Data Orchestration in Tableau CRM
Data is the new oil and rightly so. But the reality is that data is often scattered across systems and not only stored in Salesforce, which becomes a challenge when businesses want to enrich their decision-making with analytics. Tableau CRM (Einstein Analytics) has got various inbuilt connectors that help us in getting all of the data in one place. Once we have all of the data we can then focus on getting actionable insights and make important business decisions. However, it’s not enough just to bring data in, we have to be smart about the Data Orchestration as well, which this blog series will set focus on.
What will we cover?
The use case dictates the volume and frequency of the data coming into Tableau CRM, but and how we meet those requirements is dependent on data sync and data preparation configuration as well as the different governance limits that we encounter when working with data preparation. Therefore this blog series, we will explore the various types of data sync, how to choose the right data sync frequency, and how to control the data volume that is getting synced.
We will also see methods to schedule dataflows and recipes and how to overcome certain obstacles while scheduling them. We will address scenarios where there is a dependency between two dataflows or dependency between dataflow and recipe, these use-cases are not directly addressed out of box, we will discuss how to tackle such scenarios.
Like any other tool, Tableau CRM is built with keeping the end-user as 👸🤴. The analytics developer will have to be aware of various aspects of the tool and work to ensure that the end-users are getting the best experience.
In this blog series, we are focused on how to connect to data sources and how we get to create the dataset. However, there are many other aspects to data in Tableau CRM that will not be covered in this blog series like best practices for optimizing dataflows, various transformations we can do in dataflows/recipes, how to export data out of Tableau CRM etc.
Data Orchestration Blogs
It is recommended to read this blog series in chronological order as each blog builds on concepts covered in previous blogs.