vacancy observation · 01
Data Architect
enterprise data models · cloud platforms
organisation-wide governance
DemandField · Relevance and insight
Is this opportunity worth your attention?
DemandField is built to evaluate a real vacancy against your career evidence, explain what is supported, and show where the evidence is weak or missing. Use that context to decide what deserves your attention.
Field study 01relevance in context
01Vacancydemand
02Requirementswhat is asked
03Career evidencewhat is supported
04Gapswhat needs attention
Relevancegrounded judgementevidence stays visible
Why a field
Work is a field, not a feed.
A vacancy is evidence of what an organisation needs. Your career history is evidence of what you have actually done. Relevance depends on the relationship between those two sources, not on a score detached from them.
DemandField is designed to keep both sources visible alongside the explanation: which requirements have support, which need stronger evidence, and which remain gaps. An absence of evidence is not proof that you lack a skill.
vacancyvacancyvacancyCV
isolated records
demandcareer evidencegapsrelevance
evidence and reasons together
Acquire → Evaluate → Act
Relevance sits between demand and action.
In the intended system, DemandFlow brings in usable vacancy evidence. DemandField explains its relevance to your career evidence. You choose what to pursue, and DemandForward turns that choice into a grounded CV. Vacancy evidence arrives from the outside in; career experience turns toward opportunity from the inside out.
Illustration: vacancy requirements and career evidence inform relevance and a grounded CV.Vacancy requirements and Career evidence are shown with supporting evidence, a weakly documented area and a gap. Missing evidence does not prove missing capability. The final CV uses the demonstrated experience and remains subject to human review. This illustrates the intended system.
acquired vacancies
vacancy observation · 02
Enterprise Data Lead
data platforms · platform strategy
migration across countries · data governance
career evidence
Data architecture across cloud and enterprise platforms
Defined governance across multiple systems
Led an enterprise data migration programme
illustrative relevance
data architecture
supported · demonstrated workgovernance
weak · organisational scope unclearmigration
gap · geographic scope not evidencedDemandFlow · Acquire
Bring vacancies into view.
Usable vacancy evidence, with its source preserved.
Explore DemandFlowDemandForward · Act
Move with intent.
Focused application material grounded in your career evidence.
Explore DemandForwardWhat the field preserves
Context that can compound.
Source before synthesis.
Keep observed material distinct from the interpretation built from it.
Evidence before conclusion.
Show the supporting material, weak evidence and gaps behind a relevance judgement.
Perspectives without fiction.
Change the truthful angle on the same experience without inventing a different past.
Automation without surrender.
Reduce repeated work while people remain responsible for the decision.
A better field of view
Understanding should accumulate, not reset.
Over time, repeated relevance patterns could make changing role demand, underused experience and credible capability gaps easier to recognise. That context could inform a next move; it would not predict whether an employer hires you.
Real signals. Real experience. Human judgement.