You can use Salesforce Data 360 Connector in mappings and mapping tasks to bring trusted, integrated data into Salesforce Data 360. Use the data to build unified customer profiles, deliver near real-time analytics, and supply data to Agentforce AI agents.
You can use other connectors to read data from multiple sources, such as on-premises systems, cloud applications, IoT devices, and streaming platforms. You can apply transformations to cleanse, standardize, deduplicate, and enrich the data. Then, use Salesforce Data 360 Connector to write the data to data lake objects in Salesforce Data 360. Use the Streaming API ingestion mode to process near real-time micro-batch datasets, or use the Bulk API ingestion mode to load large datasets. You can also read data from data lake objects and data model objects.
The following examples show how you can use Salesforce Data 360 Connector to solve common business challenges:
•Build a unified customer 360 view from customer interaction data, web behavioral data, and customer relationship management (CRM) and enterprise resource planning (ERP) data.
•Stream transactional updates and event data into Salesforce Data 360 to deliver near real-time analytics.
•Create clean, high-quality datasets from diverse sources to provide trusted context for Agentforce AI agents.
You can combine customer data from multiple systems in Salesforce Data 360 to create unified, trusted customer profiles for personalized marketing, sales, and support.
You are a data engineer at a retail enterprise. Your organization stores customer data in customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, web analytics systems, and customer interaction records. The data resides in on-premises systems and cloud applications. Your marketing, sales, and support teams need a complete view of each customer to personalize each customer's engagement.
Your team faces the following challenges:
•Customer records reside in separate CRM, ERP, web analytics, and customer interaction systems.
•Records for the same customer have inconsistent formats and duplicates exist across systems.
•Marketing, sales, and support teams don't have a single trusted view of each customer.
•Each system requires manual effort or custom code to load its data.
To solve these challenges, create a mapping that reads customer data from each source system and prepares the data with transformations. Then use Salesforce Data 360 Connector to write the data to data lake objects in Salesforce Data 360.
Use the following high-level process to build the unified view:
1Read customer data from your on-premises systems and cloud applications in a mapping.
2Use transformations in the mapping to cleanse, standardize, deduplicate, and enrich the customer data before you load it.
3Create a target at run time with the Profile category, and enter a primary key that uniquely identifies each customer record. Salesforce Data 360 requires the primary key to create a data stream.
4Write the customer records to data lake objects with the upsert operation.
5Use the Bulk API ingestion mode for the first load to the new target. For subsequent runs, use the Streaming API ingestion mode to keep the profiles current.
After ingestion, Salesforce Data 360 unifies the customer records into trusted profiles. Each team works from the same consistent, deduplicated customer data.
Deliver near real-time analytics
You can stream transactional updates and event data into Salesforce Data 360 so that you can make near real-time decisions and gain operational insight.
You are an operations analyst at an online retail enterprise. Your organization captures transactional updates and event data, such as orders and website activity, from many systems. Your team wants to analyze this data in Salesforce Data 360 soon after each event occurs and act on current customer activity.
Your team faces the following challenges:
•Transactional and event data arrives continuously from many systems.
•The volume of transactional and event data is high.
•Your team can't react to changes in customer activity.
To solve these challenges, create a mapping that reads the transactional and event data. Then use Salesforce Data 360 Connector to write the data to data lake objects in Salesforce Data 360 as near real-time micro-batch datasets.
Use the following high-level process to deliver near real-time analytics:
1Read transactional and event data from your source systems in a mapping.
2Create a target at run time with the Engagement category because the event data is time-series oriented. Enter the name of the field that captures when each event occurred.
3Use the Bulk API ingestion mode for the first run of the mapping to avoid a delay when you load data into the data stream.
4For subsequent runs, use the Streaming API ingestion mode to process near real-time micro-batch datasets.
Note:
The Streaming API job completes after it reads the data from the source and pushes it to the target. However, the data isn't loaded to the target immediately. The data might take a few minutes to load.
Transactional updates and event data reach Salesforce Data 360 in near real-time micro-batches. Your team can base decisions on current data and gain operational insight into customer activity.
Prepare trusted data for Agentforce AI agents
You can integrate, cleanse, and load data from diverse sources into Salesforce Data 360 so that Agentforce AI agents work with accurate and trusted context.
You are a data engineer at a financial services enterprise. Your organization uses Agentforce AI agents that rely on data in Salesforce Data 360. The data that the agents need resides in on-premises systems, cloud applications, IoT devices, and streaming platforms.
Your team faces the following challenges:
•The data that the agents use resides in many separate systems.
•Incomplete, inconsistent, or duplicate data leads to inaccurate or unreliable agent responses.
•Manual dataset preparation is slow and hard to repeat.
•The data in Salesforce Data 360 needs to stay current so that agents use the latest information.
To solve these challenges, create mappings that cleanse and integrate data from diverse sources. Then use Salesforce Data 360 Connector to write the data to data lake objects.
Use the following high-level process to prepare trusted data for the agents:
1Read the data that the agents need from your source systems in a mapping.
2Use transformations in the mapping to cleanse, standardize, deduplicate, and enrich the data before you load it.
3Write the data to data lake objects in Salesforce Data 360. Use the Bulk API ingestion mode to load large datasets or the Streaming API ingestion mode to process near real-time micro-batch datasets.
4Run the mapping in a mapping task regularly to keep the data in Salesforce Data 360 current.
The Agentforce AI agents can access clean, high-quality data from Salesforce Data 360 and work with accurate, trusted context. Your team can repeat the data preparation process consistently.