Book Image

Salesforce Data Architecture and Management

By : Ahsan Zafar
Book Image

Salesforce Data Architecture and Management

By: Ahsan Zafar

Overview of this book

As Salesforce orgs mature over time, data management and integrations are becoming more challenging than ever. Salesforce Data Architecture and Management follows a hands-on approach to managing data and tracking the performance of your Salesforce org. You’ll start by understanding the role and skills required to become a successful data architect. The book focuses on data modeling concepts, how to apply them in Salesforce, and how they relate to objects and fields in Salesforce. You’ll learn the intricacies of managing data in Salesforce, starting from understanding why Salesforce has chosen to optimize for read rather than write operations. After developing a solid foundation, you’ll explore examples and best practices for managing your data. You’ll understand how to manage your master data and discover what the Golden Record is and why it is important for organizations. Next, you'll learn how to align your MDM and CRM strategy with a discussion on Salesforce’s Customer 360 and its key components. You’ll also cover data governance, its multiple facets, and how GDPR compliance can be achieved with Salesforce. Finally, you'll discover Large Data Volumes (LDVs) and best practices for migrating data using APIs. By the end of this book, you’ll be well-versed with data management, data backup, storage, and archiving in Salesforce.
Table of Contents (14 chapters)
Section 1: Data Architecture and Data Management Essentials
Section 2: Salesforce Data Governance and Master Data Management
Section 3: Large Data Volumes (LDVs) and Data Migrations

The Preparation phase

In this phase, you are prepping everything that's needed for the next phase, which is the execution phase. Armed with the trove of information that was gathered in the previous phase, you are set to act on it and prep for successful data migration.

Let's discuss the best practices that apply to this phase.

Best practices

You have developed a sound understanding of the current landscape along with understanding the type of data that is in scope, the source systems and their constraints, and the business requirements of the data migration. Now it's time to put all of this into action by following the best practices described as follows:

  • Analysis: Carefully analyze the result of the exercise from the assessment phase. Seemingly minor details that are skipped during analysis can cause a lot of rework sometimes down the road. For example, assuming that a legacy system has good data quality; without vetting this assumption with the users...