Data Collection with Tableau and Power BI

October 25, 2020

Business Intelligence (BI) software is a top priority item in the toolkit of almost every business team. This is due to the significance data collection, storage, and visualization has to a business. Analysis from BI tools is used to make strategic business decisions.

Before running a project, you have to ensure that you use the right business intelligence product(s).

This article intends to compare the two most commonly used data visualizations tools: Tableau and Power BI. The comparison will concentrate on four broad parameters. These are integration, data visualization and dashboard, analytics, as well as data sources and connectivity.

An Overview of Tableau

Tableau is an analytics tool that helps combine and integrate data from many sources into visualizations. Users can access this data in a single desktop environment with the use of Tableau Desktop.

Alternatively, the data can be accessed through a shared dashboard. Tableau’s user-friendly interface allows users to analyze data easily, create visualizations, reports, and share insights.

Tableau Desktop acts as a report development tool. You can use this tool to create reports and format your data into visualizations. You can then organize those visualizations into a dashboard for personal use or share them through the Tableau Server.

The Tableau Server portal is secure, and can be used to share your dashboard with others from any mobile device or browser.

An Overview of Power BI

Power BI is a product of Microsoft Corporation. It is composed of several tools, including Power Pivot, Power Query, Power View and Map.

Power Query extracts and transforms data; Power View and Map helps visualize data; and Power Pivot models and analyzes data. With Power BI, users can create dashboards and reports by themselves without necessarily engaging system and database administrators.

This is possible with the tools self-service business intelligence capabilities as well as interactive visualizations. Power BI offers a cloud-based business service (Power BI Services) as well as a desktop interface (Power BI Desktop).

This tool is popular for its data warehousing capabilities (including data discovery, interactive dashboards, and data preparation) and its capacity to load custom visualizations.

Key Differences

Integration

When it comes to integration, Tableau can connect to almost any data repository. Whether it be Hadoop clusters or MS Excel, with this program, you can open up data source integration to many relational databases and NoSQL databases.

The improved Tableau’s support for JavaScript and REpresentational State Transfer APIs (REST) promotes efficiency while reducing the reliance of integration across analytic platforms on customized programming.

Besides, the integration support through APIs is beneficial in ensuring streamlined collaboration with Microsoft Office, Salesforce, SAP, Google Analytics, social media channels, enterprise resource software, among other programs.

In addition to built-in security measures, Tableau is composed of other security system integrations, including OAuth, Kerberos, and Active Directory.

Power BI leverages Microsoft’s connections and familiarity with many applications. Microsoft Power BI allows users to mine data from Google Analytics, MySQL, Salesforce, Facebook, Zendesk, MailChimp, Microsoft Excel, and Oracle.

Microsoft adopted a hybrid integration strategy founded on the Microsoft Azure Cloud APIs. This innovation allows users to use desktop-based and browser-based authoring with platforms and apps in the cloud and on-premise.

Power BI has a competitive edge over Tableau when it comes to integration. This is partly because Power BI has access to multiple APIs with the advantage of pre-built dashboards that allow instant insights.

Data Visualizations and Dashboard

You can create various types of baseline visualizations with Tableau. These are not limited to line charts, scatter plots, and heat maps. With Tableau, you can also develop complex and sophisticated visualizations without prior coding knowledge.

You can ask “what if” questions regarding the data and use as many data points in your analysis as possible.

Microsoft’s Power BI makes uploading data very easy. You can use various visualizations as blueprints and then insert data from a sidebar in the visualization.

Another way to create visualizations with Power BI is by asking queries with natural language. But you have a data point limit of 3,500 to drill down into datasets when conducting an analysis.

For data visualization, Tableau is a better tool. Power BI’s focus is on predictive modeling as well as reporting.

Analytics

Tableau capabilities go beyond data visualizations. You can perform advanced analytical work with this tool. It supports more complex data federation workflows, and this makes data mashups more reusable.

The tool has also made significant strides in developing its mobile app to ensure it supports all mobile screen sizes.

Power BI’s analytics interface closely resembles Excel. This assures recognizable functionality and an easy-to-learn Power BI experience. Like with Tableau, you can perform complex data analysis using Microsoft Power BI.

An analysis with Power BI can include premise-based integration points and data sources. Power BI Desktop and Power BI Suite contain data discovery and interactive dashboards.

Data Sources and Data Connectivity

A key strength of Tableau is its ability to support many data connectors. These include the cloud option, online analytical processing (OLAP), as well as big data options like Hadoop and NoSQL.

When you add data from multiple sources, Tableau will determine the relationship automatically. You can create or modify data links manually based on your preference. You can connect to a distinct data warehouse if you use Tableau.

Power BI also connects to a number of user’s external sources. They range from SAP HANA, to MYSQL, and JSON. Like Tableau, Power BI determines relationships automatically once you add data from multiple sources.

Many programs that work with Microsoft integrate with Power BI, and this provides an extensive library for potential collaboration opportunities.

Most important, with Power BI, you can connect to data stored in text-based files, third-party databases, Microsoft Azure databases, and online services such as Google Analytics and Salesforce.

Which is Better for Business Intelligence?

Power BI is a widely used tool not only by data analysts but by common stakeholders. Teams with a need to perform data analysis can use this tool without any prior experience in data analysis.

Tableau is a bit challenging to learn for individuals without data analysis experience. Power BI is preferred for its ease of use, while Tableau is a tool of choice for its capabilities and speed.

Organizations with limited human and financial resources can rely on Power BI. In contrast, medium to large enterprises with a wealth of human capital, and a priority on analytics can use Tableau.

In essence, the choice of each tool is dependent on individual needs and capabilities.

Conclusion

Both Power BI and Tableau are excellent because they are both easy to use due to their intuitive design.

That said, Microsoft has a competitive advantage on infrastructure components and support, while Tableau boasts being a leader when it comes to its ease of use.

Tableau has created a strong base of data connectors in the recent past. It is this same technology that the company leverages to provide highly interactive data visualization tools and multiple dashboard options.

Microsoft Power BI is offered on-premise and on the Azure Cloud Platform. This explains why Microsoft infrastructure components such as security, architecture, and administration are superior to Tableau’s.

References

Power BI vs Tableau: A Data Analytics Duel

Power BI vs Tableau vs QlikView: Which BI Software is the Winner?

Competitive Report: Tableau vs QlikView vs Power BI


Peer Review Contributions by: Lalithnarayan C


About the author

Eric Kahuha

Eric is a data scientist interested in using scientific methods, algorithms, and processes to extract insights from both structural and unstructured data. Enjoys converting raw data into meaningful information and contributing to data science topical issues.

This article was contributed by a student member of Section's Engineering Education Program. Please report any errors or innaccuracies to enged@section.io.