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As 2024 draws to a close, we want to express our heartfelt gratitude to the incredible Tableau Community. Your boundless creativity and innovative ideas have been a constant source of inspiration, driving us to develop products that enhance and enrich your experience. It’s your feedback and collaboration that help us push the boundaries of what’s possible in data visualization and analytics.

Thank you DataFam for inspiring, voting for, and sharing with us about the features released in 2024! This year we released features that address 150+ ideas with 46,000+ points in the Forums, across Tableau Desktop, Online, Tableau Pulse, and more. And we made progress on providing visibility into the ideas on the Tableau Community Forums: This year we have updated more than 2,900 ideas.

Here are some of the features inspired by ideas you submitted on the forums:

Viz Extensions (2024.2) & Table Viz Extension (2024.3)

See this idea on Sankey Charts and Thierry Jakercevic’s idea on tables

You asked, and we delivered—unlock your creativity and speed to insight with the latest updates to table formatting in Tableau. These enhancements, including resizing, sorting, and conditional formatting, are designed to help you visualize and present your data more efficiently and clearly. Together, these features not only streamline your workflow but also enhance the overall clarity and attention to important data points and anomalies.

For developers, Viz Extensions provide a unique opportunity to expand Tableau’s analytics capabilities with custom visualizations. These can be developed using any JavaScript visualization library and easily integrated into Tableau through our Extensions API. This makes it straightforward to deliver personalized visualization tools that can be scaled to every analyst in your organization and shared with the wider Tableau Community.

"Tables in Tableau have always been in the forefront of user feedback. It's a critical means for how our users deliver analytics, and stands to have more dedicated usability improvements. The community advocacy around these themes helped us build the business case with leadership that we should make a more dedicated approach as a Viz Extension, delivering to a variety of feedback on how users specifically wanted to design and format tables inside Tableau. It provided us the quantitation such that we could discuss usability in Tableau with decision makers, and help them rationalize that the investment to this experience is a data driven decision. The ideas forum continues to be how we think about the key issues we want to explore in our long term roadmap and specifically how we can develop the foundations that allow for us to be more responsive to the feedback as we look ahead."

Wilson Po, Product Management Director

We encourage developers to join our DataDev Developer community to share feedback and ideas for new extensions. Developers can learn more about developing new Viz Extensions by learning more about the Tableau Extension API.

Accessibility: Google Fonts in Tableau Cloud (2024.3)

Idea submitted by stelloprint dev

You spoke, and we listened! Support for Google fonts is a significant win for our front-end developers. With these web safe fonts, they can be used without having to install them on all client machines. This feature helps provide a consistent experience across all viewers' browsers, while maintaining the intended look and feel of your data visualizations and dashboards.

 

"Thanks to our community’s input and ideas regarding our product support for web safe fonts. From their requests and feedback, we were able to prioritize and focus on adding new Google fonts to Tableau Cloud. These ten new fonts add new variety and options for our Cloud customers while also being web safe fonts. This adds a huge amount of value for customers needing a consistent representation of their data across browsers."

Rob Thomas, Product Management Senior Manager

Multi-fact Relationships (2024.2)

Idea submitted by Cristhian De la Hoz

The Tableau Community was ecstatic with the release of Multi-fact Relationships! This data modeling capability allows you to combine multiple fact tables and gain insights from complex data models. With Multi-fact Relationships, you can easily connect different data sources and analyze them together, without having to blend them. This is especially useful when you have dimensions that share the same meaning across tables, or when analyzing data that goes through different stages. To use Multi-fact Relationships, simply identify your base tables and create relationships between them and shared dimension tables. With this powerful tool, you can unlock new types of analysis and accelerate your time to insights. 

Say goodbye to linking fields - easily blend data sources with shared tables and trees.

 

We define Data Literacy as the range of capabilities that describe someone’s ability to explore, understand, and communicate with data. These days, when we have questions, there’s plenty of data around for us to find the answers, and having some data literacy will make that possible. Unfortunately, it’s not always structured in a way that makes it easy to use, which is why we’ll spend time exploring the different characteristics of data, the ways it can be structured, and why. Lastly, we’ll touch on how to restructure the data to make it work better for your needs.

Know what to look for in data

There are lots of words that are used to describe the details of an item: traits, characteristics, specifications, descriptions. All of them help us find the information we need to determine if something is a good fit for what we’re doing. Recently, I needed to replace a broken part on my bike. For the bike to work right, the replacement I bought had to match the specifications of the original and the only way to be sure of that was to read the descriptions of the parts I was considering buying. It’s similar when looking at data: for us to get the answers we need, the data has to have the traits that make finding our answer possible, and the only way to be sure it does is to know what qualities it needs to be a good fit.

The first trait is High Volume. More (relevant) data gives us a much more reliable result. If one person tells you a restaurant is good, you might believe them. However, if 100 people tell you a restaurant is good, it’d be hard to believe otherwise. The same goes for our professional data - more records telling us something is good or bad, gives us higher confidence in our results.

The second trait is Historical. If I visited Seattle for the first time and it was sunny the first day of my visit, it would be naive of me to say that it’s always sunny in Seattle. I’d need data stretching back quite a while to feel confident in making a statement like this and it’s no different for your analysis. The more historical data you have, the more likely you are to accurately anticipate what’s to come in the future.

