NEW 2024.2|Multi-fact Relationship Data Models -Tableau

Multi-fact Relationship Data Models -Tableau Tableau’s latest data model introduces a pivotal enhancement for advanced multi-dimensional analysis, addressing a long-standing challenge in managing intricate data relationships. This feature empowers users to dissect data from multiple perspectives, overcoming previous limitations associated with shared dimensions. https://youtu.be/DmRxOO6zN_U The Challenge of Multi-Dimensional Analysis Consider a scenario common in incident and case management: you have core tables for “Customers” and “Products,” and you introduce a “Support Cases” table. This new table links to both “Customers” and “Products” via their respective IDs, enabling analysis of support cases by customer and by product. Historically, Tableau encountered difficulties with this type of scenario. Establishing direct relationships between “Support Cases” and both “Customers” and “Products” often resulted in conflicts, hindering the ability to perform simultaneous, accurate analyses. This limitation stemmed from the complexities of shared dimensions across multiple relationships. RHS Image: Tableau Conference Illustrative Example: Premium Users and Returns Analysis To illustrate this challenge and its solution, let’s employ the familiar Sample Superstore data, augmented with a “Premium Users” table. This table contains order details specific to premium customers, including order IDs, regions, and serial numbers. We also have “Returns” and “Users” tables, providing return details and manager information, respectively. Our objective is to analyze returned orders by premium customers while simultaneously identifying the managers responsible for those customers. Watch video for step-by-step solution Traditional Approach and Its Limitations Initially, establishing a direct relationship between “Premium Users” and “Returns” allows us to identify returned orders from premium customers. However, attempting to simultaneously relate “Users” to “Users” for manager identification leads to data inconsistencies. This arises from conflicting interpretations of the relationships, resulting in inaccurate results. The Solution: Leveraging Base Tables The solution lies in leveraging Tableau’s new “base table” functionality. By designating “Users” as the base table (akin to a central fact table), we can establish independent relationships with both “Returns” and “Users.” This approach eliminates conflicts, ensuring accurate analysis across multiple dimensions. Implementing Base Tables for Accurate Analysis Specifically, we establish a relationship between “Premium Users” and “Returns” to analyze returned orders. Subsequently, we establish a separate relationship between “Premium Users” and “Users” to identify the corresponding managers. This separation of relationships, anchored by the base table, ensures data integrity and accurate results. Benefits of Base Tables for Multi-Analysis This enhancement significantly improves Tableau’s capacity for complex multi-analysis. By strategically employing base tables, users can effectively manage intricate data relationships and gain deeper insights from their data. Practical Application and Exploration This example is available on Tableau Public, allowing you to explore the data model and practice this advanced technique. Experiment with the data and observe how the base table functionality facilitates accurate multi-dimensional analysis. Call to Action: Sharing Insights and Experiences Reflect on your past Tableau projects. Have you encountered similar challenges with complex relationships? How do you foresee utilizing this new base table functionality? Share your insights and experiences in the comments. Additionally, please suggest topics for future discussions on Tableau’s evolving features. Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

Tableau AI Agent: Beyond Text to Viz – Unlock the Power of AI in Tableau- using IPL dataset

