With more than five million Power BI subscribers (as well as many more users on the free Desktop version) the service’s rapid growth and lengthy list of specialized certifications serve to underline how the ability to use Power BI is a valuable skillset for those seeking to enter data analytics and machine learning. But what is Power BI?
Related topic: What’s the best language for machine learning?
In this post, we’ll provide an in-depth overview of Microsoft’s Power BI, a suite of business analytics tools that enables the quick creation of dashboards, reports, and data visualizations.
We’ll take a look at the different tiers of Power BI and the user cases they each target, its main features, a walkthrough of how data analysts might best leverage the platform, its limitations, and alternate tools.
If you’d like to skip ahead to one of the sections, feel free to use the clickable menu:
- What is Power BI?
- What are the main features of Power BI?
- How is Power BI used by data analysts?
- Limitations & alternatives
- Key takeaways
1. What is Power BI?
Power BI refers to a suite of business analytics tools by Microsoft for both individuals and enterprise users.
You might have encountered tutorials or demos online describing how it is a beginner-friendly, no-code solution with its drag-and-drop approach to creating dashboards and visualizations.
Power BI remains one of the more popular BI tools among analysts as you can create self-service dashboards with enterprise-grade features such as built-in authentication, connection to data sources, and resource scaling. It also encompasses a mobile app version for Windows, iOS and Android operating systems.
Power BI is offered at three tiers. Power BI Desktop is free to download and use, with less functionality than the paid cloud-based software-as-a-service (SaaS) solutions aimed at enterprise users, Power BI Pro and Power BI Premium. Power BI Pro is licensed per user at $120 a year, while Power BI Premium is licensed per cloud compute and storage, starting at $5,000 a month. The Pro and Premium options allow users to publish and access dashboards to the Power BI cloud service, and access reports or visualizations published by other licensed users.
As Power BI is a Microsoft product, existing fans of Microsoft products will appreciate that it is well-integrated within the Microsoft ecosystem of tools, including Teams, Active Directory, and Azure Cloud Computing.
See the following video for examples of other tools data analysts use in the data analytics process:
2. What are the main features of Power BI?
If you’re a beginner to data analytics, you’ll find that Power BI’s point-and-click interface makes it easy for you to quickly perform advanced analytics without requiring knowledge of Python or software engineering. Previous experience or familiarity with Excel is a bonus, as it’s easy to transfer any modeling done in an Excel spreadsheet directly to create dashboards in Power BI.
You can share published dashboards and analytic findings easily within Microsoft’s broader suite of productivity tools: simply publish to Power BI cloud or embed them directly in Teams’ chats. This makes it easier for your key stakeholders and decision makers to access important metrics.
If you hold an enterprise license, you’ll find it helpful to be able to easily enable authentication through direct integration with Microsoft’s own identity provider, Active Directory. As data security is particularly important for these users, it is a significant advantage to configure authentication directly instead of trying to integrate third-party authentication separately.
Analysts working with large datasets will find it easy to increase computing resources with integrated access to Azure Cloud Computing. The Power BI Premium license includes autoscaling, which automates the process of provisioning necessary computing resources which can significantly lower costs.
The platform continues to be well-supported by Microsoft, with new features announced regularly on Power BI’s blog. As the data science and analytics landscape is constantly in a state of innovation, Power BI’s product roadmap likely enables you to remain at (or near) the cutting edge of the field.
Power BI supports an extensive range of more than 500 data sources, making it easy to connect data from multiple sources whether on-prem or on the cloud. Data security is maintained through settings at the data source level, with some data sources offering single-sign on (SSO) access through Direct Query, reducing the need for DevOps involvement or knowledge. Row-level security can also be configured to restrict viewer access to certain datasets within a dashboard.
Cortana, billed as Microsoft’s personal productivity assistant, can be enabled within Power BI to allow natural language queries. You can simply state aloud or type any questions to produce simple aggregations from datasets (such as: “what was the most popular travel category for last year?”). Cortana can also be used to auto-generate basic dashboards. This is in addition to Power BI’s Quick Insights feature that auto-generates data visualizations based on predefined algorithms to surface anomalous data points, seasonal trends, correlations, and other patterns.
3. How is Power BI used by data analysts?
A typical workflow in Power BI consists of data connection, preparation, exploratory data analysis, modeling and iteration, and reporting.
To begin, import data directly into Power BI through data connectors that connect Power BI to Azure’s data sources, external databases, Python scripts, and many more. As your data likely needs to be cleaned and transformed before meaningful exploratory analysis can be performed, you can make use of Power Query, a graphical user interface (GUI) with predefined transformation functions that applies queries and other options to clean datasets.
Next, you can start building dashboards by dragging and dropping different chart options onto a blank dashboard or a templated report. With a better understanding of the dataset, you can now create more advanced models through statistical analysis or create auto-generated machine learning models with the built-in AutoML feature.
Once published to Power BI’s cloud service, your dashboards or reports are easily accessible by business stakeholders for decision-making, teammates for further iteration, and to adjacent functions in the organization for self-service analytics.
4. Limitations and alternatives
The free Desktop version is limited to installation on Windows computers. Although Mac OS users can still access it through roundabout solutions (including setting up a local virtual machine or via third party applications) this may be too technical to set up for the average user.
Machine learning capability is limited relative to other analytics tools; only the Premium license offers “AI-powered data modeling”. This simply means the use of three tools (AutoML, Cognitive Services, and Azure Machine Learning), which offers fairly limited custom machine learning functionality relative to other options.
While there is some integration with R and Python at all license levels, there are a number of limitations at the Desktop tier. Data for visualizations is capped at 150,000 rows or 250 MB, a limit that data scientists will run into quite quickly. Other potential issues include low resolution of generated images (72 dpi), a five minute execution time limit, and the images are not interactive or allow cross-filtering.
Power BI shares these limitations with other tools in its category (such as Tableau or Looker). If you’re looking to go beyond PowerBI’s dashboarding functionality for more complex ML processes—or to build interactive dashboards with more control over the layout and styling—tools like Dash, Streamlit, and Bokeh provide open-source ways to build more complex visualizations and dashboards.
5. Key takeaways
In this article, we answered the question: what is Power BI?We’ve taken a deep dive into Power BI to understand its widespread adoption by analytics teams across industries. Its low code interface makes it a beginner-friendly option for general business analytics users to quickly create dashboards and reports for sharing. It is best leveraged by analysts that do not need to customize and configure every step in an analytics workflow.
To go beyond the platform’s limitations, you might prefer to look into building your own platform and pipeline from scratch through alternative tools.
If the world of business analytics interests you but you don’t know where to start, why not try our free introductory data analytics course? It covers the basics of data analytics as a field and will give you a good idea of whether or not it’s a career path you’re interested in pursuing further.
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