Third we have Detail. Recently, the water pressure in my kitchen sink dropped significantly, forcing me to troubleshoot the situation. First I checked the water supply, then the valves under the sink, then the line going to the faucet, and finally the faucet itself getting more detailed with each dead end. I finally discovered that it was debris in the faucet blocking the water flow. It’s no different than exploring our data to understand why something is happening in our business or lives. The more detail we have in our data, the more depth and accuracy we can get in our answers and insight which we can confidently use to make the best decisions

Lastly is Consistency and Standardization. Think about the last time you were in a conversation with a friend and you were using the same words but had different understandings of what they meant. You end up with miscommunications that potentially cause issues down the road. Similarly, a lack of consistency in your data: field names, date formats, number formats, and a lack of standardization in how data is recorded results in numbers that aren’t an accurate representation of what actually happened. Imagine that you work at a cell phone store and you return five iPhones. When typing in what they were you type: iPHONE5, I phone5, iphone 5, I Phone 5, and Iphone Five. You then try to report on how many iPhone 5’s you returned and instead of your system showing five iPhone 5’s it shows one return of five different iPhones because it’s impossible for your system to know they are the same thing. Standardizing on a naming convention, or date format, or currency prevents situations like this and provides the consistency needed to get an accurate picture of your data.

Know what well-structured data looks like

It’s bound to happen. At some point you’ll come up against data that just feels difficult to work with. You may not know why, but I’d bet that it comes down to the data not being well-structured. It’s a bit of a loaded statement as the meaning of “well-structured” can differ based on the use case, and the systems/applications you’re using, however there are some basic data structure principles that should improve your experience when working with data.

First, you need to figure out how it’ll be used. Are you going to use software to visualize the data? Or are you presenting the data in a spreadsheet? Or are you preparing the data for someone else? Each method will change the way the data needs to be structured. There are exceptions to every rule, but once you know the way it’ll be used, these tips are a good rule of thumb to get you started:

Columns and rows make up the core of our structure. Just like columns in buildings, columns in data are the vertical sections of our data structure stretching up and down the page. In the example below, “Date” would be a column of information along with 7 other columns. Rows on the other hand are the horizontal sections of our data structure spanning left to right across the page. In the example below there are 12 rows of data.

If you are using software like Tableau to visualize your data, each row should be an instance or an event, and each column should represent a detail about that instance or event. In the image below, the first row (or event) is a commute. The details (columns) are the date, the time of day, the temperature, the precipitation type etc…Each row after the first row is an additional event where we collect the same types of details that we did in the row (event) above: 

This allows the software to easily search through your data when trying to retrieve the answers to your questions. The downside to this format is that it’s hard for humans to consume which is why it’s not uncommon see data structured like this when people don’t have access to data visualization software: 

The format above is human readable but requires you to manually calculate totals and aggregations, and manipulate structure. This structure will also continue to grow wider over time as new data is added, making it much harder to visually analyze without lots of scrolling. Lastly, having individual columns for each date or having multiple types of values within a single column makes it almost impossible for an application to help you analyze your data.

Want to learn more about well-structured data? Check out this Trailhead module today!

What to do if your data is not well-structured

Rather than let these issues prevent us from getting answers, there are a few simple things we can do to solve a good amount of our data problems.

Go back to the source. Restructuring your data can help your immediate problem but doesn’t solve the larger issue of how it got that way in the first place. It also creates additional data sources that may not align to your corporate standards which can be a problem when comparing results across your business. It’s always the best decision to start with your data team to see what’s possible. Remember, for them to make the right changes, they’ll need plenty of context so come prepared to explain what you are hoping to get from the data.

Make the change. In a perfect world your data team would help you fix poorly structured data, but that isn’t always possible, and not everyone has a data team. When it’s not, there are a couple of common structure issues that you can address on your own.

Split ‘em Up! The first issue is when fields that should be separate appear as a single line of information. For example if the fields [Name] and [Customer ID] showed up together as:

“SamPriddy CN1357WA” and needed to be separate fields instead, you’d perform what’s called a “Split”. A split gives the user the ability to choose which “delimiter” gets used to decide where to break the field apart. A delimiter is just the name for the character in the line of information that the user chooses to use as the breaking point. So in the Case of the example above where the field says: “SamPriddy CN1357WA” we could use the “Space” character as our breaking point, or our delimiter, since we wanted to split the name apart from the customer ID. This functionality is built right into Tableau Desktop and Tableau Prep with no coding required. Want to learn how? Check out this help article for more information.

Pivot, Pivot. The second issue relates back to the commute data example above. In its human readable form the data is wide, having a column for each date, with a mix of different types of values filling in the column below. You’d use a “Pivot” to change the data from being wide to being long instead.

This is by no means the only way to do a pivot but an example of the concept and what it means. Here’s a simplified example to illustrate the process:

The data below is formatted for human consumption. We want to use this in Tableau though and this format won’t work. We’ll need to do a pivot to get it into the appropriate structure. 

First we’ll make space by shifting our data out of the way. We’ll then make a column header for our fields that would be more appropriate as a category. In the example below we’ll copy/paste the “Product Type” fields down column A for the amount of values that showed up across their row. Because “Appliances” had four values (1 for each quarter) we’ll copy/paste “Appliances” until we have four rows of this product type. Similarly, we’ll pivot the individual quarters down and to the left, with Column B being our pivot point, changing the data from being horizontal, to vertical. We’ll then rename the column “Quarter” as it now contains all quarters, not just one specific quarter. 

Next, we continue the process until we have all of our product types duplicated for the amount of records they originally showed (4 each in this example). This is what we mean when we say the data gets “longer”. We haven’t added any data, just changed the layout. We’ll also continue copy/pasting the quarters down our “Quarter” column until each product type has a specific quarter next to it. 

Once that’s done, all that’s left to do is to fill in our values. Remember that originally Q1-Q4 were horizontal so we’ll move our values into the empty column next to the quarter they belong with. We’ll repeat this process until all of our values are where they belong in their new column which we’ll name “Values.” Depending on your data, the name should fit whatever best fits the values. 

After the final column is populated we’re ready to connect to this data to answer some questions. 

