6 tips to make your Dashboards more Performant in Tableau

We here at Tableau are very proud of how easy it is to see and understand data with Tableau. Once you get started, it’s intuitive to dive deeper by adding more and more fields, formulae, and calculations to a simple visualization—until it becomes...

We here at Tableau are very proud of how easy it is to see and understand data with Tableau. Once you get started, it’s intuitive to dive deeper by adding more and more fields, formulae, and calculations to a simple visualization—until it becomes slower and slower to render. In a world where two-second response times can lose an audience, performance is crucial.

So where do I start?
So how can you make your dashboards run faster? Your first step is to identify the problem spots by running and interpreting your performance recording. The performance recorder is every Tableau speed demon’s ticket to the fast lane. The performance recorder can pinpoint slow worksheets, slow queries, and long render-times on a dashboard. It even shows the query text, allowing you to work with your database team on optimizing at the database level.

Now that you know which views or data connections are slowing you down, below are six tips to make those dashboards more performant. For each tip, we’ve listed the most common causes of performance degradation as well as some quick solutions.

To get in-Depth knowledge on Tableau Dashboard you can enroll for live Tableau Online Training

1. Your data strategy drives performance
Extracts are typically much faster to work with than a live data source, and are especially great for prototyping. The key is to use domain-specific cuts of your data. The Data Engine is not intended to be a replacement for a data warehouse. Rather, it’s meant to be a supplement for fast prototyping and data discovery.

• Minimize the number of fields based on the analysis being performed. Use the hide all unused fields option to remove unused columns from a data source.
• Minimize the number of records. Use extract filters to keep only the data you need.
• Optimize extracts to speed up future queries by materializing calculations, removing columns and the use of accelerated views.

Keep in mind: Extracts are not always the long-term solution. The typical extent of an extract is between 500 million to one billion rows; mileage will vary. When querying against constantly-refreshing data, a live connection often makes more sense when operationalizing the view.

2. Reduce the marks (data points) in your view
When data is highly granular, Tableau must render and precisely place each element. Each mark represents a batch that Tableau must parse. More marks create more batches; drawing 1,000 points on a graph is more difficult than drawing three bars in a chart.

Large crosstabs with a bevy of quick filters can cause increased load times when you try to view all the rows and dimensions on a Tableau view.

Excessive marks (think: data points) on a view also reduce the visual analytics value. Large, slow, manual table scans can cause information overload and make it harder to see and understand your data.

Here’s how you can avoid this trap:

• Practice guided analytics. There’s no need to fit everything you plan to show in a single view. Compile related views and connect them with action filters to travel from overview to highly-granular views at the speed of thought.
• Remove unneeded dimensions from the detail shelf.
• Explore. Try displaying your data in different types of views.

3. Limit your filters by number and type
Filtering in Tableau is extremely powerful and expressive. However, inefficient and excessive filters are one of the most common causes of poorly performing workbooks and dashboards. Note: Showing the filter dialog requires Tableau to load its members and may create extra queries, especially if the filtered dimension is not in the view.

• Reduce the number of filters in use. Excessive filters on a view will create a more complex query, which takes longer to return results. Double-check your filters and remove any that aren’t necessary.
• Use an include filter. Exclude filters load the entire domain of a dimension, while include filters do not. An include filter runs much faster than an exclude filter, especially for dimensions with many members.
• Use a continuous date filter. Continuous date filters (relative and range-of-date filters) can take advantage of the indexing properties in your database and are faster than discrete date filters.
• Use Boolean or numeric filters. Computers process integers and Booleans (t/f) much faster than strings.
• Use parameters and action filters. These reduce the query load (and work across data sources).

4. Optimize and materialize your calculations

• Perform calculations in the database. Wherever possible, especially on production views, perform calculations in the database to reduce overhead in Tableau. Aggregate calculations are great for calculated fields in Tableau. Perform row-level calculations in the database when you can.

• Reduce the number of nested calculations. Just like Russian nesting dolls, unpacking a calculation and then building it takes longer for each extra layer.
  

• Reduce the granularity of LOD or table calculations in the view. The more granular the calculation, the longer it takes.
  LODs - Look at the number of unique dimension members in the calculation.
  Table Calculations - the more marks in the view, the longer it will take to calculate.

• Where possible, use MIN or MAX instead of AVG. AVG requires more processing than MIN or MAX. Often rows will be duplicated and display the same result with MIN, MAX, or AVG.

• Make groups with calculations. Like include filters, calculated groups load only named members of the domain, whereas Tableau’s group function loads the entire domain.

