Every organization I have worked with has the same quiet problem.
Customer data lives in the CRM. Orders live in the ERP. Behavior lives in the web analytics tool. Support history lives in the service platform. And every one of them has a slightly different version of the truth.
Data integration is the discipline of moving, combining and sharing data across these systems so the business can actually use it. It sounds simple. It rarely is.
Data Integration vs Application Integration
People often mix these up.
Application integration is about systems working together in a business process: "when an order is placed, reserve stock".
Data integration is about making data available, consistent and usable across systems: "give analytics a complete, clean view of all orders and customers".
They overlap, and often use the same tools, but the goals and design choices are different.
The Main Styles
Batch and ETL / ELT
Extract data in bulk on a schedule, transform it, and load it into a target such as a data warehouse. Simple, efficient and still the right answer for much reporting and analytics. The trade-off is latency: data is hours old.
Change Data Capture (CDC)
Capture changes from a source database as they happen and stream them to targets. It gives near real-time synchronization with low impact on the source system, but requires careful operation and understanding of the source schema.
APIs and events
Share data at the business level through APIs and business events. Ideal for operational use cases where data needs to be current, governed and meaningful. It requires more design effort up front.
Virtualization and zero-copy
Query data where it lives instead of copying it. Modern data platforms, including Salesforce Data 360, Snowflake and Databricks, increasingly support zero-copy sharing. It reduces duplication, but depends on the performance and availability of the source.
The Questions That Decide the Style
When I help a team choose, I ask:
- How fresh must the data be: seconds, minutes or a day?
- How much data, and how often?
- Who owns the data, and who is allowed to use it?
- What quality does the consumer need?
- Is this for operations, analytics or AI?
The answers usually make the choice obvious.
The Problems That Are Not Technical
Ownership
If nobody owns customer data, every system will keep its own version. Define clear data owners and systems of record for key entities.
Quality
Integration does not fix bad data. It spreads it faster. Build data quality checks into pipelines, and fix problems at the source where possible.
Meaning
"Active customer" can mean three different things in three different departments. Shared definitions and a business glossary save endless arguments.
Privacy
Every copy of personal data is another copy to protect and govern. Move only what is needed.
Data Integration and AI
AI has made data integration a board-level topic. Models and agents are only as good as the data they can reach. Organizations with well-integrated, well-governed data are moving much faster with AI than those still fighting over spreadsheets.
Final Thought
Data integration is not about moving data around.
It is about making the right data available, in the right shape, to the right people and systems, at the right time, and trusting it when it gets there.