Book Image

Learning Einstein Analytics

By : Santosh Tukaram Chitalkar
Book Image

Learning Einstein Analytics

By: Santosh Tukaram Chitalkar

Overview of this book

Salesforce Einstein analytics aka Wave Analytics is a cloud-based platform which connects data from the multiple sources and explores it to uncover insights. It empowers sales reps, marketers, and analysts with the insights to make customer interactions smarter, without building mathematical models. You will learn to create app, lenses, dashboards and share dashboards with other users. This book starts off with explaining you fundamental concepts like lenses, step, measures and sets you up with Einstein Analytics platform. We then move on to creating an app and here you will learn to create datasets, dashboards and different ways to import data into Analytics. Moving on we look at Einstein for sales, services, and marketing individually. Here you will learn to manage your pipeline, understand important business drivers and visualize trends. You will also learn features related to data monitoring tools and embedding dashboards with lightning, visualforce page and mobile devices. Further, you will learn advanced features pertaining to recent advancements in Einstein which include machine learning constructs and getting predictions for events. By the end of this book, you will become proficient in the Einstein analytics, getting insights faster and understanding your customer in a better way.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
7
Security and Sharing in Einstein Analytics
Index

Summary


Now that we have finished with this chapter, we know that List widgets are filter widgets. We also looked at how custom actions can be performed and how broadcasting helps dashboard components communicate. We also covered how the dashboard increases productivity.

Creating datasets has been made easier by Einstein. But so many times it happens that we use so many fields unnecessarily in a dataset, or we might forget a file in the data. How do we tackle such problems? We can use a feature called Recipe, which we are going to study in the next chapter; this chapter also includes data preparation, data recipes, scheduling recipes, and exporting datasets from Einstein using datasetUtils.