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Tool Mastery | Field Summary

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Field Summary.png

This article is part of the Tool Mastery Series, a compilation of Knowledge Base contributions to introduce diverse working examples for Designer Tools. Here we’ll delve into uses of the Field Summary Tool on our way to mastering the Alteryx Designer: 


The Field Summary Tool analyzes data and creates a summary report containing descriptive statistics of data in selected columns. It’s a great tool to use when you want to make sure your data is structured correctly before using any further analysis, most notably with the suite of models that can be generated with the Predictive Tools. Think of the Field Summary as a Browse Tool, but on steroids. Not only does it give you a summary of your fields, but it will give you recommendations based on its data type on how to fix your data to be used for further analysis.


The configuration for the Tool should be easy enough – just select your fields you want summaries for and if you would like to sample your data:




Now let’s go over the various summaries we get, depending on the field’s data type. Summaries will be provided for Numeric, String, Spatial, and Date/Time fields. Depending on the type, a different set of statistics will be provided, as shown below (borrowed from the Tool's help document):




These statistics are provided via three outlets – The O anchor gives us a data stream, the R anchor gives us a static report that can be viewed via the Browse Tool, and the I anchor gives us an interactive dashboard that can be viewed via the Browse Tool. 


Usually we’ll use the Field Summary Tool if we’re unfamiliar or unsure about the data we’re using, before we plug into a Predictive model. Understanding your data before doing any predictive analysis can save many headaches for any errors that may arise when running your model, or even when assessing why your output is less than desirable.


Many the suggestions provided by the Field Summary Tool can help guide you to either categorize your data differently, or to even look to supplement your data with less biased data. For example, for String data types, a common remark is that some values in a certain field have a small number of value counts. This can greatly affect a model, as it can create bias for that variable and doesn’t accurately capture the true effect of that variable on the model.


We can agree that the plethora of predictive tools available is great, but it’s not much help if we feed it bad data! Using the Field Summary will be there when you’re confused or heartbroken, lending you a helping hand in trying to understand what went wrong (or avoid the heartaches by heeding it’s advice before casting your heart into those predictive tools!).


By now, you should have expert-level proficiency with the Field Summary Tool! If you can think of a use case we left out, feel free to use the comments section below! Consider yourself a Tool Master already? Let us know at if you’d like your creative tool uses to be featured in the Tool Mastery Series.


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