Predictive analytics is a powerful tool that transforms raw data into actionable insights, enabling data-driven decision-making. With Tableau’s predictive modeling functions, you can extend time axes, forecast trends, and identify future opportunities. This article walks you through creating predictions in Tableau and explores how to choose the right predictive model for your analysis.
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Forecasting is a powerful tool in analytics, allowing businesses to make data-driven predictions about future trends and performance. Tableau, a leading data visualization platform, provides robust forecasting capabilities that are easy to use yet sophisticated enough to handle complex data. By integrating statistical models directly into its visualizations, Tableau empowers users to forecast trends and display them in an intuitive manner.
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When working with Power BI line charts, you may want to display two data series with different scales using dual Y-axes (primary and secondary Y-axis). However, if the scales don’t match, it can make your chart confusing and potentially misleading. To ensure your data is accurately represented, synchronizing the Y-axes by setting consistent minimum and maximum values is key. This guide will walk you through the steps to synchronize the primary and secondary Y-axes specifically for Power BI line charts.
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Power BI slicers are powerful tools for making data more interactive and customizable within reports. They allow users to filter visuals on the dashboard by selecting specific criteria, making it easy to narrow down data for quick insights. In this article, we’ll dive into what Power BI slicers are, how to set them up, and how to sync them across multiple pages to create a seamless user experience.
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Power BI is a powerful tool for data visualization, and often, users integrate background images, icons, and visuals to create custom dashboards that elevate the storytelling behind the data. But what if you need to extract these images from a PBIX file? While Power BI Desktop doesn’t offer a built-in method for image extraction, there’s a clever trick that allows you to extract embedded images and backgrounds.
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If you’ve read my previous tutorial on creating Likert Scale and NPS scores in Tableau, you might have found yourself wondering: how can I achieve the same in Power BI? The good news is, Power BI offers a straightforward way to replicate this process while leveraging its powerful visualization and data modeling features.
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Be sure to check out my previous article on creating a Likert scale, as we’ll be using the same restaurant survey dataset. However, we won’t be using any of the calculated fields from the Likert scale, so feel free to skip it if you’d like. That said, the final dashboard will feature both the Likert and NPS charts side by side—they always pair well together, don’t you think?
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Surveys are powerful tools for gathering feedback and insights, with Likert scales and Net Promoter Score (NPS) being two popular methods for measuring attitudes, opinions, and customer satisfaction. Likert scales assess the level of agreement or disagreement on a statement, while NPS measures customer loyalty by asking how likely respondents are to recommend a product or service.
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