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
What is Tableau Next & Tableau Agentforce?
Tableau AI Agent Tableau’s Reinforce 2024 keynote delivered a powerful vision for the future of analytics, with a clear emphasis on artificial intelligence and a unified data experience. Over 60% of the presentation was dedicated to showcasing AI-driven capabilities, but the real highlight was a compelling demonstration of a unified solution leveraging Salesforce CRM, marking a significant step towards a one-stop shop for data-driven decision-making. The event commenced with a celebration of Tableau’s five-year anniversary, followed by a subtle yet crucial observation: the introduction of “Tableau Agent” beneath the familiar “Tableau Pulse.” This immediately sparked curiosity about the evolution of Tableau’s AI-powered assistance. https://youtu.be/-Iwf62vK88Q Addressing the Core Challenges of Modern Analytics The keynote candidly addressed the persistent challenges faced by data professionals. These included: Difficulty in Accessing Timely Insights: Decision-makers often struggle to locate the right data or dashboards when needed. Trust in Data and Insights: Users frequently question the accuracy and reliability of the data they encounter. Data Landscape Fragmentation: The growing complexity and fragmentation of data sources pose a significant hurdle. Lack of Reusability: Existing data assets, such as prep flows and visualizations, are often difficult to reuse and share effectively. These challenges underscored the need for a more integrated and intelligent analytics platform, paving the way for the unveiling of Tableau Agent and its capabilities. Tableau Agentforce Vs Tableau Next Tableau Next: This is the next-generation analytics platform by Tableau, built on the Salesforce platform. It integrates deeply with AgentForce to provide personalized, contextual, and actionable insights. Tableau Next enhances AgentForce by equipping agents with advanced analytics skills, enabling them to deliver data-driven insights through visualizations and semantic understanding. Check above Video to see Live Demonstration of Unified Analytics Power As per Tableau site, Tableau Next is an agentic analytics platform that transforms agile, self-service analytics in a modular way—with reusable and extensible components, semantic AI, and consistent data built into every workflow for every user, department, and industry. This gives your enterprise the ability to drive personalized and contextual analytics experiences and to take action on data within Salesforce and third-party applications in an intuitive way. The live demonstration showcased the power of Tableau’s unified solution in a real-world marketing scenario. Tomika, representing a marketing manager, utilized Tableau Pulse to monitor critical metrics like CSAT scores. A sudden dip in the score prompted deeper investigation, revealing regional disparities. The integration with Salesforce Data Cloud allowed for granular analysis, going beyond aggregated data. This enabled the user to examine individual customer interactions and identify specific pain points. Key Highlights of the Demonstration: Seamless Integration: The integration between Tableau and Salesforce Data Cloud facilitated a unified view of customer data from diverse sources. AI-Powered Analysis: Tableau Agent enabled natural language queries, providing instant insights and visualizations. Unstructured Data Analysis: The ability to analyze unstructured data, such as customer reviews, provided a comprehensive understanding of customer sentiment. Actionable Insights: The platform enabled seamless transitions to Service Cloud, facilitating immediate action on identified issues. The demonstration illustrated how the unified solution empowers users to: Gain a holistic view of customer data. Leverage AI for rapid analysis and insight generation. Effectively analyze both structured and unstructured data. Translate insights into immediate action. Accessing the New Features To access the advanced AI features, including Tableau Einstein, a Tableau Plus license is required. For Salesforce integration, Pulse for Salesforce is available. New users can explore Tableau through a free trial at tableau.com/trial, although it’s important to note that the trial version may not include all the advanced AI capabilities. Conclusion Tableau Reinforce presented a compelling vision for the future of analytics Tableau Next, driven by AI and a unified data experience. The showcased capabilities promise to empower organizations to make faster, more informed decisions, ultimately driving greater customer success. The integration with Salesforce CRM further solidifies Tableau’s position as a leading platform for data-driven insights. 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 Vs Tableau Agent (Co-Pilot) in 2024