Tableau AI Agent:  Unlock the Power of AI in Tableau- using IPL dataset The Indian Premier League (IPL) is a whirlwind of cricketing action, with spectacular catches, blistering boundaries, and nail-biting finishes. But when the dust settles, who truly emerges as the Most Valuable Player (MVP)? Is it simply the player with the most “Man of the Match” awards, or is there a deeper story hidden within the data? In this blog post, we’ll embark on a data-driven journey to uncover the MVPs across IPL seasons from 2009 to 2017. And we’ll do it all without writing a single line of code, thanks to the magic of Tableau Agent. https://youtu.be/9Zzu_0kvvi4 Our Data: A Cricketing Treasure Trove We have a rich dataset at our disposal, featuring every IPL player who has won at least one “Man of the Match” award. This dataset includes a wealth of information, including: Player Name Team Names Match Winner City Date Venue Scores Runs Wickets Taken And much more… This granular data allows us to delve deep into player performance and unearth valuable insights. Tableau Agent: Your AI-Powered Data Analyst Tableau Agent is a revolutionary tool that allows you to analyze data using natural language. Simply ask questions, and Tableau Agent will generate insightful visualizations in response. It’s like having a data analyst at your beck and call, but without the need for coding expertise. Uncovering the MVPs: A Step-by-Step Guide   First Season: We begin by asking Tableau Agent to determine the first season in which each player bagged a “Man of the Match” award. This gives us a baseline for their career trajectory. Number of Awards: Next, we ask Tableau Agent to calculate the total number of “Man of the Match” awards each player has accumulated. This gives us a sense of their overall impact. Career Span: To understand how consistently players have performed over time, we ask Tableau Agent to calculate the difference between the first and last seasons in which they won an award. Average Awards per Season: To account for varying career lengths, we calculate the average number of awards per season for each player. This normalizes the data and allows for fairer comparisons. Filtering for Consistency: Finally, to identify the most consistently valuable players, we filter for those with an average of more than two awards per season. This highlights the players who have consistently delivered exceptional performances.         Insights from the Data: Unexpected Twists and Turns Our analysis reveals some fascinating insights: Andre Russell’s Rise: Despite playing only a few seasons, Andre Russell emerges as a top contender but why was he disappeared. Manoj Tewari Decline:  After a strong start, experienced a dip in performance after 2011. This prompts further investigation into the factors that may have contributed to this decline. James Faulkner’s Enigma: James Faulkner, once a dominant force, faded after the 2015 World Cup. This raises questions about the impact of major tournaments on player form and longevity.   Watch full video to find the “Why” factor? The Power of No-Code Analysis This analysis demonstrates the power of Tableau Agent to unlock valuable insights from data without requiring any coding skills. It democratizes data analysis, making it accessible to a wider audience. This is just the beginning. With Tableau Agent, the possibilities for exploring IPL data are endless. We can delve into team performance, batting and bowling trends, and much more. The power to uncover hidden stories within the data is now at your fingertips. So, dive in, explore, and discover the fascinating world of IPL data analysis with Tableau Agent. Who knows what other secrets you might unearth? Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

When to use Blending Vs When to use Joins in Tableau?

Data Blending -When to Blend & When to join Tableau offers powerful ways to combine data from different sources, and two key methods are cross-DB joins and data blending. While both allow you to integrate data, they differ significantly in their approach and have distinct use cases. This blog post will delve into the intricacies of each method, helping you understand when to use which, and why data blending remains relevant even with the availability of cross-DB joins. https://youtu.be/HKfVW6eJlMk Understanding Joins Before we dive into Tableau specifics, let’s recap what database joins are. In SQL, a join clause combines data from multiple tables based on common columns. Various types of joins exist, each serving a different purpose: Inner Join: Returns rows only when there’s a match in both tables. Left Join: Returns all rows from the left table and matching rows from the right. Right Join: Returns all rows from the right table and matching rows from the left. Full Outer Join: Returns all rows from both tables, regardless of matches. And a few more specialized types. Understanding these foundational concepts is crucial when working with data in Tableau. Data Blending in Tableau Data blending in Tableau is a powerful feature that allows you to combine data from multiple sources directly within a visualization at the sheet level. This is particularly useful when your data resides in different databases or formats. Blending happens on the fly, linking data sources based on common fields. Data blending offers flexibility when combining disparate datasets, making it a handy tool for creating rich visualizations without heavy data integration processes. Aggregation-Based Combination: Tableau aggregates data from each source first, then combines it based on shared dimensions. This method prevents the duplication issues often seen with cross-database joins. Cross-DB Joins vs. Blending: When to Use Each While both methods combine data, they have distinct applications: Cross-DB Joins: Ideal when your data sources are relational databases that Tableau supports for cross-database joins. Efficient for combining data at the same level of granularity. Offers more flexibility in terms of join types and calculations.   Data Blending: Essential when dealing with data sources that don’t support cross-DB joins, such as cubes or Salesforce.com. Crucial when combining data with different levels of detail (e.g., city-level data with state-level data). Can be more efficient than joins for very large datasets. Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

