The Data Problem Nobody Wants to Own

Every IAM team wants better analytics, cleaner access reviews, and AI that actually helps people make better decisions. What gets far less attention is the less glamorous part underneath all of it: the state of the identity data itself.

Identity data often gets treated as a technical detail in the background. In reality, its quality shapes everything that comes next. Fragmented or inconsistent data makes it harder to trust the analytics, governance decisions, and AI built on top of it.

Nexis is named in the 2026 Gartner® Establish an Identity Data Layer to Reduce Technical Debt and Secure AI report. [1]

And we think that distinction deserves more attention.

More Visibility Does Not Fix the Data Beneath It

For years, much of the IAM conversation has focused on visibility. Connect more systems, bring the data together, and create a better view of who has access to what.

That is important. But it does not automatically make the underlying data better.

An identity can still exist twice. An account can still have no clear owner. An entitlement can still lack context. And different systems can still tell different stories about the same person or access right.

This is where Gartner’s framing is particularly relevant. Gartner recommends “treating IAM technical debt as a data management and governance challenge rather than solely as a tooling challenge, by setting up a centralized identity data layer.”[1] Gartner also cautions that “adding new identity visibility and intelligence tools without well-defined data management processes can amplify the consequences of poor IAM data.”[1]

We see the same risk. A new visibility layer can make fragmented data easier to see, but it does not make that data trustworthy by itself. Without correlation, ownership, validation, and governance, organizations can end up with a better-looking view of the same underlying problems.

AI Does Not Make the Data Problem Disappear

AI makes this even more important.

Gartner reports that “while more than 80% of enterprises plan to increase investment in traditional and generative AI by 2026, only 40% of AI prototypes reach production, primarily due to challenges with data availability and quality.”[1]

For us, there is a clear lesson here for IAM: adding AI does not make poor identity data disappear.

If an AI system works with fragmented or incorrectly correlated access data, the quality of its output will suffer as well. Gartner specifically points to “inaccurate recommendations, incomplete access-path analysis, incorrect ownership attribution, and unreliable automation as potential consequences when AI agents operate on fragmented IAM data.”[1]

That is why we believe the discussion about AI in identity governance needs to start one level earlier. Before asking how intelligent the model is, we should ask how reliable the identity context is that we give it.

A Dashboard Is Only the Beginning

There is another principle in the report that strongly resonates with how we approach identity governance at Nexis: bringing data together is not the end goal.

Gartner emphasizes the importance of bidirectional data flows. “Information should not only move from source systems into a central data layer for analysis; approved corrections and improvements should also be able to flow back into operational systems. Without those downward flows and the continuous improvement of data quality, Gartner notes that the data layer remains a reporting tool.”[1]

This distinction matters to us.

Finding excessive access creates value when that access can actually be reviewed and adjusted. Identifying a segregation of duties conflict matters when someone can resolve or document it. Finding an entitlement without a clear owner matters when that information can be corrected and carried back into the governance process.

Visibility helps us understand the problem. Governance is what allows us to do something about it.

How NEXIS Approaches This

This connection between data, context, and action is central to how we have built NEXIS.

The NEXIS Platform brings identity and authorization data from different systems together and helps uncover inconsistencies and governance gaps across that data. Rather than looking at identities, accounts, roles, and entitlements in isolation, NEXIS makes their relationships visible and usable for governance.

Identity Grids and more than 20 matrix views turn complex relationships into views that IAM teams and business users can understand and validate. Cross-application segregation of duties checks add another perspective by identifying conflicts that may remain hidden when individual systems are assessed separately.

But visibility is only one part of the process.

NICO, the NEXIS Intelligent Co-Pilot, supports recertification and access decisions with recommendations and provides reasoning to help reviewers understand them. NEXIS combines this with capabilities for mining, simulation, recertification, and identity security posture management to move from identifying issues toward improving the underlying access landscape.

The objective is not a one-time cleanup. Identity data changes whenever people move roles, applications change, new accounts appear, or access is added and removed. For us, maintaining identity quality therefore has to be part of an ongoing governance process.

That is also where we believe AI-ready identity governance begins: with identity data you can understand and trust, and governance processes that allow you to act on it.

See how NEXIS brings identity data, intelligence, and governance together in a live walkthrough.

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References

[1] Gartner, Establish an Identity Data Layer to Reduce Technical Debt and Secure AI, Nat Krishnan, 31 August 2026. Gartner subscribers can access the report here: https://www.gartner.com/en/documents/8332317

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