Do you wish to understand what Tableau Aggregation is? Do you wish to implement Tableau Aggregation for your analysis? If yes, then you’ve come to the right place. With data analytics having a newfound significance in the digital realm, it extends to different ways that patterns can be analyzed for insights to be incorporated into one’s strategy. Visual insights really do stand apart from other kinds when it comes to understanding your data and getting constant updates about your performance.
Tableau being a robust Business Intelligence tool has gained some traction in the current times where data analytics has become an essential element of boosting one’s online presence.
Aggregation is another feature rendered under Tableau where this process of analytics can be made more accurate by aggregating relevant insights from all data sources and preparing them for analysis. This article throws some light on Tableau and the benefits of Tableau Aggregation. You will also read about how you can implement it in Tableau and extract its merits for the most efficient use.
Introduction to Tableau
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Tableau is a robust BI tool that facilitates the easy creation of interactive and vibrant analytics. With all details, filtered for different factors, being highlighted on dashboards, even people from a non-technical background can easily analyze their data and use it to implement their brand strategies. The Tableau dashboard appears with varied visual insights as shown below.
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With Tableau’s Business Intelligence and data visualization features, it makes the deployment of insights much easier to understand and implement. The platform also remains extremely easy to use and work with.
You can simply connect to your data source and then use drag-and-drop controls to get results and explore your data sets interactively. It allows you to build dashboards to represent your insights and is suitable for use by people from all technical and non-technical backgrounds for their business insights.
What is Tableau Aggregation?
Why use Data Aggregation in the first place when you could directly configure different systems and sources for analytics. Well, the answer is pretty clear! Businesses encounter redundant data, inaccuracies, and even some potential paradoxes from different sources which is what makes Aggregation much-needed for analytics. Tableau allows an aggregate function to be represented as shown below.
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Tableau Aggregation Benefits?
Aggregation in Tableau presents several benefits, which can be primarily observed as follows:
1) Consistency of Details from Multiple Sources
Tableau Aggregation helps to accumulate multiple values to return a single value that remains consistent as per different sources. This further extends to the concept of granularity, where the level of details can be made broader or more streamlined as per business requirements.
2) Relevant Aggregate Dimensions can be Used
At times businesses and brands need to use a combination of dimensions to chalk out insights and use that data appropriately. Plotting these points to extract a pattern can often be difficult. Tableau Aggregation makes this possible with the use of singular aggregate dimensions that can provide accurate insights clubbed from a variety of factors.
3) Local Cumulative Computation makes the Process more Efficient
Gathering insights from remote sources also increases the query processing load as different computations have to be made for each data insight. Aggregation greatly improves query performance by bringing the computation to a local level where patterns can be extracted faster and more efficiently.
Steps to Implement Tableau Aggregation
Tableau facilitates Data Aggregation and representation in different forms. Measures, as well as dimensions, can be aggregated and viewed in relevant types as per the insight that is being focussed on. Additionally, you can alter the Aggregation levels within a specific view and fine-tune exactly the factors that you want to incorporate in the analysis process.
Following are the aspects and steps that can be followed to implement Tableau Aggregation:
Step 1: Adding Tableau Aggregation Measures
In addition to the list of predefined Aggregations in Tableau, you can add Aggregation measures to the view. Tableau will automatically aggregate the desired value, and it will become part of the name of the measure within the view. The sum, average, variance, standard deviation, and other factors can be defined to add a specific measure as shown below:
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Step 2: Setting the Default Aggregation for a Measure in Tableau
The default Tableau Aggregation can be set for any of the measures in consideration. To do so, you can simply right-click on the “Measure” menu option and head to “Default Properties.” Within that, you can specify your default Tableau Aggregation under the “Aggregation” drop-down menu as shown below:
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Step 3: Changing the Default Measures for Aggregation in Tableau
While you cannot change the Aggregation for a published data source, you can change the default measure for Tableau Aggregation to any other through the drop-down procedure described in the previous point.
The key factor to note here is that the default Tableau Aggregation is only set when the data source is published at first. Any alterations to the same can only be made from the successive stages.
Step 4: Aggregating Dimensions in Tableau
With Tableau, you can implement the much-desired feature of aggregating dimensions within the view. A new temporary measure needs to be created before you can aggregate dimensions such a “Maximum”, “Minimum”, and “Count”, etc. The new attribute is calculated with the following formula in Tableau:
If MIN (dimension) = MAX (dimension) then MIN (dimension) else “*” end
This process can be initiated with the new dimension taking the characteristics of a specific measure that can be specified as shown below:
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Step 5: Disaggregation of Data in Tableau
The Tableau Disaggregation of datasets can provide equally valuable insights as Tableau Aggregation. Sometimes the collective impact of only certain factors is desired by a business, and some already included parameters need to be removed from consideration.
This is where Disaggregation plays a vital role. In order to disaggregate a measure, you can click on the “Analysis” menu tab and specify the different Aggregations to be carried out under “Aggregate Measures” as shown below.
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Specific elements of Disaggregation can be useful when implemented using a scatter plot as shown below.
1) Building a Basic Scatter Plot
You can select the distinct measures you want as part of your Tableau Aggregation on the “Rows” and “Columns” shelves to form a scatter plot comparison of two values as shown below:
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2) Adding Details using Dimensions & More Fields to Shelves
Drag in “Category” dimensions to “Color” on the card and encode a colour code for each dimension that you wish to include. More fields and details can be added to your plot by adding fields to the “Rows” and “Columns” shelves as shown below:
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3) Disaggregate Data
You could also try to disaggregate the data by selecting the “Aggregate Measures” option from the “Analysis” menu. When you deselect the already selected “Aggregate Measures” option, you are essentially dis-aggregating the data, and Tableau will implement the same within your current view.
Your scatter plot elements will be disaggregated and represented as independent entities as shown in the example below:
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Following these steps, you can carry out the respective tasks of Tableau Aggregation, adding dimensions, changing specific of the measures as well as Disaggregation of data as per your requirements. Within Tableau, you can fine-tune each dimension and element to only deliver insights from specific parameters.
You can add as many measures and aggregate selective dimensions in order to come up with a custom combination of factors that you would like to use for the most robust and efficient insights for data sourced from multiple data warehouses and storage platforms.
Conclusion
Tableau Aggregation can be an effective way of analyzing your business data, setting up specific measures, and even Disaggregation of some factors when required. After all, even those who can’t relay JSON data sets from their databases should get the privilege of understanding their business data in a simple yet accurate visual format.
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Aman Deep Sharma is a data enthusiast with a flair for writing. He holds a B.Tech degree in Information Technology, and his expertise lies in making data analysis approachable and valuable for everyone, from beginners to seasoned professionals. Aman finds joy in breaking down complex topics related to data engineering and integration to help data practitioners solve their day-to-day problems.
