Learn Be A Better Analyst 3 Steps to Shaping Your Data to be Machine-Readable

3 Steps to Shaping Your Data to be Machine-Readable

First featured on the Tableau Public website. There’s a real art to data, even if it is a science. I’ve learned a thing or two about taming data, so let me share my secrets to getting it into a usable, Tableau-friendly format. I’ll give you a hint: it’s all about making it “machine-readable”.

To illustrate, I’ll walk you through my latest Tableau viz, Sean Bean Survival Calculator, which I made because I wanted to know if the Sean Bean meme was justified.

1. Create one column for each dimension and measure
The data I found was just a written list. OK if you’re a human reading it, but not ideal for Tableau. Computer says no. First, I defined the dimensions and measures I would need.

Dimensions: categorization fields, like actor name, gender and movie.

Measures: quantitative fields to count, like, total films, films died, films survived, and survival rate.
Image-01

2. Find patterns to automate conversion of data
Next, I realized I had a problem. I had manually sourced the total number of films each actor starred in. This was easy, as it’s just a raw number. But I needed to count the number of films in which each actor’s character died, using a different data source.

Image-02

The data was a written list, including actor name, and then a list of the films the actor died in. I had this information for 102 actors and did not want to manually count each film! I noticed a pattern. The film names were separated by commas.

Image-03

With a simple spreadsheet formula, I was able to achieve my goal.

Image-04

Oops –  nearly forgot the ‘and’ in that list! So I added ‘+1’ at the end of the equation. And that’s your logic.

Image-05

It took me 1 minute to get the death count for all the actors. There was less risk of human error, too.

3. Validate and test with a few lines of data
Before committing to complete the database, I tested 4-5 rows of sample data in Tableau. Pro tip: you can simply copy and paste your data straight into Tableau.

Image-06I made sure my data would enable the kind of visualisations I wanted. This lets me spot gaps: for example, if I’m missing a crucial field. Or opportunities: get inspired to add something extra which could improve my story.

There you have it – 3 simple tricks to keep your data machine-readable, so you have more time to focus on discovering the insights within.

LEAVE A REPLY

Please enter your comment!
Please enter your name here

Follow

Tableau PublicYouTubeTwitterFacebookLinkedInInstagramEmail Newsletter

Latest Articles

Groceries made easy using Google Sheets

Have you ever wanted to help out more around the house, but not have it take up a lot...

Time Saver Tool v2.0

Have you ever discovered a way to speed up a process but cannot convince others to do it? Felt like you don't...

SG Tableau User Group Oct 2020

The SG Tableau User Group just wrapped up it's October virtual livestream event, which you can now watch on YouTube here. It...

Find insights beyond the summary stats (w/ data viz)

In this video we will see how summary stats can be misleading and the importance of data viz to easily dig deeper into the numbers beneath them.

Data Studio Community Livestream | Jun 2020

Welcome to the first Data Studio Community Livestream - you'll see 4 speakers covering a wide variety of topics, which should help any and...