The scheduled path from source to output, for data feeds and for models, with alerts when either the feed or its quality checks fail.
We build data feeds that arrive on schedule, with alerts when a feed fails or when the data arriving fails its quality checks, such as the pipeline that brings open banking data into your warehouse and on into the models and reports that use it.
We build the path from data to model output as one repeatable pipeline, so every option is tested through the same code.
Ask what happens when a feed arrives late or arrives wrong. Plenty of pipelines move data reliably and tell nobody the data itself has degraded, which is how a drifted field reaches a regulatory report unnoticed. At Gini we build pipelines that clean and validate on the way through, carry lineage with them, and raise an alert when a feed fails or when the data fails its checks.
Somebody named gets an alert while there is still time to act, and the pipeline Gini builds stops rather than publishing a partial load. Dependencies are declared, so a downstream model does not run on data that never arrived. Each check records what it tested and when, which is the evidence trail your auditor asks for.
Usually, yes, and the case for it is the reporting calendar rather than the headcount. At Gini we automate the extraction, cleaning and reconciliation steps, keep the outputs identical to what the manual process produced, then hand your analysts the checks instead of the keystrokes. The month-end that took four days tends to be the first thing clients ask us to look at.
The ability to answer a challenge without an investigation. When a supervisor or an auditor asks where a figure came from, the lineage Gini builds names the source record, the transformations applied and the version of the code that applied them. It also makes impact assessment possible, since you can see which reports a proposed source change would move before you make it.