This example is shown to illustrate the concept, however it’s more likely that you’ll do a pivot in a tool like Tableau Desktop, or a data preparation tool like Tableau Prep. In those cases, the tools have functionality that eliminates the manual work shown in the example above, but ultimately gives you the same results. Want to learn how to do pivots in Tableau? Check out this help article for in-depth instructions.

What next?

Be a sponge, and practice, practice, practice! The best thing you can do is continue to learn, and get hands on as much as possible. Check out our Data Skills homepage for access to tons of Data Literacy resources. Visit Tableau Public to download and use Tableau Desktop for free as well as free learning and sample data sets! If you prefer webinars, check out the Datafam Discovery Kit for more resources and updates on upcoming live webinars. 

The Cloud Migration App for Small Deployments is an open-source application based on the Tableau Migration SDK that allows administrators to copy content and users from Tableau Server to their Tableau Cloud site with ease. Created to simplify the migration process for Tableau admins with smaller deployments, this tool supports essential content transfers such as workbooks, users, groups, and more, providing a straightforward solution to make the transition to Tableau Cloud smoother.

The Cloud Migration App for Small Deployments is built to meet core migration needs in the following areas:

• Authentication — The app supports only local authentication (Server) to Tab ID + MFA (Tableau Cloud) for migrated users, helping keep the process secure and manageable.
• Sites — To ensure a focused, efficient migration, the app transfers content for one site at a time.
• Data sources — All data sources supported by Tableau Cloud are eligible, including those integrated through Tableau Bridge (note that Bridge must be installed and configured separately by the user).
• Migrated content — The app transfers users, groups, projects, data sources, workbooks, permissions, and custom views, providing a comprehensive content migration experience.
• Tableau Server version — While the app can migrate from recent Server versions, it is recommended to have the latest version installed.
• Operating system — The app runs on Microsoft Windows and MacOS, making it easy to integrate into most IT environments.
• Recommended workload — Though there is no strict limit, the app is designed to handle migrations of around 100 workbooks smoothly. This is a helpful benchmark for implementations with larger workloads to consider alternative or supplementary tools.

For more details on how to best prepare for your migration, see SDK pre-migration checklist.

1. Ensure the app fits your needs

The Cloud Migration App for Small Deployments is ideal for specific use cases. To experience a smooth migration, we recommend administrators make sure their needs are addressed by the tool.

The following are situations in which the tool may not be the best fit:

• Data management — Tableau Prep Flows and Virtual Connections are not transferred by the app. Administrators will need to migrate these manually.
• Embedded content — The app migrates Tableau content only. If your Tableau Server content is embedded within other applications, those embedded integrations will require separate adjustments post-migration.
• Authentication — The tool does not support SAML or OIDC authentication, meaning advanced authentication configurations must be set up separately on Tableau Cloud.
• Data sources — The app does not support cube data sources or large Hyper extracts (over 25GB). The app supports data sources compatible with Tableau Cloud, so ensure your data fits within these requirements.

If you’re a large organization or have additional needs not supported by the app, refer to the Tableau Cloud Migration page to learn more about additional resources available to support your migration.

2. Set up your migration

After confirming the Cloud Migration App for Small Deployments meets your needs, you’ll only need a personal system (Windows or MacOS) to run it. To download the app, follow the instructions on the app's page.

You’ll start by providing the web addresses (URLs) of your Tableau Server and Tableau Cloud Site, along with your authentication credentials (Personal Access Token Name and Secret).

You also have the option to specify mappings between Tableau Server usernames and Tableau Cloud usernames. This is helpful since Tableau Cloud, unlike Tableau Server, requires usernames in the format name@domain. A file with username mappings can be used, or the app can apply a default domain name to any usernames not listed in a mapping file.

3. Migrate your content

To initiate the migration, simply click the Run Migration button. A window below the button displays progress in real time, and a log file will be generated for reference. Once migration is complete, all selected content should be available in your Tableau Cloud site. We highly recommend reviewing all workbooks and content post-migration to confirm everything operates as expected.

For administrators who may have large content sets or limited time to manually verify the migration, our trusted partner Wiiisdom offers an automated solution to verify workbooks quickly and efficiently.

How to get started

We’re excited to make the transition to Tableau Cloud more accessible than ever with the Cloud Migration App for Small Deployments—and we are here to support you every step of the way.

If you’re ready to get started, contact your account representative or visit the app's page. We hope you enjoy a seamless migration and we look forward to seeing what you create in the cloud!

Since the launch of the VizQL Data Service Developer Preview, the DataDev community has shown impressive creativity and innovation. The ability to programmatically query Tableau’s published data sources has empowered developers to simplify workflows, integrate custom applications, automate processes efficiently, and more. Thanks to your valuable feedback and the real-world use cases we’ve seen, we're excited to expand the developer preview to all Tableau Cloud customers.

Are you on-premises with Tableau Server? We haven’t forgotten about you! The developer team is hard at work building VizQL Data Service for general availability for both Tableau Cloud and on-premises Server. We encourage you to join the Developer Preview via our beta site and begin exploring the benefits of VizQL Data Service for yourself.

Your data, your site, your way

If you’ve been waiting to test the VizQL Data Service API with your own data sources in Tableau Cloud, now is the time! As part of the 2024.3 release, you can now experiment with the VizQL Data Service API using your own data within your Tableau Cloud site, while it remains in beta. This expansion moves beyond the limitations of the initial beta environment, allowing more users to explore its potential. Whether you’re ‌integrating Tableau data into customer-facing applications or automating critical workflows, this expanded access allows you to use your real, published data within your Tableau Cloud site.

The service remains in beta, and is off by default—meaning you’ll need to grant API Access permissions at the data source level to turn on programmatic queries. Let’s get more into that!

