insights out of Raw Data. 4. Power BI is also helpful handy but the presentation is not so attractive compare to Tableau 5. Power BI is not so intuitive as Tableau 6. However Power BI provides robust integration capabilities, allowing users to easily import data from a wide range of sources such as databases, cloud services, files, and online platforms. 7. Tableau makes it easy to explore complex datasets. 8. Tableau user-friendly interface and diverse visualization options, empowering users to create interactive dashboards

Power BI has a large library of standard visuals. Which offer a wide variety of flexibility when it comes to customizing your visuals. Their even redesigning their standard visuals one by one at the moment, so the future looks bright. But besides this their is also a big marketplace with visuals build by the community. These can be either free or with a cost. But on top of that their are still other ways in which you can built the visual exactly how you want. - Python or R visuals allow you to write you code how you want and use the libraries you're familiar with. - Deneb allows us to create or own visuals using Vega or Vega Lite. The sky is literally the limit for creating custom visuals with Power BI.

I completely agree with the statement. Furthermore, we have more and more tools that require less and less complex code, such as Power BI and Tableau. Both are excellent data visualization tools with a huge range of customizations. Furthermore, on a smaller scale, in my opinion, we have Looker Studio, which, in my opinion, is easier and more intuitive to use than those mentioned previously but not as robust.

Tableau: Intuitive Visual Analytics: Tableau's drag-and-drop interface allows users to easily create complex visualizations without requiring extensive technical expertise. Data Blending: Tableau's data blending capabilities allow users to seamlessly combine data from multiple sources, including spreadsheets, databases, cloud-based applications, and more. Real-time Updates: Tableau's live connection to data sources enables visualizations to update in real time. Advanced Analytics Integration: Tableau seamlessly integrates predictive analytics, statistical modeling, and machine learning. Robust Mapping and Spatial Analytics: Tableau's extensive mapping and geographic visualization features enable users to analyze in compelling ways.

I agree, more customisation options and less enablement efforts to Enable the customisation, be it templatisation of reports can be used across organisation.

Tableau allows users to drill down into more granular data and is very intuitive for filtering and examining different layers of the data. Tableau also has flexible filtering, that can be applied across multiple views and dashboards, making it easy to explore complex datasets. Power BI has the option to sync filters across reports, and create custom filters with DAX formulas. However, I haven't found it to be as intuitive as Tableau, especially if data is connected to multiple datasets.

I recently used Power BI in a new way—instead of just using it for data analysis, I turned it into a powerful search and report application. Inspired by popular search engines, I aimed to provide a similar, easy-to-use search experience right within our organization. To make things even more user-friendly, I set up standard pages where our team can quickly get insights and generate aggregated excel reports. I also automated some of the routine tasks with Power Automate. The result is a dashboard that not only does its usual job but also acts like a fully interactive software application. It’s as easy to use as any commercial software, but it’s tailored just for our needs.

A lot of data analysis and data visualization happens on jupyter notebook. But lot of people don't know about Jupyter Widgets. Jupyter widgets allow you to build interactive elements through which you can control visualization within Jupyter.

Major of these tools, tableau, powerBI, qliksense, MSTR are into the self serve mode where a business user start using the reports and allowing them to access to the data and do the further changes to the existing report

Tableau excels in creating interactive visualizations and user-friendly dashboards, ideal for users needing to delve into complex data and share insights organization-wide. Microsoft Power BI integrates seamlessly with Microsoft products and provides cost-effective solutions for real-time dashboard updates, perfect for Microsoft-centric businesses. Matplotlib, a Python library, offers extensive customization for detailed, static visualizations, making it the choice for researchers and data scientists who require precision and academic rigor in their visual outputs. Each tool fits different aspects of data visualization, catering to various professional needs and expertise levels.

If you're heavily invested in Microsoft products, Power BI would be better. Its data integration features are stronger when used within the Microsoft ecosystem (like Azure, SQL Server, and Excel). Power BI provides data modeling capabilities and deep integration with other Microsoft products, which can be an advantage if your organization heavily uses that environment. Tableau, however, is better in my opinion, because it allows broader connectivity with various data sources outside the Microsoft ecosystem. It's excellent at handling different types of data from various sources simultaneously ( Big Data platforms, Cloud platforms like AWS, APIs from Google Analytics, Meta Ads, etc.)

Tableau is a clear winner in terms of connectivity to diverse data sources. And the best part is the list of connectors are growing every year (or perhaps every quarter) .

