Clash of the BI Titans: Breaking Down Power BI, Tableau, and Looker

Which business intelligence tool is the best: Power BI, Tableau, or Looker? Let's explore their strengths, weaknesses, and roles in the market.

Long gone are the days when you had to spend a bunch of time on creating a professional chart. Today, we have access to software that ingests business data and transforms it into user-friendly, interactive charts, reports, and dashboards. This software is collectively known as Business Intelligence (BI) tools.

Currently, there is a plethora of BI tools in the market. In this article, I would like to focus attention on the three most popular BI platforms that are widely chosen by both companies and individual professionals: Power BI, Tableau, and Looker. However, before diving into the comparative analysis, highlighting the strengths and weaknesses of each tool, I would like to take a step back in order to explore the landscape in which they operate nowadays.

Thus, the article will cover:

• Quick BI market overview
• Comparative analysis: Power BI vs. Tableau vs. Looker
  • Product overview
  • Comparison methodology
  • 1. System requirements
  • 2. Implementation
  • 3. Data source connectivity
  • 4. Querying
  • 5. Performance with large datasets
  • 6. Visualisations capability
  • 7. Collaboration & sharing
  • 8. Cost
  • At-a-glance summary
• Final thoughts

Quick BI Market Overview

In order to understand what is going on the market of BI tools, the best way is to turn to Gartner Magic Quadrant for Analytics and Business Intelligence Platforms. Magic Quadrant is annual assessment of the competitive landscape within a particular product category. This Gartner’s tool really matters in the industry and offers valuable insights to guide decision-making in selecting the right platform.

Since 2002 Gartner assess Analytics and Business Intelligence Platforms along two axes - their ability to execute (revenue, customers, partners) and completeness of vision (product features, roadmap etc.). Overall, there are 12 critical capabilities as evaluation criteria and analysis is made based on extensive questionnaires, Gartner Peer Insights reviews, analysts’ opinions of vendors, videos on the web, and job postings, among others.

The latest available Magic Quadrant for Analytics and BI Platforms is as of January 2023:

The best position for a BI vendor is Leaders quadrant. In accordance with Magic Quadrant definitions, Leaders demonstrate a solid understanding of the key product capabilities and the commitment to customer success that buyers in this market demand. In 2022 Microsoft, Salesforce (Tableau) are recognised as Leaders in this Magic Quadrant. Notably, Microsoft, with its primary BI platform Power BI, has also been positioned furthest to the right for Completeness of Vision, and highest in the Ability to Execute in the Magic Quadrant for the fifth consecutive year.

Google’s Looker is still one of the Challengers and is positioned furthest and highest in the quadrant. Challengers are well-positioned to succeed in this market. However, they may be limited to specific use cases, technical environments or application domains. Alternatively, they may fall short of the Leaders in terms of effective marketing, sales channels, geographic presence, industry-specific content and innovation.

Gartner’s Magic Quadrant is a great way to understand the competitive landscape of BI tools, but what BI tools are in demand for data roles nowadays? To answer this question let’s refer to Luke Barousse’s app ”Top Skills & Pay of Data Nerds”, which offers valuable real-time analysis of skill requirements for Data Analysts, Data Engineers, and Data Scientists. This app analyses job postings collected daily from Google’s search results (~6500 jobs/day) from around the globe.

As we can see, as of the publishing date of this article, Tableau, Power BI, and Looker are among the most sought-after BI tools in global data job requirements. Thus, understanding pros and cons of each of these tools is a vital necessity for any data professional.

Comparative Analysis: Power BI vs. Tableau vs. Looker

As I mentioned earlier, my focus will be on Power BI, Tableau, and Looker because:

1. These are the most popular and recognised tools in the market (you will find them in any data analyst job requirements);
2. I have experience working with them (this will add an another layer to the comparative analysis, enabling me to offer insights beyond just technical specifications).

Products Overview

❗ If this is the first time you've come across these tools, or if you haven't been interested in them for a while, be sure to read the product overview below. This will save you hours that you would have spent trying to figure out what products and pricing plans each vendor has.