• Use Booleans or numeric calculations instead of string calculations. Computers can process integers and Booleans (t/f) much faster than strings. Boolean>Int>Float>Date>DateTime>String

5. Take advantage of Tableau’s query optimization

• Blend on low-granularity dimensions. The more members in a blend, the longer it takes. Blending aggregates the data to the level of the relationship. Blends are not meant to replace row-level joins.
• Minimize joined tables. Lots of joins take lots of time. If you find yourself creating a data connection with many joined tables, it may be faster to materialize the view in the database.
• Assume referential integrity if your database is configured with this option.
• Remove custom SQL. Tableau can take advantage of database optimizations when the data connection does not use custom SQL.

6. Clean up your workbooks!

• Reduce dashboard scope. Excess worksheets on a dashboard can impact performance.
• Delete or consolidate unused worksheets and data sources. A clean workbook is a happy workbook.

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In this post, we've highlighted seven best practices you can apply right now to make your dashboards more effective. And for even more tips and tricks, be sure to join our webinar series to dive into the art of dashboarding. ### 1. Steal like an...

In this post, we've highlighted seven best practices you can apply right now to make your dashboards more effective. And for even more tips and tricks, be sure to join our webinar series to dive into the art of dashboarding.

1. Steal like an artist

Austin Kleon, author of Steal Like an Artist, encourages us to embrace the influence of others. This holds true in designing dashboards as much as in any creative endeavor. Don’t create your work in a vacuum: Take inspiration from as many places as you can.

A great example is Andy Kriebel’s financial statement dashboard, based on the work of Lindsay Poulter. Lindsay’s original design was applied to public transport data, and Andy redesigned a financial statement using that idea.

When you see a great visualization, examine it and consider any aspects that would benefit your own work.

To get in-Depth knowledge on Tableau Dashboard you can enroll for live Tableau Online Training

2. Avoid clutter

When you first start building dashboards, it’s tempting to throw every possible applicable chart or graph onto them. Don’t fall into that trap.

The trick is to remove as much as you possibly can while ensuring the end user gets the right insight from your dashboard. This is often an iterative process, as you can only discover the “better” version over time. Go take a look at the latest dashboard you created: does it have too much on it? Is there anything you can remove or rearrange to add clarity?

3. Use a grid layout

When placing objects on a dashboard (views, filters, titles, etc.), consider a grid format.

A grid helps provide a reading order for your dashboard, allowing your users to guide themselves through the dashboard in a predictable and logical way. You can use a columnar- or row-based flow to create a narrative that leads users from overview to detail.

4. Use the right fonts

Typography is important. It can be tempting to use too many font types and sizes in a dashboard. Don’t do that; instead, define a clear hierarchy for your typography.

In the example above, there is a top-, mid-, and low-level font. The mid-level is blue to focus viewer attention. Color draws the eye to the most important level (which needn’t necessarily be the top-level font).

Reducing the levels of hierarchy to only the most necessary reduces confusion for your users, and color is one of the best ways to attract viewers.

5. Simplify your use of color

How many times have you seen a dashboard that looks like an accident in a paint shop? Color is one of the most tempting things to play with when building a dashboard. It feels productive, and it seems like you’re adding value by making things colorful. But get this: unnecessary color does not add value.

You should be able to justify every single color on your dashboard: why did you choose any specific color, and what does it communicate to your user? If you can’t answer that question, remove the color.

Keep in mind that 8% of males have color-vision deficiency (CVD): choose palettes that work well universally. This means avoiding reds and greens, or at least choosing reds/greens that can be seen by people with CVD. To dive deeper, review Tableau’s best practices for color.

6. BANs

Don’t bury the most important fact, your KPI, in a chart. Show it loud and proud as a Big A** Number (BAN)!

This technique hits users with the most vital information immediately, and charts provide further context. At Tableau, our research team has been studying the impact of BANs using eye-tracking software.

7. Collaborate and iterate

If you take one tip away from this post, it’s this one: no dashboard is right the first time. Time and collaboration are the only ways to hone in on the best representation of your data. Other people’s opinions will bring you fresh perspectives.

All the dashboards in our book went through many iterations. Even now, we are still evolving them to make improvements and adapt to changing business questions.

Don’t know where to start? Here are some ideas:

• Attend a Tableau User Group (or start your own).
• Join Tableau Community to ask and answer questions on the forum.
• Get involved in #MakeoverMonday.
• Set up an internal Tableau group at your company.

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