Which statement best defines Data Integration?

Which statement best defines Data Integration?

• A. A data cleaning method
• B. Pre-built data connections to multiple source systems so visualisations can be created as soon as systems are connected.
• C. A data visualization tool
• D. Data storage strategy

Answer: (B)

Data integration is about bringing data from different sources into a single, usable view so you can analyse it across systems. The statement describing pre-built data connections to multiple source systems so visualisations can be created as soon as systems are connected captures this idea: it focuses on quickly linking diverse data and making it ready for analysis and dashboards. This reflects the practical value of integration—you can pull in data from various apps, align formats, and deliver unified insights without heavy manual rebuilding. It's not just about cleaning data—that would be data quality work—nor is it about the visualization tool itself, which is for presenting data, not integrating it. It also isn't a storage strategy, which concerns where and how data is stored. In real-world use, data integration often involves connectors, data mapping, and harmonising disparate data so analytics across sources makes sense.

Data integration is about bringing data from different sources into a single, usable view so you can analyse it across systems. The statement describing pre-built data connections to multiple source systems so visualisations can be created as soon as systems are connected captures this idea: it focuses on quickly linking diverse data and making it ready for analysis and dashboards. This reflects the practical value of integration—you can pull in data from various apps, align formats, and deliver unified insights without heavy manual rebuilding.

It's not just about cleaning data—that would be data quality work—nor is it about the visualization tool itself, which is for presenting data, not integrating it. It also isn't a storage strategy, which concerns where and how data is stored. In real-world use, data integration often involves connectors, data mapping, and harmonising disparate data so analytics across sources makes sense.