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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Geographic data is often stored in a Degree-Minute-Second (DMS) format, making it a bit tricky to convert into the standard decimal degrees (DD) required for Tableau’s mapping functions. In this post, I’ll walk you through how to transform DMS coordinates to decimal degrees directly in Tableau, step-by-step, so you can plot your geographic data without headaches.
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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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Ensuring data security and privacy is paramount when dealing with business intelligence and analytics. Tableau, a leading data visualization tool, offers robust features to manage data security effectively. One such feature is Row-Level Security (RLS), which restricts data access for different users based on their roles or attributes. This blog post will guide you through setting up Row-Level Security in Tableau using dynamic user filters.
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Creating a visually appealing and efficient Tableau report is crucial for making data-driven decisions. However, as your data grows, your reports might start to lag, leading to frustration and inefficiency. In this blog post, we will explore how to optimize your Tableau reports from three key perspectives: data sources, individual visualizations, and dashboards. By following these tips, you can ensure your reports run smoothly and deliver insights effectively.
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Following our discussions on Fixed and Exclude LODs in earlier articles, you should now be fairly comfortable with these concepts. Therefore, tackling Include LOD should be relatively straightforward. Let’s dive in without further ado.
In a prior article, I illustrated and demonstrated the process of converting Tableau’s fixed level of detail (LOD) to Power BI. In this piece, I will extend this effort by guiding you through the translation of the EXCLUDE LOD to Power BI DAX.
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If you’ve navigated Tableau’s powerful Level of Detail (LOD) function, you know its complexity and potential for creating impactful visualizations. Now, whether you’re transitioning from Tableau to Power BI or need to excel in both platforms with limited Power BI skills, fear not! In this series, we’ll unravel the mysteries of applying Tableau’s three main LOD functions in Power BI.
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