Data Integration

Short answer: Data integration is a broad goal, and there are several ways to reach it. A company can copy data into a central store such as a data warehouse. It can copy data between databases. It can keep records in step between applications through direct system connections. Or it can use a tool that reads each source live when someone asks, without copying the data.

Data integration overlaps with system integration. System integration connects applications so processes flow between them. For example, an approved order in the sales system creates a delivery job in the warehouse system. Data integration focuses on making the data consistent and usable together, often for reporting and analysis. A data pipeline is the running process that moves the data. The hardest part is usually matching records across systems that use different identifiers. The team also has to agree definitions, decide which system wins when values conflict and deal with poor data quality. Timing also matters: some uses need data within minutes, while most reporting works with daily updates.

The team lists the source systems and the people or processes that need the combined data. It maps fields and identifiers between systems, often with a cross-reference table that links each system's code for the same item. It chooses a method for each flow: copies on a schedule, syncing within minutes, or live reads from each source when someone asks. Virtual approaches avoid duplicate copies but depend on the speed and availability of the source systems. The team builds connectors or pipelines and writes down the conflict rules. Monitoring then checks that records keep matching.

An online seller takes orders on its own website and two marketplaces: 400, 250 and 150 orders a week, or 800 in total. Each channel reports orders in its own format, and one marketplace uses its own codes for 120 products. To plan picking and reorder stock, the warehouse team combines three exports by hand every week. Data integration brings orders from all three channels into one order table every night, using a mapping table for the 120 products. All 800 weekly orders appear in one view by product and channel. The team sees which products sell fastest across all channels and reorders them before they run out.

Most goals around reporting, automation, customer experience and AI depend on data integration. Two questions decide the method and the cost: which business questions or processes need combined data, and how current that data must be. The company should answer both before it chooses a method.

Every integration is a dependency that needs monitoring and maintenance, and changes in source systems can break it. Copying data to more places spreads sensitive information. Integration often exposes data quality problems that stayed hidden while systems were separate. Near real-time integration costs more than daily updates and is not always necessary. A company should test vendor claims about ready-made connectors with real data.