Power BI

Developed by Microsoft. The initial release of Power BI was in 2011. However, it was first released to the general public four years later, in July 2015, when Microsoft included many additional features.

• Power BI Desktop: the free desktop version of Power BI. You can add data sources, create reports, perform analysis and gain insights, but you cannot share your work.
• Power BI Pro: allows you to publish Power BI reports and dashboards to share and collaborate.
• Power BI Premium per User: besides publishing ability gives access to larger model sizes, more frequent refreshes, XMLA read/write, deployment pipelines, advanced AI, data flows, data marts.
• Power BI Premium per Capacity: includes all the features available in Power BI Premium per user and gains you access to to the rest of Microsoft Fabric workloads through a unified product experience and capacity. It's suitable for organisations that need a fixed amount of computing and storage resources dedicated to their Power BI usage.
• Microsoft Fabric Capacity: Microsoft Fabric, which was introduced in 2023, is an AI-powered analytics platform that unites your data and services, including data science and data lakes. Power BI is a part of Microsoft Fabric. Microsoft Fabric introduces a more flexible approach with its:
  • Capacity Reservation SKUs, integrating a broader set of analytics and data management capabilities beyond just Power BI, such as data warehousing, data integration, and real-time analytics.
  • Pay-as-you-go basis, providing the ability to scale resources up or down as needed and even pause them completely with no usage commitment.

Tableau

Tableau Software, LLC was founded in 2003 and was acquired by Salesforce in an all-stock deal in 2019.

Tableau’s products can be divided into two categories, which are developer and sharing tools.

• Developer tools create visualisations, reports, and dashboards.
  • Tableau Desktop: development interface where you can connect to your data and start building visualisations and reports.
  • Tableau Public: a free version of Tableau Desktop. This is a visualisation tool on the cloud that is available for everyone and has a lot of limitations.
  • Tableau Data Builder: provides data preparation, making it easier and faster to combine, shape and clean data for analysis within Tableau.
• Sharing tools allow to share your work done with the developer tools.
  • Tableau Reader: a free desktop application that allows users interact with reports built in Tableau Desktop.
  • Tableau Server: the server side product from Tableau and it takes care of the data source management, memory management, user management, folder management and other server related components.
  • Tableau Cloud: If you wish to skip the hardware setup of Tableau server and go with a cloud-based version instead. There is no storage limit on the data that can be published.

Looker

Looker Data Sciences, Inc. was founded in 2012 and was acquired by Google in 2019. Since then Google has made a couple of significant changes to the services they have for data, which are quite confusing. Let's untangle ourselves.

“The Looker family” consists of three products:

• Looker (or Google Cloud core) is a unified BI platform used for data modelling, governance, and creating complex data systems. It can also do dashboards, but that’s not where its strength lies.
• Looker Studio (formerly Google Data Studio, initially released in 2016) is a free self-service BI tool for everyone. It helps to visualise data from hundreds of data sources and share your customisable, informative reports and dashboards.
• Looker Studio Pro includes the features provided with Looker Studio, but with support and expanded administrative features, including team content management, for enterprise customers.

Comparison Methodology

I don't have any sophisticated methodology for comparison. I simply consider 8 criteria, which, in my opinion, are critical when choosing a tool to work with:

1. System requirements
2. Implementation
3. Data source connectivity
4. Querying
5. Performance with Large Data Sets
6. Visualisations capability
7. Collaboration & sharing
8. Cost

1. System Requirements

BI and analytics tools are not standalone entities; they must integrate and operate in harmony with all the software and tooling you have behind it.

Power BI: Power BI Desktop, the primary tool for creating Power BI reports, is designed for Windows. It cannot be directly installed on macOS. However, Mac users can use Power BI Service (the web version) in a browser or run Power BI Desktop on Windows through a virtual machine or dual-boot setup using Boot Camp.