Introducing API Access permissions

Available now in Tableau Cloud as part of the 24.3 release, API Access permissions offer more control over who can query published data sources via the API.

As developers explored the capabilities of VizQL Data Service, the need for more detailed control over data access became clear. Many of you wanted the ability to manage who could programmatically query data sources for various administrative reasons. In response, we’ve introduced API Access permissions, allowing you to assign specific users or groups the ability to query data sources directly within the data source permissions dialog.

While users still need the existing “view” ability to access a data source, this new permission provides an additional layer of control. For example, an admin might want to manage consumption costs on back-end systems (e.g. Snowflake) or control load and usage on on-premises servers.

Get started easily with the Postman collection

In addition to the new permissions feature, we are making it easier to access and work with our developer tools. To help you get up and running with the VizQL Data Service API, we have created a Postman Collection. This resource is designed to simplify testing and interacting with the API, giving you a straightforward way to start querying your data programmatically. You can find this collection in our GitHub repository.

Try VizQL Data Service

With the latest updates, Tableau Cloud users can start using API Access permissions as part of the 2024.3 release. Head over to your Tableau Cloud site and start exploring how these new capabilities can enhance your workflows and give you more control over data access. And don't forget to join the VizQL Data Service Developer Preview to share your feedback!

Tableau Visionary Tristan Guillevin and DataDev Ambassador Jessica Bautista co-run LaDataViz, a data visualization studio and Tableau Developer Partner. 

Since Tableau's first release in 2004, all visualizations created inside Worksheets have been rendered using VizQL, a breakthrough technology that allows you to create a chart with a simple drag-and-drop. I still remember building my first visualization in Tableau and thinking, "Wow, is that so easy?"

And most of the time, it is indeed that easy! However, after using Tableau for some time, we all eventually want to build a Donut Chart, be asked to create a Sankey, or need a Network Diagram, and for that, things can get a little more complicated. 

Shipping sankey chart by Tristan Guillevin.

Thanks to the Tableau Community, blogs, and YouTube videos, we always find a way to do it. Still, it often involves hacks, data densification, or other complicated techniques that just feel off compared to the ease of making simple charts.

Or at least, that's how it used to feel.

Twenty years have passed since Tableau 1.0 and VizQL, and the next revolution, Viz Extensions, is now available in Tableau 2024.2. Say hello to complex charts, as easy as drag and drop.

What are Viz Extensions?

Viz Extensions expands Tableau's visualization options, making it easier for anyone to create new visualization types without any calculations or templates.

Viz Extensions are available directly from your Worksheet in Tableau. Select the chart you want to create and simply drag and drop your data fields into the Marks cards to generate a custom visualization, filter your data, or select a data point to trigger an action. It is Tableau as you know it, but for any chart you can dream of.

Walkthrough of LaDataViz Radar Chart. 

Each Viz Extension is a new custom chart built by a developer. Do you need to compare football players' statistics? Use the Radar Viz Extension. Do you need to visualize the connections between many data points? Use the Network Viz Extension. It really is that easy.

Finally, for us at LaDataViz, Viz Extensions are everything we hoped for in the evolution of Tableau. We already created a tool for the community to generate advanced charts in Tableau, but Viz Extensions allowed us to go further than we had hoped. 

Available Viz Extensions

Tableau and developer partners LaDataViz, AppsForTableau, Actinivision, and DataMa have created Viz Extensions that are all now available on the Tableau Exchange. Dive into a roundup of the Viz Extensions available categorized by chart type. 

• Beeswarm Chart
• Bump Chart
• Chord Diagrams
• Funnel Chart
• Network Diagrams
• Polar Chart
• Process Chart
• Radar Charts
• Radial Chart
• Sankey Charts
• Streamgraph Chart
• Sunburst Charts
• Table Visualizations
• Tree Diagrams
• Venn Diagram
• Viz Extensions for Dashboard Optimization
• Waterfall and KPI Charts

Get started with Viz Extensions

It all starts in your Tableau Worksheet! Since Tableau 2024.2, you’ll find a new Add Extensions button when selecting your Marks. Then, you can choose between:

• Browse Viz Extensions: Viz Extensions from the Tableau Exchange (built by Tableau Partners like us). These are free to try.
• Access Local Viz Extensions: if you purchase a Viz Extensions or develop your own, you can use a logcal .trex file here.

A Tableau worksheet where you'll find an "Add Extension" button when selecting your Marks.

Each Viz Extension has its own Marks type and behavior, so try them!

You can find all the Extensions available on the Tableau Exchange here: https://exchange.tableau.com/viz-extensions

To learn more about Viz Extensions, check out our FAQ, which includes a 40-minute deep dive video hosted by TableauTim. 

Beeswarm Chart

Beeswarm by LaDataViz: Visualize the distribution of data points without overlap.

Beeswarm Chart featuring Sherlock Holmes Canon & Adaptations by Jessica Bautista Gauna.

Beeswarm charts are excellent for displaying data distributions across categories in a way that maximizes space and avoids overlapping points. This makes it easy to identify clusters, gaps, outliers, and the overall spread of your data. By providing a clear view of each data point, Beeswarm charts are handy in detailed exploratory data analysis where individual data values are interesting. 

Bump Chart

Bump Chart by LaDataViz: Visualize changes in rank over time among categories.

Bump Chart featuring Women's FIFA Ranking by Becca B.

Bump charts are perfect for illustrating competitive dynamics, such as market positioning, sales rankings, or sports standings, in a clear and compelling way. They are invaluable for displaying rank progression through time, allowing viewers to easily track the rise and fall of different entities within a dataset. They are particularly effective in identifying trends, highlighting winners and losers, and visualizing shifts in position.

Chord Diagrams

Chord Diagram by LaDataViz: Visualize the inter-relationships between entities in a compelling, circular layout. 

