Book Image

Learning Tableau 10 - Second Edition

Book Image

Learning Tableau 10 - Second Edition

Overview of this book

Tableau has for some time been one of the most popular Business Intelligence and data visualization tools available. Why? Because, quite simply, it’s a tool that’s responsive to the needs of modern businesses. But it’s most effective when you know how to get what you want from it – it might make your business intelligent, but it isn’t going to make you intelligent… We’ll make sure you’re well prepared to take full advantage of Tableau 10’s new features. Whether you’re an experienced data analyst that wants to explore 2016’s new Tableau, or you’re a beginner that wants to expand their skillset and bring a more professional and sharper approach to their organization, we’ve got you covered. Beginning with the fundamentals, such as data preparation, you’ll soon learn how to build and customize your own data visualizations and dashboards, essential for high-level visibility and effective data storytelling. You’ll also find out how to so trend analysis and forecasting using clustering and distribution models to inform your analytics. But it’s not just about you – when it comes to data it’s all about availability and access. That’s why we’ll show you how to share your Tableau visualizations. It’s only once insights are shared and communicated that you – and your organization – will start making smarter and informed decisions. And really, that’s exactly what this guide is for.
Table of Contents (12 chapters)
8
8. Deeper Analysis – Trends, Clustering, Distributions, and Forecasting

Clustering


Tableau 10 introduces the ability to quickly perform clustering analysis in your visualizations. This allows you to find groups, or clusters, of individual data points that are similar based on any number of variables of your choosing. This can be useful in many different industries and fields of study, for example:

  • Marketing may find it useful to determine groups of customers related to each other based on spending amounts, frequency of purchases, times and days of orders, and so on

  • Patient care directors in hospitals may benefit from understanding groups of patients related to each other based on diagnoses, medication, length of stay, and number of read missions

  • Immunologists may search for related strains of bacteria based on drug resistance or genetic markers

  • Renewable energy professionals would like to pinpoint clusters of windmills based on energy production and then correlate that with geographic location

Note

Tableau uses a standard k-means clustering algorithm that will yield...