Tableau: Tableau Desktop is compatible with both Windows and Mac operating systems, providing flexibility for users across different platforms.

Looker is a web-based platform, so it doesn't have traditional system requirements like desktop software. You can access Looker through a web browser on any operating system, including Windows and Mac, as long as you have an internet connection.

2. Implementation

Power BI is easier and quicker to implement, especially for those already familiar with the Microsoft ecosystem. There is lower technical barrier for setup and deployment.

Tableau implementation can be complex depending on the use case, but it offers extensive resources and community support for implementation.

Looker requires more technical expertise for implementation due to Looker Modeling Language (LookML), which is the language that is used in Looker to create semantic data models. It takes longer setup time but offers flexibility and powerful customisation.

3. Data Source Connectivity

Power BI has a strong connectivity with various data sources, especially Microsoft-based sources like Azure, SQL Server, and Excel. However, Microsoft does not support running Power BI as a cloud service on other platforms such as Google Cloud or Amazon Web Services.

Tableau is known for its ability to connect to a vast array of data sources, both on-premises and in the cloud. Strong connectivity with databases, spreadsheets, and cloud services. At the same time, it needs a strong data warehouse component or intermediary layer to scale effectively. A development path outside of a traditional data warehouse is achievable, but only when tools such as Tableau Prep, Knime, or Alteryx are utilized.

Looker offers extensive connectivity options with databases and a wide range of data sources, including SQL-based and NoSQL databases. Integration with Google BigQuery is particularly strong. Directly uploading data from a file to Looker is not the platform's primary function. Looker is designed to connect directly to databases and data warehouses. So if your approach is to use a few Excel spreadsheets as your source and begin developing then this is likely a poor fit.

4. Querying & Ease of Use

Power BI uses a combination of DAX and Power Query for querying, which is powerful but can have a learning curve. The tool is strong in ad-hoc querying and quick insights. Offers a user-friendly interface with drag-and-drop functionalities, making it relatively easy for users to create visualisations, reports, and dashboards without extensive technical knowledge.

Tableau is known for its powerful and flexible querying capabilities. Users can easily create complex queries with its intuitive interface and drag-and-drop functionality. When it comes to advanced analytics, things will be more complicated (LOD functions and other advanced functions like Window functions, Ranking functions, Table calculations etc.).

Looker. Queries are handled through LookML, offering powerful and complex data modelling. That’s why it is suitable for users requiring detailed and complex data operations. For a user to seamlessly analyse and visualise data in looker its good to have some knowledge of data modelling concepts, LookML and SQL.

5. Performance with Large Data Sets

Performance with large data sets is a crucial factor, as no one prefers to be held up by slow-loading dashboards.

Power BI has good performance with moderate to large datasets, but can experience slowdowns with extremely large or complex datasets. It is limited by the capacity of Power BI service (max: 100 TB) Power BI Premium offers better handling of large datasets.

Tableau is excellent at handling large datasets and complex data models. It is also known for robust performance even with extensive data. Tableau does not have a data size limit and can handle as much data as your local storage can handle without any requirement for cloud service.

Looker handles large datasets well, especially when integrated with powerful databases like Google BigQuery. Performance can depend on the underlying database's capabilities.

6. Visualisations Capability

Power BI offers a wide range of visualisation options that are easy to set up. It provides a unique feature called “Q&A” that enables users to ask questions in natural language and generate visualisations dynamically based on those queries.

Tableau is industry leader in terms of advanced and aesthetically pleasing visualisations. It offers highly flexible and customisable visualisation options.

Looker provides customisable and interactive dashboards. Visualisations are robust but can be less intuitive than Tableau. It may also lack some advanced customisation features compared to Tableau and Power BI.

7. Collaboration & Sharing

All tools have easy sharing steps, but have different sharing options.

Power BI integrates well with Microsoft applications and offers robust sharing and collaboration features through Power BI Service, enabling secure report distribution and access control. This is an extra step that needs to be taken. In addition, the guest user must have the proper licensing in place to view the content that you shared (minimum Pro licence).

