Plan Your Data Cloud Implementation
Learning Objectives
After completing this unit, you’ll be able to:
- Assess the quality and structure of your data.
- Determine connection methods.
Before You Start
This badge is part of the Data Cloud: Unlock the Value of Your Data. The trail explains the strategic importance and benefits of Salesforce Data Cloud. In this badge, you learn how to get ready for a successful Data Cloud implementation. First you explore how to analyze your data and make integration decisions, then learn how to assemble your implementation team.
Data Assessment
Data Cloud is designed to unify, enrich, and activate customer data in real time. Its effectiveness as a foundation for AI solutions like Agentforce or for personalizing a campaign relies on the quality of the data it ingests and uses. Poor data quality can lead to missing or inaccurate insights, inefficiency, and an erosion of trust.
As you review your implementation, it’s important to consider the quality of the data sources needed for your chosen first use case. What sources have the most up-to-date data? What are the various sources’ unique identifiers? What data types are included in your sources? While you can transform your data in Data Cloud using formulas along with batch and streaming transformations, reviewing and understanding your data in advance can save you time.
Learn more data fundamentals in Data Quality and Batch Data Transforms in Data Cloud: Quick Look.
Integration Decisions
Your organization has data stored in a diverse range of data lakes, warehouses, and systems. Depending on your use case, it’s important to not only look at the quality and format of your data, but also how you want to integrate it with Data Cloud.
Ingest Your Data
One way to integrate data into Data Cloud is to ingest it as batch, streaming, or real-time data into Data Cloud. Data Cloud supports internal Salesforce data sources along with hundreds of third-party integrations. This is useful when you need a single centralized system for data transformation, governance, and control.
Ingesting your data is a good option when:
- You have a high volume of data.
- Data already exists in Salesforce, but is scattered across multiple clouds and needs to be unified.
- Source data requires complex transformations to meet data quality standards.
Let’s review when to ingest data as batch, streaming, or real-time:
- Batch: Use batch ingestion when you’re working with large volumes of data that can be processed at scheduled intervals and isn’t time-sensitive. For example, use batch ingestion for historical analysis for quarterly reports.
- Streaming: Use streaming ingestion when you need to continuously process data, but a short delay is okay. For example, use streaming ingestion for monitoring website usage.
- Real-time: Use real-time ingestion when you need to instantly process data for critical and time-sensitive tasks. For example, use real-time ingestion for fraud detection on credit cards.
Connect with Zero Copy
Access, query, and activate data directly from external data warehouses and lakes (such as Snowflake, Google BigQuery, Databricks, and Amazon Redshift) without copying or duplicating that data into Data Cloud. This is helpful when you want to retain existing integrations and want to reduce integration complexity.
Connecting with zero copy is a good option for:
- Real-time use cases
- Existing data lakes
Depending on your use case, it’s good to identify why you’d want to use one method over another and what timing is needed for accessing that data. For example, the data might need to be refreshed in real time, daily, or monthly.
The People Behind the Data
In the next unit, you shift gears from your data to who needs access to that data and who should be involved in your implementation.