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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In the world of databases, SQL is a foundational language that serves as the backbone for interacting with relational databases. However, when delving deeper into SQL, you will come across terms like ANSI SQL and T-SQL, which may seem confusing at first. In this article, we’ll explore what SQL, ANSI SQL, and T-SQL are, how they differ, and their specific use cases.
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Power Query is a powerful tool used in Power BI, Excel, and other Microsoft products to clean, transform, and load data. While it simplifies data preparation, users often encounter various errors that can disrupt workflows and hinder progress. Understanding these errors and knowing how to resolve them is crucial for any data professional. In this blog post, we will explore some common Power Query errors, their causes, and practical solutions to fix them.
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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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Data warehousing and business intelligence are crucial for modern enterprises, enabling them to make informed decisions based on historical data. One key aspect of data warehousing is handling changing data over time, especially when tracking historical changes in dimensions. This is where Slowly Changing Dimensions (SCD) come into play. In this blog post, we’ll delve into what SCDs are, the different types of SCDs, and how to implement them effectively.
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In today’s fast-paced business environment, efficiency and productivity are paramount. Microsoft Fabric, a robust and versatile platform, has introduced a new feature aimed at enhancing workflow management: Task Flow. This feature promises to streamline processes, reduce manual intervention, and ultimately, boost productivity. In this comprehensive guide, we will explore the ins and outs of Task Flow, its benefits, and how you can leverage it to optimize your operations.
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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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Regular Expressions, or Regex, are not unique to the Business Intelligence (BI) world, but they are something I frequently encounter and often find puzzling. I wish I had a handy cheat sheet whenever I need to use Regex in my formulas, but I can never find a concise and helpful one through Google searches. So, I decided to create one for people like me who don’t necessarily struggle with Regex but are often annoyed by its complexity.
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