Tableau excels with interactive dashboards that can be shared via Tableau Server or Tableau Cloud, allowing for collaborative analytics within an organisation. For Tableau, sharing is similar to Power BI but more straightforward: first, you need to publish worksheets to Tableau Server, then you can share the visualisations with other people by sending a link or an email. That’s why the guest user should have at least Viewer licence.

Looker (Google Cloud Core) emphasises data governance and centralised data models, allowing users to share insights and dashboards securely within the Looker platform, ensuring consistency and reliability of the data being shared. Sharing and collaborating on Looker Studio is easy to do as you can share your reports or dashboards to external users by entering their email address. One constraint is that it requires a person to have a Google account, such as a Gmail account, to get shared.

8. Cost

All these BI platforms have different pricing models and plans for individual data professionals and businesses. Let's analyse them separately.

8.1. Cost for Individuals

Each tool, we are considering, has a free version. However, there are some points to take into account.

• Power BI is perfectly fine for individual analysts who want to work with their data and are not going to share their reports, dashboards, or analytical apps. Power BI Desktop is a core-featured tool. The only unavailable features: advanced AI, data flows, data marts, and XMLA endpoint read/write.
• Tableau has a separate tool as a free version - Tableau Public. It grants access to the majority of the software’s features, but:
  • it does not provide local workbook storage,
  • you can not connect to cloud-based data source,
  • workbook gets public as soon as you publish them,
  • you can access the data for up to 1 million rows only.
• Looker family has a separate BI tool called Looker Studio. It is completely free and . The main advantage over Power BI Desktop and Tableau Public is that you can control access to your data: your work is private, but you can also share it with an access link.

If you want to have a core-featured tool:

• the most expensive solution for individuals is Tableau. It has the highest monthly rate with a commitment to pay annually. Thus, $900 annually can be considered as not affordable.
• Looker (Google Cloud core) is available only for organisations. Additionally, dashboards are not where its strength lies. That’s why companies frequently build Looker Studio on top of Looker (Google Cloud core).
• Power BI Desktop is completely free and even if you need sharing option the Pro version is relatively inexpensive, plus you can pay monthly. The only point, the guest user must have the proper licensing in place to view the content that you shared (minimum Pro licence).

Thus, for individual professionals, Power BI and Looker Studio are the most cost-effective solutions.

8.2. Cost for Organisations

Comparing pricing plans for software is a nightmare. BI solutions are not an exception. Most vendors do not provide clear pricing plans. Everything is limited to the wording a la "contact our sales team to get a custom offer". Even if all prices were available, it would be very difficult to make an apples-to-apples cost comparison, because there are a lot of factors to take into consideration depending on the organisation size, projected growth factor, size of data models, and the hardware and software configurations.

Nevertheless, I managed to gather some information from dozens of sources to understand, at least roughly, how much a particular tool can cost for individual professionals and for organisations.

• Power BI:
  • Microsoft has the clearest pricing plans explanation on their official web site. You just need to consider your inputs and choose the most optimal plan. Previously, it was even simpler, but Microsoft Fabric launch increased the quantity of options and caused a confusion for non-Fabric users.
  • There are plans per user (Pro, Premium) and per capacity (Premium, Fabric).
• Tableau:
  • Tableau primarily offers a subscription-based pricing model, which means you pay an annual fee for each user to access the platform.
  • There are two hosting options: Tableau Cloud (Tableau-hosted) and Tableau Server (Self-hosted).
  • Tableau Cloud's licensing fees include hosting costs, whereas Tableau Server incurs additional costs for hardware, maintenance, and support beyond your Tableau licensing.
  • Pricing for Tableau Cloud is clear, while pricing for Tableau Server needs to be requested at sales team.
  • There are three types of user licences: Creator, Explorer, Viewer.
  • For an organisation can be minimum requirements regarding quantity of licences.
• Looker:
  • Looker (Google Cloud core) pricing has two main components: platform pricing and user pricing.
  • There are three platform editions: Standard (< 50 users), Enterprise and Embed.
  • There are three types of user licences: Developer, Standard, Viewer.
  • In each platform edition 2 Developer and 10 Standard licenses are included.