Tableau Pulse 2025.1 Features

Tableau Pulse 2025.1 New Features Get ready Tableau enthusiasts! The latest updates to Tableau Cloud are here, and they’re all about supercharging your data analysis experience. Tableau 2025.1 is just around the corner, bringing a host of exciting enhancements,especially for Tableau Pulse. If you’re eager to make your data analysis more intuitive and insightful, you’ve come to the right place. Let’s explore the key features that will revolutionize how you work with data in Tableau. https://www.youtube.com/watch?v=7sPC48pmZ10 Visual Insights at a Glance Visual Insights at a Glance: Period Change in Breakdown Chart: Finally! Tableau Pulse now displays percentage changes right next to the overall value in breakdown charts. This makes it incredibly easy to spot trends and fluctuations. This feature is generally available, so you can start using it right away. Drill Down from Breakdown/Insights Chart: Need to dig deeper? Now you can! By clicking on a specific item or region in an insights chart, you can drill down to view more granular details. For example, if you see a spike in sales in Japan, you can instantly explore the underlying data. This feature is also generally available. Turn Off Expected Range: Sometimes, you want to focus on the raw data without the expected range cluttering your view. Tableau Pulse now allows you to easily toggle off unexpected values directly in the metric definitions. This keeps your insights clean and focused. Enhanced User Experience: New Mobile Homepage: The mobile experience just got a major upgrade! You can now see at least three metrics at a glance on your mobile homepage, providing a quick overview of your key data points. Customizable Corner Styles: Personalize your Pulse metrics with the new corner style options. Choose between square and rounded corners to match your aesthetic preferences. Link Related Content: Add up to five links to your core definitions, allowing you to provide additional context and resources. This feature is generally available. Slack Integration Enhancements: If you’re a Slack user, you’ll love the new grouping and sorting capabilities within the Slack digest, making it easier to manage and organize your Tableau Pulse notifications. Time Range Grouping: Analyze your metrics within specific time frames with the new time range grouping feature. This allows for more targeted analysis and reporting. This feature is generally available. Conclusion These updates to Tableau Pulse represent a significant step forward in making data analysis more accessible and actionable. The visual enhancements, improved mobile experience, and data-driven goal tracking all contribute to a more intuitive and efficient workflow. The enhanced Q&A, while a premium feature, has the potential to revolutionize how we interact with our data. I’m particularly excited about the period change in breakdown charts and the drill-down capabilities. These features make it easier to identify trends and explore the underlying data, ultimately leading to better decision-making. and efficient workflow. The enhanced Q&A, while a premium feature, has the potential to revolutionize how we interact with our data. I’m particularly excited about the period change in breakdown charts and the drill-down capabilities. These features make it easier to identify trends and explore the underlying data, ultimately leading to better decision-making.   Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

