Book Image

Creating Actionable Insights Using CRM Analytics

By : Mark Tossell
Book Image

Creating Actionable Insights Using CRM Analytics

By: Mark Tossell

Overview of this book

CRM Analytics, formerly known as Tableau CRM and Einstein Analytics, is a powerful and versatile data analytics platform that enables organizations to extract, combine, transform, and visualize their data to create valuable business insights. Creating Actionable Insights Using CRM Analytics provides a hands-on approach to CRM Analytics implementation and associated methodologies that will have you up and running and productive in no time. The book provides you with detailed explanations of essential concepts to help you to gain confidence and become competent in using the CRM Analytics platform for data extraction, combination, transformation, visualization, and action. As you make progress, you'll understand what CRM Analytics is and where it provides business value. You'll also learn how to bring your data together in CRM Analytics, build datasets and lenses for data analysis, create effective analytics dashboards for visualization and consumption by end users, and build dashboard actions that take the user from data to insight to action with ease. By the end of this book, you'll be able to solve business problems using CRM Analytics and design, build, test, and deploy analytics dashboards efficiently.
Table of Contents (19 chapters)
1
Section 1: Getting Started with CRM Analytics
4
Section 2: Building Datasets in CRMA
10
Section 3: How to Build Awesome Analytics Dashboards in CRMA
15
Section 4: From Data To Insight To Action

Advanced use of the Data Prep tool

As you dive into more complex and powerful usage of the Data Prep tool, you will begin with one of the most common requirements for the ETL process—data joins.

The ETL Process

ETL is a data integration process that encompasses three distinct but interrelated steps (extract, transform and load) and is used to synthesize data from multiple sources many times to build a dataset that reflects business rules and meets analysis requirements.

Diving into data joins

We looked briefly at joining and augmenting your data in Chapter 4, Building Data Recipes.
In this section, we will dive much deeper into this important area of data transformation. First, let's begin by understanding the various types of data joins.

Understanding the various types of joins

There are four common ways to join your data, as seen in the following diagram:

Figure 5.1 – The four types of Structured Query Language...