To compare minimum costs for an organisation more or less fairly, we'll assume the following: 2 Developer / Creator roles and 10 Standard / Explorer roles as these are standard included in Looker's plans. Additionally, we'll include 38 Viewer roles, making a total of 50 users.

Cost Summary

Wrapping up cost, Power BI is relatively low-cost both for individuals and companies when you look at all it offers. In fact, some sources indicate that Power BI's entry and dominance have led other BI vendors to become more competitive in their pricing and licensing strategies.

At-a-Glance Summary

In summarising the Tableau vs. Power BI vs. Looker comparison, I used this scoring method:

• Winner - 1 point (green),
• Loser - 0 points (red),
• Neither winner, nor loser - 0.5 point (yellow),
• The library that receives the most points is the winner.

• Power BI: Ideal for users who need a cost-effective tool with seamless integration with Microsoft products and a user-friendly interface.
• Tableau: Suited for users who prioritise advanced data visualisation and are dealing with large datasets. Tableau’s strength is data visualisation and less so a full-suite Business Intelligence platform, where governance and administration needs lead to added costs.
• Looker defines itself not as a traditional BI tool but rather a modern data platform — and if your organisation adheres to a modern data stack approach within the cloud, then Looker is an excellent fit. It is best for users deeply integrated into the Google Cloud ecosystem and who require strong data modelling capabilities.

Thus, based on the scoring results, Tableau, the winner of this battle, is closely followed by Power BI, while Looker is #3. However, I wouldn't place too much importance on this score; it is all very conditional. The choice depends largely on the specific needs of the user and business, such as the size and complexity of data, budget constraints, technical expertise, and existing infrastructure.

If you are an individual data professional or researcher:

• Looking for an affordable tool, don't need complex customisable visualisations, and don't have a Windows machine → Looker Studio will be an optimal solution.
• Looking for an affordable tool and work on a Windows machine → Power BI is a better option, as it has enhanced data visualisation capabilities. However, it won't be possible to share your work for free; a license is needed.
• Looking for an affordable tool, need advanced high-quality visualisations, and don't have restrictions on making your data publicly available → Tableau Public is a good choice.
• Don't care about costs and need advanced high-quality visualizations while maintaining data privacy → Tableau is a great solution.

For organisations, the choice will highly depend on the specific situation in the company, budget constraints, the environment, and the goals and capabilities of the data team.

Final Thoughts

There is no one ideal tool. Each tool has its own strengths and weaknesses across different criteria. For most businesses and data professionals, it's not about finding the 'best' BI tool; it's about finding the best fit.

REFERENCES

1. 20+ Business Intelligence (BI) Statistics for 2024, from Exploding topics.
2. Magic Quadrant for Analytics and Business Intelligence Platforms as of January 2023, from Gartner.
3. Top Skills for Data Nerds, from Top Skills & Pay of Data Nerds.
4. Microsoft named a Leader in the 2023 Gartner® Magic Quadrant™ for Analytics and BI Platforms, from Microsoft Power BI.
5. Power BI Pricing, from Microsoft Power BI.
6. Looker. Your unified business intelligence platform, from Google Cloud.
7. Looker Studio, from Google Cloud.
8. Looker Data Platform (SaaS Version), from AWS Marketplace.
9. The Tableau platform - The world's leading analytics platform, from Tableau.
10. Tableau Pricing Demystified | True Cost of Your Tableau Investment, from Datadrive.
11. Everything You Need To Know About Tableau Server Pricing, from DashboardFox.
12. Analytics and Business Intelligence Platforms Reviews and Ratings, from Gartner Peer Insights.
13. The Best Data Visualization Tools Of 2024, from Forbes Advisor.
14. Looker Vs Tableau Vs Power BI: Which Is Best For You?, from Ipsos Jarmany.