A way to see how data actually creates value in your business, from where it comes from to the decisions it enables.
How enterprises trace data from source to trusted product to real business value.
Where the data actually comes from, and how reliable it is.
Whether it's governed, owned and checked enough to act on.
Turned into something reusable, not a one off export.
Used to actually decide something, not just report on it.
The business result the decision produces.
Illustrative examples of how a data product connects a source, an owner, and the decisions that depend on it.
Sales operations, with data engineering as technical custodian
Removes the manual reconciliation sales and success teams do today across CRM, billing and support tickets before every renewal or upsell conversation.
The single most reused input for churn prediction, next-best-action and lifetime value models. Model quality tracks this product's completeness almost directly.
Identity resolution across systems is imperfect. Duplicate or merged accounts can silently double count revenue in downstream reporting.
Any decision about a CDP, CRM replacement or churn model build vs buy should treat this product's maturity as a precondition, not an afterthought.
Plant operations, with a data platform team providing pipelines
Shifts maintenance from fixed schedules to condition based intervention, which is the difference between planned downtime and unplanned line stoppage.
Directly feeds failure prediction and remaining-useful-life models. Without a clean, timestamped maintenance event history to train against, those models cannot be validated.
Sensor drift and gaps during connectivity loss are common. A model trained on unflagged gaps will learn the gap pattern, not the failure pattern.
A build vs buy decision on a predictive maintenance platform should weigh whether this signal history exists at sufficient quality before comparing vendor AI capability.
Data strategy runs on the same Donna engine as every other decision here.
Start with Donna