Power BI provides robust integration capabilities, allowing users to easily import data from a wide range of sources such as databases, cloud services, files, and online platforms. This integration extends to popular applications like Excel, SharePoint, Dynamics 365, and many others, enabling users to leverage their existing data investments.

Qlik Sense is an analytics platform that allows business users to explore data. It is known for its advanced analytics capabilities and its ability to be deployed on-premise or in the cloud. Qlik Sense describes itself as a tool that helps users find insights and make quicker decisions. The ease of managing connections to various data sources and flexible embedding is also the unique selling point of this tool. Google’s Looker is a unified self-service analytics platform that allows business users to create dashboards and reports from various data sources, build embedded applications, and streamline workflows with real-time insights.

Honestly since using Power BI; I’ve found it is truly seamless integration like another user has stated. I’ve easily compiled information from Excel spreadsheets to even website tables to build comprehensive solutions for users. Not saying Tableau doesn’t have these capabilities. I’ve found Microsoft to be more easy to use for the end users.

When handling a large volume of data, it can be challenging in PowerBI, especially when your data doesn't quite fit into a 'star-schema'. A lot of steps have to be done in SQL, prior to loading data into PowerBI in order to have better performance. If your data contains free- texts, it can also become quite heavy due to how PowerBI handles Cardinality/Distinct values. While PowerBI has its own strengths, when it comes to scalability, Tableau seems stronger in this area in comparison.

Comparing Tableau and PowerBI, based on my experience, Tableau excels in managing larger volumes of data giving you a 360-degree view of your data. It is fast and provides extensive features for visualizing large data sets without requiring cloud services. By leveraging Tableau's scalability features, you can maintain smooth operation and ensure optimal performance even with extensive datasets, empowering you to navigate complex visualizations effortlessly. In contrast, PowerBI is known for its efficiency in working with smaller datasets. Also, offers a streamlined experience for smaller-scale projects. With this info, you can choose the best fit for your specific data visualization needs. Agree?

Taking into account that every day we have more access to large-scale data, having robust software that does not suffer from slowdowns and has integration with tools such as, for example, BigQuery and Databricks is important so that we can have massive data in a more fluid way within a dataviz tool. I currently work with BigQuery connected to Power BI, and it works perfectly for large volumes of data. However, if the data is really massive, an alternative is to create a python notebook within BigQuery itself, but this alternative has a cost. If you have a large volume of data and need large amounts of processing, if your GCP admin can provide a json key to connect BigQuery to your Jupyter notebook, it is an excellent alternative.

People choose to use Tableau over Microsoft Power BI for its flexibility and customization, intuitive user experience, advanced analytics integration, data blending capabilities, broad data connectivity, and enterprise-level scalability. Tableau's emphasis on flexibility, customization, advanced analytics, and broad data connectivity make it a compelling choice for organizations seeking a comprehensive and user-friendly data visualization and analysis platform, especially for more complex or specialized use cases

Both PowerBI and Tableau offer accessible color themes in the default color selections. When designing visualizations, it's always good to have who your audiences are in mind, and to make sure your visualizations are as inclusive as possible while providing meaningful insights.

When talking about accessibility, above all, intuitiveness and ease of handling linked to robustness with regard to the depth and diversity of graphs and tables, I think, a priori, of Looker Studio; however, it fails, in my opinion, in terms of diversity, and this is where Power BI comes in. I believe that this tool, at the current moment in the job market, is the tool that best balances intuitiveness and ease of use with a variety of views, from the most basic to the most profound.

Maybe the most important aspect to take into account when we're talking about a data visualization software is that the software can't solve all our problems. We still have to take into account the following: - How data literate is our (possible) audience? - Am I asking the correct question for my analysis? - Am I providing support for my audience? - ... The tool is just to support your end result. So no matter your tool choice the basics of data viz practices and implementation are still the most important.

I think that data visualization goes far beyond the beauty of graphs, and understanding this is an art. I say this because there are several factors to be considered in a dashboard; for example, are the colors within the same spectrum? Are the colors accessible? In other words, will people with color blindness be able to correctly read the data? Is there well-structured storytelling behind the graphics? That is, is there a rationale for the layout and distribution of the graphics? All these factors should be taken into consideration when building a dashboard, in my opinion.

Where software and tools can take huge swaths of data and parse that into digestible data points, data visualization is the presentation portion of that equation. The purpose of such imagery is to quickly transfer information from the machine to the human brain, not only efficiently but also in the most meaningful manner possible. Therefore, it is not the aesthetic value of a visualization that counts but the clarity of the message it conveys. Data visualization is such an integral part of self-service BI tools that the tools to make and publish them largely share common feature sets.