Chord diagram featuring Young Adult Migration by Gbolahan Adebayo.

Chord diagrams are convenient for displaying the flow or connections between different entities, such as the relationships in social networks, migration patterns, or trade flows between countries. These diagrams use arcs and ribbons to visually connect data points around a circle, making it easy to see the interaction volume between components and identify dominant and minor relationships in a dataset.

Chord Diagram by Infotopics | Apps for Tableau: Illustrate the relationships and connections between entities in a network or system.

Chord diagram featuring Young Adult Migration by Gbolahan Adebayo.

A chord diagram is a visual representation that illustrates the relationships and connections between entities in a network or system. It typically consists of a circle divided into segments, with each segment representing a category or entity. The connections between the segments are depicted as curved lines, or "chords," which indicate the interactions or relationships between the categories. Chord diagrams are commonly used in data visualization to show patterns of interactions, flows of information, or relationships between different elements, such as in social networks, genetic studies, or supply chain analysis. They provide a clear and intuitive way to understand complex relationships and identify key connections within a system.

Funnel Chart

Funnel by Actinvision: Data progressing through stages.  

Funnel Chart Viz Extension by Actinvision in Tableau. 

Visualize your data through stages with the Funnel Chart Viz Extension which has advanced style configurations—allowing fine-tuning of the funnel’s appearance parameters. Color customization and label configuration is also available. 

Network Diagrams

Network by LaDataViz: Visualize connections and relationships within your data. 

Network chart featuring The Marvel Cinematic Network by Tristan Guillevin

Network charts are invaluable for exploring and presenting the relationships between entities, whether individuals in social networks, infrastructure components, or data structure items. These diagrams provide a visual map of interconnections, helping users to understand how data points interact and influence each other.

Network Diagram by Infotopics | Apps for Tableau: Understand the relationships in data.

Network Diagram Viz Extension by Infotopics in Tableau. 

A network diagram is a data visualization method that allows users to understand relationships in data easily. Network diagrams are composed of nodes and paths. Nodes are singular data points that are connected to other nodes through paths. The paths show the relationship between two or more nodes. 

Polar Chart

Polar Area Chart by LaDataViz: Analyze and present multivariate data sets in a circular format.

Polar Area Chart Viz Extension by LaDataViz in Tableau. 

Enhance your data visualization toolkit with the Polar Areas Chart Extension for Tableau, an innovative take on the traditional Coxcomb or Nightingale Rose chart. This version offers a visually compelling method for analyzing and presenting multivariate data sets in circular format. Polar Areas charts are particularly effective for showcasing relationships and proportions among multiple variables in a format emphasizing comparisons and trends. By varying the radius of each segment proportionally to the data it represents, these charts allow for intuitive and immediate visual comparisons across different categories or time periods.

Process Chart

ProcessMining by Infotopics | Apps for Tableau: Visualize and analyze your processes based on log file extractions.

ProcessMining Viz Extension by Infotopics in Tableau. 

This ProcessMining extension for Tableau helps visualize processes, instead of assuming it. Audit, analyze, and improve your processes by answering both compliance-related and performance-related questions. Get instant insight in all sub processes, outliers, deviations, process bottlenecks, and more. Use the interactivity of Tableau to enable users to filter and zoom in on specific cases, periods, process steps (nodes) or connections (Links) of your process to analyze, optimize, or adjust your processes.

Radar Charts

Radar Chart by LaDataViz: Visualize and compare multiple variables of one or more groups.

Sample Superstore Radar Chart by Tristan Guillevin.

Radar charts are particularly useful for displaying performance metrics across multiple areas in a way that is easy to compare and contrast. They allow viewers to quickly gauge strengths and weaknesses at a glance, making them perfect for benchmarking, skills assessments, or any scenario where relative performance across various categories needs to be visually summarized.

Radar Chart by Infotopics | Apps for Tableau: A graphical method of displaying multivariate data in a two-dimensional chart.

Radar Chart Viz Extension by Infotopics in Tableau. 

A radar chart is a visual tool that shows data across multiple categories using a spider-web-like graph. Each category has its own axis, and the value for each category is plotted along its axis. It's useful for comparing the performance or characteristics of different entities, like comparing the strengths and weaknesses of various products, team members, or strategies at a glance.

Radar by Actinvision: Display your data in a radar chart.

Radar Viz Extension by Actinvision in Tableau. 

With the Radar Viz Extension from Actinvision, simply display your data in a Radar Chart. Main features include advanced style configuration to fine tune every bit of the radar’s appearance parameters, color customization via a predefined color palette or color of your choosing and dot label configurations. 

Radial Chart

Radial Chart by Actinvision: Bar charts displayed on polar coordinate planes.

Radial Chart Viz Extension by Actinvision in Tableau. 

Used to show comparisons among categories by using circular shapes, the Radial or Circular Bar Chart simply refers to bar charts displayed on polar coordinate planes instead of a cartesian system.   To build your own, assign one dimension with categories to the Level mark and assign one or multiple measures to Value.   This extension is created and hosted by Actinvision and leverages all of Tableau’s interactivity. Please note the current version does not yet support formatting and keyboard navigation.

Sankey Charts

Sankey by Tableau: Show the flow of data between multiple categories 

Sankey chart Viz Extension in Tableau.

Sankey diagrams work best with a data set that shows a before and after state, or relationships/transitions between two or more categories. To build a viz with this extension, assign two to five categorical dimensions to the Level encoding box and assign one or more measures to the Link encoding box. This Viz Extension is created and hosted by Tableau and makes use of Tableau interactivity behaviors.

Radial Sankey by LaDataViz: Visualize interactions between sources and targets. 

Radial Sankey chart featuring The Dune Actors by Josh Preston.