Explore Metrics with Tableau

Exploring Metrics with Tableau Pulse: A Comprehensive Guide Hello everyone! I’m Ritesh, and you’re watching “Dancing with Data.” This is the third and final video in our introductory series on Tableau Pulse. If you haven’t seen the first two videos, I highly recommend watching those before diving into this one. You can find the links to those videos in the description and the comment section. Series Recap Day 1 Video: We covered how to enable Tableau Pulse from scratch, even if you don’t have a license. We walked through obtaining the Tableau free trial version. Day 2 Video: We discussed how to create metrics with Tableau Pulse. If you’re new to Tableau Pulse and just joining us, be sure to start with these foundational videos to get the most out of today’s session. Today’s Focus: Exploring Metrics with Tableau Pulse Now, let’s dive into how you can explore metrics with Tableau Pulse. Homepage Overview Once you’ve enabled and created Tableau Pulse, you’ll find that Tableau allows you to make data-driven decisions and provides insights about the metrics you follow. There is an option for users to follow metrics. Only certain roles such as Creator, Site Administrator Explorer, or Explorer (can publish) can create metric definitions. However, all users can follow and interact with metrics. These metrics use the core definition plus optional filters to scope the data for different audiences and purposes. Once you follow any metric, insights about your data are delivered directly to you via email or Slack, but you need to configure this first. Navigating the Homepage (check the above video for the demo) Here is how you can navigate through the Tableau Pulse homepage: Follow Metrics: By default, you’ll see all the metrics you’re following. To follow a new metric, go to the “Browse Metrics” section. You can also search for a specific metric by name and follow it. Add Followers: You have the option to add followers to any metric you are following. Exploring Metrics To explore a metric of interest: Instant Insights: From the top to bottom, you can view instant insights available to you.(check the above video for the demo) Breakdown by Dimensions: The breakdown is available with respect to different dimensions. You can switch dimensions on the fly from one to another, like from Brand to something else, and ask questions relevant to that dimension. Adjusting Metrics: To adjust your metrics, click on “Adjust” on the top left-hand side. This will help you change the metric’s time period or apply filters. For example, you can change from month-to-date to week-to-date, or select a specific brand you are interested in.(check the above video for the demo) Setting Preferences To set your preferences: Preferences: Click on the human icon at the top right-hand side, then click on preferences. Here, you can choose whether you want to receive notifications via Slack, email, or both. You can also set the frequency—weekly, daily, or monthly. Save your settings once done. Integrated Insights: Once you’ve set your preferences, you’ll receive messages and insights through your chosen channels. For example, you can see how beautifully it integrates with Slack, providing insights as per the schedule you set. Remember, email and Slack digests are sent to the email associated with your Tableau account. If your Tableau Pulse test wasn’t ready in time, you always have access to your default Tableau Pulse page to get insights from your homepage. If a metric you followed is no longer in your Slack digest or email, it means the metric was deleted. Final Thoughts Watch the Video For a detailed walkthrough on how to explore metrics, check out our video above. It provides step-by-step instructions to help you explore Tableau Pulse metrics effortlessly. Stay tuned for more tips and tricks on mastering Tableau Pulse! Keep dancing with data! Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

Create Metrics with Tableau

Unlocking Insights with Tableau Pulse Metrics Welcome to another episode of Dancing with Data! If you missed Day 1, be sure to check out that video where we set up our Tableau site from scratch and installed the trial version of Tableau Cloud. Today, we’re diving deeper into the world of Tableau Pulse to show you how to create and manage metrics that will keep your data-driven decisions sharp and insightful. What is Tableau Pulse? Tableau Pulse provides insights about your data based on metrics you define. Once you’ve created a metric, you can add members of your organization as followers. They’ll receive regular email or Slack digests about their data. These digests surface trends, outliers, and other changes, keeping followers up to date on data relevant to their work. Users can investigate a metric on Tableau Cloud to see how different factors contribute to changes in the data, providing the information they need for data-driven decisions without needing to perform complex analyses in Tableau. Pulse Home Page: Getting Started Behind every metric in Tableau Pulse is a metric definition. Metric definitions specify the core metadata for those metrics, and viewers interact with the metrics to get insights. Parent-Child Relationship Between Definitions and Metrics Metric Definition: The set of metadata that functions as the single source of truth for all metrics based on it. Defined by users with roles like Creator, Site Administrator Explorer, or Explorer (can publish). The following table provides an example of the metadata captured by a metric definition. Metric: The interactive objects that sit in front of a definition. Created when users adjust filters or time options. Users follow and explore metrics to get insights. The following tables provide examples of the options configured for metrics. These options are applied on top of the core value specified by the metric definition. Creating Metric Definitions and Metrics To get started in Tableau Pulse, you create a metric definition that captures the core value you want to track. At its most basic level, this value is an aggregate measure tracked based on a time dimension. The definition also specifies options such as the dimensions that viewers can filter by, the way the value is formatted, and the types of insights displayed. When you create this definition, Tableau automatically creates an initial metric and sends you to that metric’s page. The initial metric created for a definition has no filters applied, but anytime you or another member of your organization adjusts the metric filters or time options in a new way, Tableau Pulse creates an additional metric. Managing Metrics for Your Organization People in your organization follow metrics, not metric definitions. By following individual metrics, they get insights specific to the dimensions that matter to them. The definition exists to let you manage the data for metrics from a single parent object. If a field in your data source changes, you can update the definition to reflect this change, and all metrics based on that definition will also reflect the change. Real-World Example Imagine you’re a member of a sales organization needing to track metrics across different territories and product lines. In Tableau Pulse, you would: Create a metric definition that includes the core value of the sum of daily sales with adjustable metric filters for region and product line. Create metrics for each region and product line. Add members of your organization as followers to the metrics relevant to their areas. Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