Hello everyone! I’m Ritesh, and you’re watching “Dancing with Data.” This year, 2024, is all about artificial intelligence, focusing on Tableau Pulse, Tableau Co-Pilot which is now Tableau Agent (also known as Einstein Co-Pilot), and similar innovations from Power BI. I am not sure if it’s a very smart move from Tableau to change the brand name so frequently but let’s dive into the world of Tableau AI Tableau Pulse Vs Tableau Co-pilot Understanding Tableau Pulse A common misconception is that Tableau Pulse provides predictive or prescriptive analysis. However, it’s primarily descriptive, as stated in their official documentation. Powered by Tableau, Pulse is a reimagined data experience for business users. It helps business users get answers to some of the analytical questions they previously had to rely on data analysts for. Tableau Pulse is designed to provide a better experience for business users, enabling them to track key metrics efficiently. AI-Generated Insights: At the top of the Pulse homepage, you get an AI-generated summary of key changes in your metrics. You can see all the metrics you follow, how they’re trending, and any anomalies. Real-Time Insights: This feature is tailored for business users to make data-driven decisions, putting AI insights and personalized metrics right at their fingertips, whether they’re at their desk or on the go. Why Business Users Love Tableau Pulse Tableau Pulse is not in beta; it’s available for production use. Teams, including those in my company, have already started using it. It’s included with Tableau Viewer, Tableau Explorer, and Tableau Creator licenses. However, as of now, you won’t find the Co-Pilot or Einstein Co-Pilot features within Tableau Pulse. Introducing Einstein Co-Pilot Now, let’s talk about Einstein Co-Pilot and how it differs from Tableau Pulse. According to the definition, Co-Pilot for Tableau guides you through data exploration, helps uncover trends and patterns across data, and provides best practices for content creation within Tableau. This feature is more aligned with data analysts who create insightful visualizations for end-users. Data Exploration: Einstein Co-Pilot assists you in exploring data fast, surfacing recommended questions and chart types. Interactive Assistant: Ask Einstein Co-Pilot questions in natural language, and it returns results with recommended chart types applied. The more you explore, the more you learn how Tableau works, building lasting data skills. Data Preparation: Co-Pilot helps with data preparation, offering smart calculation assistance, making it easier even for beginners. Summary Einstein Co-Pilot: Focuses on more complex pieces of data analysis, right from data preparation to data data visualization Tableau Pulse: Geared towards business users for easy, direct access to data insights without needing a deep understanding of data analysis. Both features serve distinct personas and use cases, helping users make the most of their data. Try It Out Interested in trying Einstein Co-Pilot for yourself? Sign up for the beta at tableau.com/copilot. All relevant links, including how to register, will be in the description. For a detailed walkthrough, check out our video above, where I show you how to explore the difference 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
How to Enable Tableau Pulse: A Step-by-Step Guide
Welcome to Dancing with Data. Today, I’m excited to share my experience with Tableau Pulse. In this video, I’ll Walk you through how to enable Tableau Pulse, get access to a free trial version, and the precautions you need to take. This is the introductory segment of the Tableau Pulse tutorial series. Getting Started with Tableau Pulse This video talks about how to download & enable/activate Tableau Pulse. Important Note Tableau Pulse applies to Tableau Cloud, not the on-premise Tableau Server. This could potentially encourage Tableau Server clients to switch to Tableau Cloud, aligning with Salesforce’s cloud-based goals. Tableau Pulse vs. Power BI If you’re curious about the equivalent feature in Power BI, make sure to watch the video till the end. I’ll cover that as well. What is Tableau Pulse? Tableau Pulse is an AI-backed feature that provides personalized data insights about the metrics you follow. These insights are sent directly to users via Slack and email digests. If you want to learn more about your data, you can visit the Metrics Insight Exploration. Step-by-Step Guide to Enable Tableau Pulse Get Tableau Cloud: Visit the Tableau Cloud site and start a free trial. Submit Your Information: Click on the submit button and check your email to activate your Tableau trial. You have 48 hours to activate your trial. Activate Your Tableau Cloud Site: After your purchase is completed, Tableau will email an invitation to activate your new Tableau Cloud site. Click the activation link within 48 hours. If the link expires, you can request a new one. Deploy Tableau Pulse: Go to the settings and turn on Tableau Pulse. You can choose to deploy it for all users or a specified group. Final Thoughts Tableau Pulse uses generative AI to provide insightful information. In my next video, I’ll cover a demo of this feature. Don’t forget to check my website for more free and useful content related to Tableau and Power BI. The link is in the description. Conclusion Tableau Pulse is a powerful tool for descriptive analytics, similar to how doctors used to check pulses to analyze bodily systems. It checks various KPIs to provide comprehensive insights. I’ll also bring some real datasets to make more sense of generative AI in future videos. 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
