When I first started working with business intelligence applications, I was wondering which one to start with. I did some googling, even asked ChatGTP about it.
I started with Power BI, as it uses a UI similar to MS Office. I started to really like it, mainly because it used basically the same Power Query interface for data transformation as Excel. PQ remained until now my main tool for data transformation/cleaning.
The main reason I stopped using Power BI for visualisations is its pricing model and embedding capabilities. In order to embed a visulisation on a public website, one has to use the Premium version of the application. Another common obstacle is that to even use Power BI Service, the web platform one has to have a business or school email account (I fortunately got it after creating this website and domain).
Having started with Tableau I got to like it as well, especially the numerous visualisation capabilities. The part I don’t like is how dependant it is it’ s web platform, not having a possibility for a local save.
The third visualisation/BI tool I am using the (Google) Looker Studio. It is not feature-heavy but has easy controls and good connectors. It uses a bit different names for the visualisation fields (dimension, metrics ..), which are more constistent with the google data idealogy, but for example what I really like is the “smart grid” which allows smoothing stacking of different visualisations next to each other.
Getting to the point
Anyhow, what I wanted to say by this article is that using any of these apps is very similar. Professionally it is called “transfarable skills”. Creating visualisation in neither of these is very similar.
What is important, is the preceding work – sourcing, transforming, cleaning and modeling the data.
Once one has the dataset and the data model prepared, finishing it in any of these applications is not too different from each other.
Here is example of preparing the same visualisation in Power BI and in Tableau.
Source of the able below is The Path to Insights: Data Models and Pipelines, the second course of the Google Business Intelligence Professional Certificate
| Tool | Uses |
|---|---|
| Azure Analysis Service (AAS) | Connect to a variety of data sources. Build in data security protocols. Grant access and assign roles cross-team. Automate basic processes |
| CloudSQL | Connect to existing MySQL, PostgreSQL or SQL Server databasesAutomate basic processes. Integrate with existing apps and Google Cloud services, including BigQuery. Observe database processes and make changes |
| Looker Studio | Visualize data with customizable charts and tables. Connect to a variety of data sourcesShare insights internally with stakeholders and online. Collaborate cross-team to generate reports. Use report templates to speed up your reporting |
| Microsoft PowerBI | Connect to multiple data sources and develop detailed models. Create personalized reports. Use AI to get fast answers using conversational languages. Collaborate cross-team to generate and share insights on Microsoft applications |
| Pentaho | Develop pipelines with a codeless interface. Connect to live data sources for updated reports. Establish connections to an expanded library. Access an integrated data science toolkit |
| SSAS SQL Server | Access and analyze data across multiple online databasesIntegrate with existing Microsoft services including BI and data warehousing tools and SSRS SQL Server. Use built-in reporting tools |
| Tableau | Connect and visualize data quickly. Analyze data without technical programming languages. Connect to a variety of data sources including spreadsheets, databases, and cloud sources. Combine multiple views of the data in intuitive dashboards. Build in live connections with updating data sources |