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Industries — Mobility & urban services

Steering urban operations with dated data.

Knowing what happened isn't enough. You need to know what state the network was in that day: which zones, which rates, which equipment was in service.

The situation

The field moves faster than reference systems

Transactions, sensor readings, checks, interventions: data flows arrive continuously, produced by different systems and sometimes by different providers.

Meanwhile, the physical reference data changes: a zone is redrawn, a rate is revised, equipment is replaced. If these changes overwrite the previous state, a year-over-year comparison no longer makes sense, and a reported figure can no longer be justified.

beVault retains flows and reference data with their dates, making each period analyzable in its own context.

What historization enables

Four concrete use cases

  1. 01

    Reconstruct a date

    Retrieve the state of the network, the zones and the rates applicable at a given period, with no manual reconstruction.

  2. 02

    Compare periods

    Compare two quarters while accounting for scope changes that occurred between them.

  3. 03

    Track physical registries

    Equipment, zones and intervention points change in the field; their successive states are preserved.

  4. 04

    Reconcile field data

    Readings, checks and interventions from separate systems share the same Business Keys once modeled.

What you get

The same data supports day-to-day operational steering and the reports requested by the supervisory authority.

Indicators are documented and linked to their source, making it possible to explain a published figure rather than recalculate it on demand. Deployment can stay within your perimeter when your organization's framework requires it.

Validated reference

parking.brussels

The Brussels parking operator, on a governed Data Vault foundation for its operations.

Next step

Tell us about a field data flow and a metric you need to justify. We'll start from there.

Discuss your operational data environment