Tableau vs Power BI in 2024: A Comprehensive Comparison

Tableau vs Power BI in 2024: A Comprehensive Comparison Today, we’re diving into a hot topic: Tableau vs Power BI in 2024. As someone who holds the prestigious titles of Tableau Ambassador and Power BI Super User, I’m uniquely positioned to provide insights on this subject. Let’s explore these tools from a neutral perspective, using the latest Gartner Magic Quadrant report as our guide. The Gartner Magic Quadrant 2024 The Gartner Magic Quadrant provides a graphical representation of the positioning of various tools in the market. According to the 2024 report, Power BI is ahead of Tableau and other competitors. This shift is significant and worth exploring further. My Journey with Tableau and Power BI In 2024, we see more tools entering the leaders’ quadrant, which used to be dominated by Microsoft Power BI, Tableau, and Qlik. This increased competition is a challenge for Tableau. When it comes to job opportunities, a quick search reveals that there are 7,638 jobs for Microsoft Power BI in India, compared to 4,632 for Tableau. While not foolproof, this indicates a higher demand for Power BI skills in the current job market. Choosing the Right Tool If you’re deciding between Tableau and Power BI, it’s essential to adopt a neutral perspective. Start by working with each tool for a week and see which one you enjoy more. Your preference will play a crucial role in your decision. However, if your company already uses a specific tool, your choice is made for you. Ease of Use For me, Tableau was easier to use, primarily due to concepts like addressing and partitioning, and the order of operations. These features allow you to perform complex calculations without writing extensive code. For example, calculating the percentage of total sales for different regions is straightforward in Tableau, thanks to its addressing and partitioning capabilities. In contrast, Power BI requires writing DAX for similar tasks. Innovation and Visualization Tableau offers more flexibility and innovation in visualizations. With its rows and columns shelves, you can create a wide range of visualizations, from area charts to Sankey diagrams. This flexibility is unmatched by Power BI, which tends to be more rigid in its visualization options. However, Power BI does offer unique visualizations like gauge charts, which require lengthy calculations in Tableau. Conclusion Both Tableau and Power BI have their strengths and weaknesses. Tableau excels in ease of use and flexibility, while Power BI offers unique visualizations and a higher demand in the job market. Ultimately, the best tool for you depends on your specific needs and preferences. Whether you choose Tableau or Power BI, mastering either tool will open up numerous job opportunities. Thank you for joining me on “Dancing with Data.” Stay tuned for more insights and comparisons in the world of data analytics! Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

Exploring Tableau’s Vision and the Future of Analytics

Hello everyone, my name is Ritesh, and you’re watching “Dancing With Data.” Today, we’re diving into the Tableau keynote video from Reinforce 2024. This session is packed with insights about Tableau’s vision and the future of analytics, with over 60% of the time dedicated to AI. But that’s not all—they also showcased a very insightful demo, which I’ll walk you through, highlighting a unified solution that leverages Salesforce CRM. Celebrating Five Years of Innovation The keynote kicked off with a celebration of Tableau’s five-year anniversary—congratulations to the team! One of the standout announcements was the introduction of Tableau Pulse and Tableau Agent. Curious about what Tableau Agent is? Let’s delve into it. The Need for Tableau Agent Have you ever been in a situation where you needed to make a quick decision, but couldn’t find the necessary data or dashboard? This is a common challenge many of us face. Often, insights are overlooked or ignored because users can’t trust the data or the insights derived from it. Additionally, the data landscape is becoming increasingly large and fragmented, making it harder to manage and utilize effectively. Addressing Common BI Challenges Tableau Agent aims to address these issues by providing a reliable and unified solution. It helps users find the right data quickly, ensuring that the insights are trustworthy. Moreover, it tackles the problem of reusability in the BI ecosystem. Whether it’s a prep flow or a visualization, Tableau Agent makes it easier to reuse and monetize what you build. Live Demonstration To give us a better understanding, let’s move on to the live demonstration. Please check the above video for detailed analysis Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

How ChatGPT solved end to end Tableau Use case?

