Decision criterion
The only metric your management will remember: the delivery time.
No one on the executive committee will ask whether your satellite is correctly historized. They'll ask why a new source takes six weeks. Data Vault automation acts precisely there: it removes the repetitive work of generating and maintaining code, and makes delivery time predictable.
- A new source integrated in days rather than weeks
- Zero regression linked to hand-written loading code
- Automatic impact analysis before every change
- A delivery timeline that's predictable and defensible internally
What changes
What Our Clients Measure
Less hand-written loading code, more consistent models, immediate impact analysis and new use cases delivered faster. The scale of the gain depends on the number of sources and the state of the existing model.
Where time is lost
Breaking down the real turnaround time of a business request
On a manual project, time spent modeling and understanding the business is the minority. Most of it goes into writing and maintaining code.
Writing Load Code
Insert scripts, hash key management, change detection, recovery: several days per source, redone with every change. beVault generates it.
Testing and UAT
Deterministically generated code is tested once and for all. Copy-paste regressions disappear.
Impact analysis
Knowing what a change breaks downstream takes days of manual investigation. Built-in lineage answers in seconds.
Go-live
The Git Store and environment promotions replace manual deployment procedures and their windows of risk.
Calculation
How the return on investment builds up
We prefer a method you can challenge line by line rather than a number handed down as fact.
- 01
1. Current cost per integrated source
Average man-days to integrate a new source, multiplied by your fully loaded daily cost. Most of our clients start at 15 to 25 days per source.
- 02
2. Annual volume of requests
Number of sources and changes delivered per year. It's often this number, more than the unit cost, that reveals the scale of the gain.
- 03
3. Maintenance cost avoided
Time spent on load incidents, code fixes and orchestrator maintenance — costs the platform absorbs.
- 04
4. Comparison to platform cost
License and implementation compared to annual savings. The crossover generally occurs between the fourth and eighth month.
Indirect effect
The real gain isn't cost, it's regaining credibility
A data team that delivers in days changes its status within the organization: it stops being a bottleneck and becomes a partner people bring ideas to. Our clients see the number of requests increase after rollout — not because demand exploded, but because the business started asking again.
Next step