Master Tableau in Minutes: Essential Tips & Tricks

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Be Inspired by Data

Tableau is one of the most helpful data visualization tools and offers many cool charts and graphs. It is easy to use and transforms complicated data into uncomplicated visuals. While visualizations have an important role to play when communicating data insights, achieving successful communication involves a more intricate process. As you dive deeper into Tableau's functionalities, enhancing the performance of your dashboards and discovering advanced techniques becomes essential to maintaining the attention of your audience and delivering impactful visualizations. However, there are some best practices which can enhance visual representations and allow your users to guide themselves through the dashboards in a predictable and logical way.

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Let’s take a deep dive into exploring these tips and tricks that can certainly super-charge your work.

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1. Data Blending

Tableau has emerged as a powerful tool for data blending, offering the ability to pull data from a variety of sources, including live and in-memory data sources, data warehouses, the cloud, big data platforms, spreadsheets, and both relational and non-relational databases. With its data blending feature, Tableau enables users to create a 360-degree view of their business by combining data from different areas, such as customer information, financial metrics, sales and inventory reports, search engine optimization (SEO), digital marketing metrics, and social media analytics.

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The steps for data blending are as follows.  

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• Connect your workbook to at least two or more data sources.
• Identify primary and secondary sources: drag a field from one data source to your workbook, making it the primary source. Add a field from another source to the same sheet to establish it as the secondary source. A linking icon will show the blending fields.
• Ensure multiple sources: Add new data sources via Data > New data source, ensuring they're listed separately in the data pane for blending.

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The steps to initiate blending are as follows.

• Set a primary data source by dragging its field to the view.
• Switch to another source and ensure a blending relationship exists.
• Look for a linking field icon. If present and without a slash, the sources are linked. If absent, adjust blend relationships.
• Drag a field from the secondary source to the view to complete the blend. The primary sources are marked with a blue check, and the secondary with an orange check, indicating their use.

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2. Order of Operations in Tableau

To understand how Tableau works to present the views, it helps to know which steps the tool takes when working with filters and how it manipulates data.  At the same time, it is essential to know the order of operations when using any kind of filter or level of detail (LOD) calculation. For instance, you might apply a filter expecting it to narrow down your dataset before aggregation, only to find it applied after, altering your results unexpectedly. Knowing the order of operations allows you to manipulate these steps to your advantage, ensuring your views accurately reflect your intentions.

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The diagram below and the definitions that follow will make this easier to comprehend.

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Source:https://help.tableau.com/current/pro/desktop/en-us/Img/order_of_operations_overall.png

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• Extract Filters or Data Source Filters:  These let you pick and choose what data gets into Tableau right from the start. It is akin to deciding what groceries to bring home before you start cooking. For example, if you don't need information about France for your project, you can filter it out before you even begin making your charts.

• Context Filters: Consider these as special filters that you tell Tableau to pay attention to first. They are like VIP guests at a party: they get priority. If you're working with categories like product types or countries, you can set these filters to make sure Tableau sorts through this information before anything else.

• Sets, Conditional and Top N Filters, and Fixed Level of Detail Calculations: These come into play before regular filters. They are similar to setting up the main decorations for a party before adding the small touches. They help you compare things or focus on the top performers, like seeing which products sell the best.

• Dimension Filters: These are your everyday filters that narrow down your data to the bits you're interested in, like picking a playlist for your party.

• Include and Exclude Level of Detail Calculations:  They are a bit more specific about what detail to pay attention to in your data, helping you fine-tune the information your charts are based on, without needing those VIP filters.

• Measure Filters:  To adjust your data based on numbers, e.g., only showing products that sold more than 100 units. After all the filtering and sorting, Tableau calculates "Totals, Forecasts, Table Calculations, Trend Lines, and Reference Lines" to wrap up your view, adding finishing touches like summing up sales or predicting trends.
  

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3. Establish a Data Strategy to Drive Performance through Extracts

Tableau is a tool that helps people see and understand their data better. Think of data extracts like taking a picture of your data, these extracts act as a compressed, memory-stored "snapshot" of your data so you can look at it quickly anytime. This is extremely helpful because it lets you work with big amounts of data fast, without having to wait a long time for the computer to go through all the data every time you ask a question.  

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The benefits of using extracts in Tableau are as follows.

• Performance: when you use data extracts in Tableau, your work can get faster, especially if your original data is slow to access or if you've been using complex custom SQL commands that slow things down.
  
• Reduced load: if you've got a lot of people using Tableau to look at data from the same database, it can put a lot of pressure on that database. Switching to an extract (a kind of saved snapshot of your data) means your database won't have to work as hard.

• Easy to share: you can package your Tableau visuals along with the extract into a single file. This makes it super easy to share your work with others.

• Choosing your summary level: you can decide how detailed you want your summaries in the extract to be – by month, quarter, or year. This makes the extract even smaller and faster to work with.

• Calculated fields get a boost: if you've got calculations in your work, turning them into part of your extract makes them run faster since Tableau doesn't have to do the math every time you use them.

• Works well online: Tableau's online platforms prefer using these extracts because they're faster and more efficient than working with the raw data.

• More features with extracts: older versions of Tableau had limitations with certain data sources, like Excel or Access. Extracts let you do more with these types of data, like unique counts, which weren't possible before.
  

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4. Level of Details (LOD) Expressions in Tableau

LOD expressions are somewhat like having a superpower in your data analysis toolkit. They allow you to perform calculations across different levels of detail within your data, all within a single visualization. Whether you need to drill down into granular details or zoom out for a broader overview, LOD expressions are your go-to solution.

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There are three types of LOD expressions.  

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• INCLUDE: This calculation adds more granularity to your analysis. For example, if you're looking at overall sales but also want to consider the impact of individual sales representatives, INCLUDE lets you do that.
  Example: { INCLUDE [Customer Name] : SUM([Sales]) } lets you add customer-level detail to your analysis.

• EXCLUDE: When you need to remove certain details to get a clearer picture, EXCLUDE is your friend. This can help when you want to focus on broader trends without getting distracted by outliers or exceptions.
  Example: { EXCLUDE [Region]: SUM([Sales]) } helps you focus on everything but regional sales to analyze broader trends.

• FIXED: This type of LOD expression allows you to set a specific level of detail that remains constant, regardless of other factors in your analysis. It's particularly useful for setting benchmarks or comparing across different segments.
  Example: { FIXED [Region] : SUM([Sales]) } calculates sales totals per region, unaffected by other dimensions in your view.
  

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The introduction of LOD expressions marked a significant evolution in Tableau's calculation language, simplifying complex data questions and offering a new level of analytical depth. They empower users to:

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• Explore data at multiple levels of granularity in a unified view.
• Conduct more sophisticated analyses without needing extensive data prep or restructuring.
• Gain deeper insights into the patterns and trends hidden within their data.

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Final Take

This journey through Tableau's hidden depths has hopefully equipped you with valuable tools to elevate your data visualizations. Remember, becoming a Tableau expert is an ongoing process. As you hone your skills, prioritize clarity and impact. Effective visualizations don't simply present data; they spark curiosity, tell stories, and guide users to actionable insights.

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So, keep these tips and tricks close at hand. With a dash of creativity and a sprinkle of Tableau magic, you'll be crafting stunning visualizations that leave a lasting impression. Remember, data is powerful, but it's your expertise that unlocks its true potential. Now, go forth and visualize!

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By Abdul Wadood.  

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