This chart excels at displaying interconnections in a compact, circular layout, making it perfect for spotting patterns, strengths, and bottlenecks in data flows. This format saves space and enhances readability and engagement by offering a 360-degree view of the data interactions.

Sankey Diagram by Infotopics | Apps for Tableau: Visualize a flow of measure across multiple dimensions.

Sankey Diagram Viz Extension by Infotopics in Tableau.

The Sankey Diagram visualizes the flow of a measure across multiple dimensions. Start visualizing the flow of money through your organizations' departments or see what paths your visitors enjoy on your website. 

Recursive Sankey Diagram by Infotopics | Apps for Tableau: Visualize the flow of a set of data from origin to destination.

Recursive Sankey Diagram Viz Extension by Infotopics in Tableau.

A recursive (source-target) Sankey diagram is a visualization that displays the flow of a particular set of data from its origin (source) to its destination (target). It consists of nodes, representing the sources and targets, and links, indicating the flow between them. The width of the links corresponds to the quantity being measured. This type of diagram is often used to depict the flow of energy, money, or materials in a system, among other things. Source-target Sankey diagrams are useful for identifying patterns and bottlenecks in a system, as well as for demonstrating the impact of changes or interventions.

Circular Sankey Diagram by Infotopics | Apps for Tableau: Visualize the flow over multiple dimensions with internal loops.

Circular Sankey Diagram Viz Extension by Infotopics in Tableau.

The Sankey Diagram (circular) visualizes the flow of a measure over multiple dimensions with internal loops. Sankey diagrams are a type of flow diagram in which the width/height of the arrows is proportional to the flow rate.

Streamgraph Charts

Streamgraph by LaDataViz: Visualize changes and trends over time in a fluid and organic style.

Streamgraph chart featuring MPAA by Zach Bowders.

Streamgraphs provide a powerful way to represent time-series data in a more engaging and intuitive format than traditional line or area charts. Their flowing, stacked layers make it easy to understand the progression of multiple data series and see how individual categories contribute to the whole over time. 

Sunburst Charts

Sunburst (zoomable) by Actinvision: Visualize hierarchical data using concentric circles.

Sunburst (zoomable) by Actinvision Viz Extension in Tableau. 

The Sunburst Diagram is used to visualize hierarchical data using concentric circles. The centermost circle represents the highest hierarchy level, drilling down as it moves away from the center. This viz extension displays two levels at a time, allowing users to drill down by clicking a specific category.  

Sunburst Diagram by Infotopics | Apps for Tableau: Shows hierarchy through a series of rings, sliced for each category node. 

Sunburst diagram by Infotopics in Tableau. 

This type of visualisation shows hierarchy through a series of rings, that are sliced for each category node. Each ring corresponds to a level in the hierarchy, with the central circle representing the root node and the hierarchy moving outwards from it. Rings are sliced up and divided based on their hierarchical relationship to the parent slice. The angle of each slice is either divided equally under its parent node or can be made proportional to a value.

Zoomable Sunburst by Infotopics | Apps for Tableau: Represent data in a radial layout.

Visualization featuring the Zoomable Sunburst Viz Extension by Infotopics for Tableau.

A hierarchical visualization that represents data in a radial layout, resembling the layers of an onion or the annual rings of a tree. It consists of nested rings, where each ring represents a level of the hierarchy, with the innermost ring typically representing the root level and subsequent rings representing its child categories or subgroups. The size of each segment within the rings corresponds to the proportion of the data it represents, while the color or shading can convey additional information or categories. Sunburst diagrams are commonly used to visualize hierarchical data structures, such as organizational charts, directory structures, or hierarchical classifications, enabling users to explore the data's composition and relationships in an intuitive and visually appealing manner.

Table Visualizations

SuperTables by Infotopics | Apps for Tableau: Analyze and discover data to support your visualization with interactive tables.

SuperTables Tableau Extension by Infotopics for Tableau.

Empower your dashboard users with dynamic, Excel-like tables that offer the flexibility to organize and analyze data directly in Tableau. SuperTables allows users to group data, pivot tables, apply inline column filters, expand rows, and export to PDF, CSV, or Excel. This tool enhances your dashboards with added context and flexibility, supporting dashboard actions to ensure accurate calculations on aggregated data.

Table by Actinvision: A simple performance-friendly table display for data.

Tableau Viz Extension by Actinvision in Tableau. 

Simply display your data in a tabular way. This Viz Extension is performance-oriented and customizable. Easily adjust style settings to match your needs and integrate your dashboard design. Renaming columns and changing cell styles is also possible. 

Tree Diagrams

Tree Diagram by LaDataViz: Visualize hierarchical information.

Product Radial Tree by Tristan Guillevin.

Organize and display hierarchical information in a clear, engaging format that captures attention and facilitates deeper analysis. Tree diagrams are fantastic for breaking down complex systems into manageable parts. They help visualize structures, such as organizational charts, website sitemaps, or project tasks, allowing users to explore relationships and dependencies intuitively.

Voronoi Treemap by LaDataViz: Visualize hierarchical data through a unique and engaging Voronoi layout.

Voronoi Treemaps featuring Land Of Colors by Tristan Guillevin

Perfect for presenting nested data in a way that is both visually striking and informative, the Voronoi Treemap adds a fresh perspective to your Tableau dashboards. This chart type is particularly effective for displaying hierarchically structured data with varying levels of granularity.

DrillDownTree by Infotopics | Apps for Tableau: Visualize and aggregate data across multiple dimensions - drill down in any order.

DrillDownTree Viz Extension in Tableau.

The DrillDownTree provides better insights into your data through drill-down actions. It is a great data exploration tool for ad hoc analysis and conducting root cause analysis. Drill down on any measure to any dimension you wish and visualize data across multiple dimensions without setting up hierarchies upfront.

