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

Data architect responsibilities

Enterprise architecture is the highest level and the starting point from where guidelines, best practices, and overall enterprise parameters are set. For example, all integrations, whenever possible, will use the enterprise middleware and tightly coupled point-to-point interfaces will be avoided. Alternatively, when integrating two systems, the receiving system must pull data from the sending system rather than the sending system pushing data into the receiving system on a set schedule. This impacts the data architecture directly because, when designing interfaces, the data architect will need to be cognizant of these constraints set by the EA. Similarly, the solution architect would need to consider what other integration options are available if a system cannot be directly integrated using a point-to-point design (the point-to-point integration pattern is generally discouraged and should not be used when other viable options are available).

In this section, we will focus on the responsibilities of a data architect. However, it is important to understand the goals of data architecture before discussing the roles and responsibilities of a data architect. In any enterprise these days, usually there are volumes of data generated or flowing into or out of the enterprise. The data architect's goal is to design blueprints to facilitate the short-term and long-term data needs of the enterprise securely. This requires understanding the long-term vision of the enterprise (business strategy) so that the data architect can propose and implement processes that will align data management with the business strategy and maximize the ROI in the enterprise's data initiatives. The responsibilities of a data architect include the following:

  • Understanding, assessing, and documenting the current state of the organization: This is the first and probably the most important step for a data architect, ensuring that they understand the current state thoroughly. This helps in identifying current issues in data flows and integration pain points as well as in formulating a plan for the future. Furthermore, documenting the current state aids in communicating with other stakeholders, such as EAs, solution architects, and business architects. This helps in securing project funding from the executive leadership.
  • Developing a dictionary for data across the organization: Data architects define and maintain data dictionaries. A data dictionary is an inventory of data items that are used to convey information about data, such as metadata.
  • Aligning data architecture activities with enterprise architecture: The data architect works closely with the EAs to ensure the initiatives that the data architect takes align with policies and guidelines established by the EA.
  • Developing a data requirements plan for the long-term storage, archiving, processing, and transmitting of data: On an ongoing basis, data architects work on ensuring that the organization's data remains viable over a long period of time. They also need to anticipate industry and technology changes to ensure initiatives taken today will not obstruct the future use of newer technologies and that the organization can continue to evolve to face new marketplace realities.
  • Guiding domain architects and external partners on optimal ways to access data: Data architects assist and provide guidance to domain architects and other stakeholders that may be trying to access the organization's data. They are expected to provide guidance around integration design as well to ensure that they align with EAs' policies and guidelines.
  • Evaluating applications: Often, data architects are asked to participate in evaluating Commercial off-the-Shelf (COTS) applications from the context of data. They will look at data flows and how the new application would interact with the data. What changes, if any, need to be made before the application can work with existing data or data that is getting produced by the organization?
  • Data governance: The data architect is also an integral part of data governance activities in the organization. They are responsible for documenting and maintaining processes related to technical data governance and data models for master data subject areas and reference master data.
  • Coaching and mentoring: Lead data architects may also have team members that specialize in certain domains reporting to them. These individuals, as well as individuals or architects from other teams, may need coaching and mentoring with respect to matters pertaining to data architecture.
  • Being the primary contact for vendors: Data architects also act as the primary technical contact for vendors pertaining to data, particularly Master Data Management (MDM) solutions or other data-focused applications within their portfolio.
  • Delivering innovative solutions: With the rapid increase in the volumes and sources of data, regulatory requirements, and policies, organizations require innovative solutions to solve their data-related business problems. A data architect is required to maintain an extensive knowledge base of industry trends, best practices, and the available tools in the marketplace to propose optimal solutions.
  • Compliance: Compliance is another area where data architects will spend a considerable amount of their time. To be clear, many organizations will have data privacy officers that understand the privacy laws and develop requirements to adhere to those laws. The data architect, on the other hand, is responsible for the technical implementation and ensures that data flow, storage, and retrieval are in line with these laws and organizational policies.

Now that we have looked at the responsibilities of data architects and their role in an organization, let's review the soft and hard skills that data architects need to be effective in their roles.