In short, a data-driven architecture contains the following components—at least all the systems I've seen have them—or can be reduced to these components:
Data ingest: We need to collect the data from systems and devices. Most of the systems have logs, or at least an option to write files into a local filesystem. Some can have capabilities to report information to network-based interfaces such as syslog, but the absence of persistence layer usually means potential data loss, if not absence of audit information.
Data transformation layer: It was also historically called extract, transform, and load (ETL). Today the data transformation layer can also be used to have real-time processing, where the aggregates are computed on the most recent data. The data transformation layer is also traditionally used to reformat and index the data to be efficiently accessed by a UI component of algorithms down the pipeline.
Data analytics and machine learning engine...