Venn Diagram

Venn Diagram by Infotopics | Apps for Tableau: A graphical representation used to illustrate the relationships between sets of data.

Venn Diagram Viz Extension by Infotopics in Tableau. 

A Venn diagram is a graphical representation used to illustrate the relationships between different sets or groups of data. It consists of overlapping circles or other shapes, each representing a set, with the overlapping areas representing the intersections or commonalities between the sets. Venn diagrams are commonly used in mathematics, logic, statistics, and various fields to visualize logical relationships, identify shared or distinct elements among different groups, and aid in problem-solving and decision-making processes.

Viz Extensions for Dashboard Optimization

Drill Down Filter by LaDataViz: Add an advanced dropdown filter menu. 

Drill Down Filter Viz Extension by LaDataViz in Tableau. 

Optimize your Tableau dashboards with the Drill Down Filter Extension, designed to enhance user interaction through an intuitive and easy-to-use drill-down filter menu. This extension simplifies the exploration of complex datasets by enabling dynamic filtering across multiple levels of detail directly within your dashboards.

WriteBackExtreme by Infotopics | Apps for Tableau: Collaborate directly with your Tableau dashboards. 

WriteBackExtreme Viz Extension by Infotopics in Tableau. 

Stay in the flow and stay in Tableau. Easily add collaboration functionality to any data point in your dashboard like a Category, Month of Order Date, or Manager.

PictureThis by Infotopics | Apps for Tableau: Create dynamic image table/cards based on your data. 

PictureThis Viz Extension by Infotopics in Tableau. 

Create dynamic image tables and grids that can be used as a filter in your dashboard, all within your familiar Tableau environment—governed and secured. Use Image URLs from your dataset that automatically create a responsive image table or grid. The images will act as a filter on another target sheet in your dashboard. Images are not stored in your dashboard, they will dynamically be lined up in an image table based on your data and can be used as a filter! The extension supports a table layout and cards layout and advanced layout options.

Waterfall and KPI Charts 

Gauge by LaDataViz: Instantly convey the status of metrics and KPIs.

Gauge Chart featuring Dynamic Targets by Rosario Gauna.

Gauges are ideal for displaying data that measure progress against a target or goal. They are an excellent tool for instantly conveying the status of metrics such as sales performance, completion rates, or any other metric where the value is compared to a predefined target. These charts provide a clear and straightforward visualization of progress in a visually engaging format that is easy to understand, making them perfect for executive dashboards and performance monitoring reports.

Waterfall Chart by Infotopics | Apps for Tableau: Understand the cumulative effect of sequentially introduced positive or negative values.

Waterfall Viz Extension by Infotopics in Tableau. 

A waterfall chart is a form of data visualization that helps in understanding the cumulative effect of sequentially introduced positive or negative values. These intermediate values can either be time-based or category-based. The waterfall chart is also known as a flying bricks chart or Mario chart due to the apparent suspension of columns (bricks) in mid-air. Often in finance, it will be referred to as a bridge. A waterfall chart can be used for analytical purposes, especially for understanding or explaining the gradual transition in the quantitative value of an entity that is subjected to increment or decrement. Often, a waterfall or cascade chart is used to show changes in revenue or profit between two time periods.

Waterfall & KPI Tree by DataMa: What's driving my variations? by DataMa: Discover the why behind your KPI variation.

Waterfall and KPI Tree Viz Extension by DataMa in Tableau. 

Enhance your data analysis with this Viz Extension, designed to unlock powerful insights into your key performance metrics. This tool excels in quick and intelligent root cause analysis, scoring and highlighting the most impactful drivers for your business. This enables you to make data-driven decisions with greater effectiveness. 

PowerKPIs by Infotopics | Apps for Tableau: Integrate strategic insights.

PowerKPI Viz Extension by Infotopics in Tableau.

Use PowerKPIs as the new central entry point for analysis in Tableau. It provides a powerful platform for illustrating the connections between different KPIs in an engaging grid layout or value driver tree, adding valuable context to data-driven decisions. From top-level summaries to detailed examinations of specific operational drivers, PowerKPIs enhances your ability to tell a compelling story of business performance through a single, intuitive interface.

Bullet by Actinvision: An ultra-customizable bullet chart.

Bullet Viz Extension by Actinvision in Tableau. 

The Bullet Viz Extension allows you to effortlessly display your data in a bullet chart, with powerful features for enhanced customization. With advanced Bullet style configuration, you can fine-tune every aspect of the chart's appearance, from margins to the direction of the bars. Additionally, the extension provides easy color customization, allowing you to choose from a predefined color palette or define precise colors for text, background, ranges, markers, and measures, giving you full control over your chart’s visual presentation.

Since the announcement of Tableau Einstein, we've been thrilled by all the enthusiasm and curiosity from our community, customers, and partners. Your excitement is palpable, and we're grateful for the outpouring of interest and feedback. As with any new innovation, we also understand that there are still many questions you have about the new platform, what it means for your organizations, the community, and careers.

We've gathered some of the most common questions about Tableau Einstein from our community to help provide some clarity—and we plan to update this post as more information becomes available. We appreciate you for sharing what you love about Tableau and partnering with us to continually improve the product for everyone.

What is Tableau Einstein and why now?

Tableau Einstein is a reimagined analytics experience: an open, composable, enterprise-grade data and analytics platform built on the Salesforce Platform and deeply integrated with Agentforce, a suite of customizable AI agents and tools. In today’s autonomous revolution, we have a unique opportunity to bridge the gap between siloed data and everyday workflows where people act on insights. This means we can harness AI to help tackle the most common and pressing data and analytics challenges—including fragmented data landscapes, a lack of trust in data, overlooked insights, and the reusability of analytics assets—in one platform.

