Notifications
Clear all
Topic starter
02/08/2026 11:49 am
TL;DR: Identity data is now spread across HR systems, directories, cloud apps, on-premises tools, and third-party platforms, making unified visibility difficult and driving a first-mile governance gap, according to DataBahn. When identity data is fragmented, enforcement, auditing, automation, and lifecycle control all degrade at the point where security decisions begin.
NHIMG editorial — based on content published by DataBahn: Why are Legacy SIEMs a problem? Identity Data Management and how DataBahn solves the first-mile identity data challenge
By the numbers:
- 93% of organizations experiencing two or more identity-related breaches in the past year
- Only 5.7% of organisations have full visibility into their service accounts
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface
Questions worth separating out
Q: What breaks when identity data is fragmented across directories and cloud providers?
A: Governance breaks first.
Q: Why does identity data normalization matter for IAM and IGA?
A: Normalization makes identity records comparable across HR, directory, cloud, and third-party systems.
Q: How should organisations measure whether identity governance is actually working?
A: Organisations should measure whether governance reduces incident cost, manual workload, and time to detect or contain risky access.
Practitioner guidance
- Build a governed identity source inventory Map every authoritative and derived identity source, including HR, directories, cloud apps, SaaS tools, and third-party systems, then mark which fields each source owns and which fields must be reconciled elsewhere.
- Normalize identities before automating reviews Define canonical structures for person, contractor, partner, service account, entitlement, and device records so access reviews compare like with like.
- Track lineage for every entitlement change Preserve the source system, timestamp, and transformation history for each identity or access record so auditors and automation can trace how the current state was derived.
What's in the full article
DataBahn's full article covers the operational detail this post intentionally leaves for the source:
- No-code onboarding and integration patterns for adding new identity data sources across cloud and custom environments
- Parsing, normalization, and enrichment mechanics for turning raw identity records into a usable governance dataset
- Identity data lake architecture details for lineage, multi-source correlation, and compliance-oriented storage
- Implementation examples showing how a unified identity framework supports provisioning and deprovisioning workflows
👉 Read DataBahn's analysis of the first-mile identity data challenge →
Identity data fragmentation: what IAM teams need to fix first?
Explore further
02/08/2026 12:59 pm
First-mile identity data is the control plane for governance, not a back-office plumbing issue. If identity records are fragmented at intake, every downstream control is forced to reconcile inconsistency instead of enforcing policy. That weakens access certification, entitlement analytics, and automation across IAM, IGA, and NHI programmes. The practical conclusion is that governance quality is bounded by the quality of identity data at ingestion.
A question worth separating out:
Q: Who is accountable when identity data quality causes a compliance failure?
A: Accountability usually sits with the control owner, the identity governance function, and the teams operating the source systems that feed the evidence chain. If population, ownership, or lineage defects are left unowned, then no one can defend the resulting access decisions under audit. Good governance assigns a named owner to the data as well as the control.
👉 Read our full editorial: Identity data fragmentation is the real first-mile governance gap
