Salesforce Integration Patterns: The Most Common Ones & How They Work

Customer relationship management has been completely revamped in recent years. There is a high demand for quick and efficient customer management. Salesforce can meet these demands and boost sales, gain loyal customers, and improve overall marketing. It gives businesses the ability to access and use customer information to streamline business processes and create the best services. 

Businesses and retailers are realizing the growing importance of Salesforce integration and are connecting Salesforce and its adjacent systems. But these point-to-point integrations are inefficient, difficult to manage, and unscalable. Instead, businesses must use a Salesforce integration strategy that connects with relevant enterprise systems.

When considering the variation of Salesforce integration needs, common patterns appear. This makes it easier to address them accordingly. In other words, these patterns are the things one must do to solve a certain Salesforce integration problem, as recognized from real use cases.

The 5 Most Common Salesforce Integration Patterns

The Migration Pattern

This is moving a specific set of data at one point in time from one system to another. It allows developers to create automated migration services that create functionality to be shared across numerous teams in an organization. Developers can set the configuration limits to pass into API calls so that the migration can migrate scoped Salesforce data in or out of Salesforce either on command or an as-needed basis via an API. Creating reusable services for frequent data migrations saves time for development and operations teams.

Migrations are useful for numerous Salesforce integration use cases, like migrating data from a legacy system to Salesforce or backing up a customer dataset. They can handle large volumes of data, process multiple records in batches, and have low failure cases. Besides, migration is vital for keeping enterprise data-agnostic from the tools that create, view, and manage it, allowing it to be used and reused by multiple systems. Without it, data would be lost whenever tools were altered, greatly affecting productivity.

The Broadcast Pattern

The broadcast pattern transfers data from a single system to multiple destination systems. This happens on a near real-time or real-time basis. Put simply, it is one-way synchronization from one to many. One-way sync usually connotes a 1:1 relationship; while the broadcast pattern creates a 1: many relationship.

This pattern is transactional and is optimized for processing records as fast as possible. Broadcast patterns keep data up-to-date between several systems through time. A broadcast Salesforce integration pattern must be super reliable to prevent losing important data in transfer. And as they often have limited human oversight and are usually initiated in mission-critical systems by push notification or are scheduled, reliability becomes even more crucial.

Moreover, it allows for the immediate transfer of customer data between systems. For instance, the pattern can enable action in Salesforce to immediately translate into order fulfillment processing. Synchronizing real-time data from Siebel to Salesforce are some use cases for the broadcast pattern.

The Aggregation Pattern

An aggregation Salesforce integration pattern gets data from many systems and copies or shifts it to one system. This gets rid of the need to run multiple migrations frequently, removing issues about data accuracy or synchronization. It is the easiest way to extract and process data from various systems into one application.

Using a Salesforce integration template constructed on an aggregation pattern allows us to request multiple systems when needed and merge data sets to create or store reports into the format of your choice. Aggregation contains a custom logic that can be adapted to merge and format data when needed, which can be easily extended to insert data into multiple systems, such as Salesforce or Siebel.

Updating Salesforce with ERP data or issue tracking systems and building APIs that collect, report, and return data from multiple systems are some real cases of using this pattern. Using APIs to retrieve data from different systems and process it into one response which modernizes legacy systems and the creation of a central data repository that is used for auditing purposes are just some instances where the aggregation Salesforce integration pattern comes in handy.

In short, the aggregation pattern allows the extraction of data from multiple systems and merging them into one application.  As a result, the data is always updated and can be processed or integrated to produce any needed dataset. 

The Bi-Directional Sync Pattern

Bi-directional sync patterns integrate different datasets in multiple systems, making them act like one system. This lets them identify the existence of different datasets. It is useful when different tools or systems are needed for purposes that need to complete different functions within the same data set. Using bi-directional sync uses both systems while maintaining steady real-time assessment of the data across systems.

Bi-directional sync integration results in optimized performance which maintains data integrity across both synchronized systems. It adds and removes multiple systems that subspecialize in a domain as storage. This integration pattern is best when object representations need to be comprehensive and constant.

Some uses of this pattern include: integrating Salesforce with various systems that contribute to operational efficiencies and a streamlined quote to cash but still act as a system of record for all data that must be synchronized.

The Correlation Pattern

This is quite similar to the bi-directional sync pattern but there is one major difference. The correlation pattern identifies the intersection of two data sets and performs bi-directional synchronization of that dataset. However, this only happens if the item transpires in both systems naturally. The bi-directional synchronization will make new records if they are found in only one system. The correlation pattern does not distinguish where the data objects come from. 

This pattern is needed in cases where two groups or systems want to share data, but only if they both have records representing the same items or contacts in reality. For example, hospitals might want to correlate patient data for shared patients across the healthcare network but want to avoid any kind of privacy violations.

In a Nutshell

Salesforce integration has countless benefits on data management in the enterprise. Using these patterns can revolutionize the way businesses use customer data, and use it for mutual benefit. Using them for reaching a wider customer base, or retaining loyal customers will make marketing products and services. To know which patterns will work best for you, get a consultation with integration experts to create your Salesforce Integration strategy.

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