Learn more about how we’re addressing these challenges in our blog, “What is Tableau Einstein?” and see Tableau Einstein in action by watching our keynote at Dreamforce 2024.

What are the benefits of Tableau Einstein being built on the Salesforce Platform?

Building Tableau Einstein natively on the Salesforce Platform allows us to offer a unique and truly unified analytics experience that brings together the best of Salesforce and Tableau technology. Tableau Einstein is the first BI platform with a workflow engine that spans the analytics journey from raw data, to a data lake, a semantic layer, a viz layer, and an action layer. As a completely open and API-first development platform, Tableau Einstein helps accelerate the development of analytics solutions and provides the ability to package up reusable, composable assets and offer them on a marketplace.

Salesforce’s Hyperforce cloud infrastructure provides world-class standards for security, compliance, agility, and scalability, backed by Salesforce’s continued commitment to privacy. With enterprise-grade governance and permissions, your analytics can reach a larger, global audience while meeting regional requirements for security and compliance. With a deep integration to Data Cloud, Tableau Einstein has the most modern data capabilities from zero-copy data ingestion to prep, data management, and semantics. Tableau Einstein is also deeply integrated with Agentforce and provides access to all of the latest no-code/low-code AI advancements and innovations.

Lastly, Salesforce CRM customers benefit from one place to centrally manage user permissions, administration, inherited security, and more.

Is Tableau Einstein valuable for me if I don't use other products from Salesforce?

Yes! Any organization can get more value from their data with Tableau Einstein through capabilities like data unification and orchestration, integrating practical insights where people work, and the ability to build, share, and reuse analytics assets and apps. And you don’t have to be an experienced Salesforce user to use Tableau Einstein.

For example, HR can analyze unstructured data from employee surveys with the help of an AI agent. A retail company can automatically forecast inventory risks, receive proactive alerts and relevant questions for exploring the data, and even take action from within Tableau Einstein, like submitting an inventory request to an ERP system. Because the platform is open and API-first, the possibilities are endless.

If Tableau Einstein is the future of Tableau, what happens to Tableau Cloud, Tableau Server, CRM Analytics, etc.?

Whether you have Tableau Cloud, Tableau Server, CRM Analytics, or any other analytics solution from Salesforce, you and your analytics assets are in good hands and will continue to be invested in. We have robust product roadmaps for each of these existing products today and will deliver on them—see all the latest capabilities we’ve released across the Tableau platform.

Remember that Tableau Einstein is going to be a journey as the vision rolls out; for many use cases today, our existing products will help you achieve your data and analytics goals. As we progress, our investments in Tableau Einstein will continue to drive innovation across these offerings. Additionally, Tableau Einstein is being built to work with existing products so that you can use both of them together as you progress in your journey.

Will existing products and Tableau Einstein work together? What happens to my Tableau assets?

Tableau and Tableau Einstein assets will be different, but compatible. For example, you’ll be able to work with Tableau Semantics from Tableau Einstein in Tableau Desktop. And starting soon you will be able to output your data from Prep Flows to Tableau Einstein's data layer DLOs. We are committed to offering tooling, guidance, and trusted partners to ensure your critical Tableau assets move forward. Helpful resources will be available soon.

Will Tableau Einstein be available for on-premises customers?

Tableau Einstein requires cloud-based deployment. That said, we are dedicated to supporting our existing on-premises customers via enhancements to Tableau Server so they can benefit from new innovations.

Does the Marketplace replace Tableau Public?

Tableau Public is here to stay as our community's free platform to connect and grow by exploring, creating, and sharing data visualizations about everyday topics of personal passion. We are excited to expand Tableau Public's capabilities to enable our community to create, share, and leverage reusable Tableau Einstein components through the new Marketplace.

What guidelines will there be around IP and data privacy for assets offered in the Marketplace?

IP protection and data privacy are critical not only for the marketplace but for the entire community. These are foundational principles in building Tableau Einstein. By developing Tableau Einstein natively on the Salesforce platform, we can leverage the same robust capabilities that independent software vendors (ISVs) use to ensure the security and integrity of their solutions on Salesforce. This makes IP protection and data privacy central to our strategy. Additionally, we are committed to further investing in advanced tools to prevent, detect, and address any potential issues, ensuring ongoing protection for all our users.

Can I use my own data lakehouse or semantic layer if I’ve already invested in another technology?

We understand that many organizations have made deep investments in other tools, and our roadmap includes connecting them to Tableau Einstein to allow you to use them together. Customers can use their own data lakehouse by leveraging Data Cloud connectors—this includes batch and streaming ingestion, zero copy, and bring-your-own-lake capabilities. We are working on the ability to translate an existing semantics layer from another system to reuse in Tableau Einstein. We are also working on opening the Tableau Einstein semantic layer completely, so that it can work with existing semantic layers in the market (like dbt).

Does Tableau Einstein support multiple languages for global teams who want to standardize and scale data sets, models, and workflows across geographies?

Tableau Einstein will support the 18 languages that core Salesforce products support, with initial English support for Insight Services and Packaged App Content, expanding after February 2025. Multi-currency is another area we are looking to support. 

How can I get started with Tableau Einstein today? What are the licensing and pricing options?

To get started with Tableau Einstein, Tableau+ is the best path forward as this offering includes many of the modular components of Tableau Einstein, available today. For all customers, this package includes everything you need to get started right now: Data Cloud for unified data modeling capabilities, Tableau Pulse to empower your teams with personalized metrics and digests, and Tableau Agent (formerly Einstein Copilot for Tableau) to begin your path with generative AI.

As we prepare to deliver licensing and pricing options in the near future, we are working diligently to ensure existing analytics customers have an affordable path to Tableau Einstein.