ChatGPT ChatGPT is an AI chatbot that can respond well to human questions. It has lately been all over the internet with examples of people pushing the limits, anticipating who it will fire, and marveling at its capacity to develop sophisticated solutions. ChatGPT creating a bar chart with a reference line also solved the scenario with the condition with the calculated field as well using Windows function or say fixed LOD functions as well and it gave me almost end to end and that includes the addressing and partitioning as well in some of the cases it won’t satisfy but almost end to end. So, how knowledgeable is ChatGPT when it comes to Tableau? Watch the video below let’s see how much it can solve right. Question: I’m using Tableau and I’ve created a bar chart to measure sales at the row shelf and dimension category and sub-category at the column shelf how can I create a reference line which can show average sales within each category and color the bars above this line with a different color? SoI just posed this question chatGPT and let us see what is outcome Wow! it was quick enough to give me the step-by-step instructions within seconds. Now we need to check the validity and correctness of the given steps. Let us try to just follow the steps: Step 1: Let’s open Tableau and do it from scratch.Drag the category and subcategory in the column shells and also drive the sales in a row. Step 2: Right-click on the “Sales” measure on the “Row” shelf and select “Add Reference Line”. The average option under the value and the scope is per pane. Step 3: A calculation (above average) with Windows function. Right-click anywhere in the “Data” pane and select “Create Calculated Field”. In the calculation editor, enter the following formula. SUM([Sales]) > WINDOW_AVG(SUM([Sales])) This formula compares each bar’s sales value to the average sales value within each category. Click “Apply” and then click “OK” to create the calculated field. Step 4: Click on the calculation (above average). Select the compute using and then click the pane(across). Everywhere crossing the average line its color is orange otherwise it’s blue. So, the final View with chatGPT By following these steps, you should now have a bar chart with a reference line showing the average sales within each category, and the bars above the average line will be colored differently, making them visually distinct. This visualization can help identify sub-categories with sales above or below the average more easily. Tableau Workbook https://public.tableau.com/app/profil… This is my website dancing with data.com where I talk about data analytics with power bi and Tableau. This post was helpful for you and will help you to know about chatGpt solved the Tableau problem end to end. Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp

Custom Reset button with Tableau

While working with numerous filters, a reset button comes in helpful to rapidly return to the initial position or set of filters. With Tableau, you have to create a separate sheet over there itself and then use action.Please click the link below to download the reset button, and you can follow along with me while I also give you the test workbook. Please feel free to download the workbook & icon from here Let’s start from scratch. Step 1:  Create a new sheet and name it “Reset”. The Next Step would be to hide this. Step 2: Click on the Shape card and select a shape you want to display for your Reset button. Select the reset icon for this example Adjust the size using the Size card. Step 3: Select and move the reset button Go back to my dashboard which is the emission environment and pull my reset button there. Make it float and pull to the right-hand side Step 4: Readjustment So, firstly hide the title because the icon is self-sufficient so I’m just hiding this header. Make it float and pull to the right-hand side To remove the row lines, click on format. Inside the format borders row, make none to row divider. Step 5: Now the final part is to create a dashboard Action to create the reset functionality. 1.  From the menu, select Dashboard/Actions 2. Add Action of type filter with the following settings. In the Target Filters, select Add Filter and add the fields for which you want to reset the filters.(all the filters in the dashboard) You will get a warning “Missing fields on Reset”, you can ignore it. Using Actions on the dashboard will conclude all the steps. Now clicking on the reset button next to the filters will reset all the filters at the same time. This technique can be used to reset any number of filters. Step 6: Workbook publishes to the public So, firstly go to the server and click on this and go to the published workbook. I am publishing this to the public so that you can make full use of it. Here is the link Ritesh Bisht Founder of Dance & Sing with Data “Ritesh is 2 times Tableau Ambassador & 1 time Power BI Super User from India and has been featured in the Top 15 Tableau & Power BI World Communities” Found me on: Linkedin Twitter Youtube Whatsapp