Data Drift in IT Asset Management: How to Ensure It Doesn’t Destroy Your I&O Operations

Imagine you went through life using only the level of thinking you had five years ago. Anything that happened in those last five years, you don’t know and can’t use to make choices. Or how about if you could only make decisions using information that you believe is correct, but actually isn’t?

While this happens now and again for the average person, for IT leaders trying to use their asset data to their strategic advantage, it happens on a much larger scale.

Even when you put processes and automations in place, the information feeding those mechanisms can actually be more harmful than helpful, especially when IT teams don’t realize how stale or incorrect the data is. And because different teams often make well-intentioned changes without understanding downstream impacts, drift can spread silently until critical processes break.

That’s because of a concept called data drift. While drift can happen more easily than you may think, the key is having steps in place to proactively address changes in IT asset data and keep all data sources connected and consistently refreshed.

Keep reading to learn:

• What data drift is and what causes it
• Why drift matters specifically for IT asset management (ITAM)
• The real ways drift impacts your business
• How modern ITAM tools proactively address data drift

What Is Data Drift?

Data drift is a concept within machine learning (ML) that refers to changes in the statistical properties of data over time. In ML, drift causes model predictions and analytics to become less accurate.

In the context of IT asset management, drift causes unsynchronized, misleading, or unreliable data that invalidates your asset records, automations, and expectations.

If the foundational “truth” of assets changes faster than the systems tracking them can account for—or those systems are providing incorrect or missing data—the dataset you’re using to inform your ITAM processes becomes unreliable. This creates growing blind spots across core operational processes, especially when teams lack automated controls to detect when assumptions and reality start to diverge.

Types of Data Drift in ITAM

While data drift is the overarching term for how data properties change, more specific forms of drift describe what has changed.

1. Covariate Drift

This occurs when the distribution of input data changes and its values shift. As IT environments evolve, this can impact the accuracy of asset inventories and usage patterns.

2. Prior Probability Drift

This occurs when the target variables a model tracks change. If your patching practices or license entitlements change, compliance statuses may shift as well. Drift can cause your systems to miss those changes, leaving asset records incorrect.

3. Concept Drift

This occurs when the relationship between data inputs and outputs changes. As user behavior and vendor rules evolve, AI-supported license optimization models may break if they don’t account for these changes.

4. Schema Drift

This occurs when the structure or format of data changes. You can experience schema drift when discovery sources add new fields, SaaS vendors rename attributes, or you introduce new device categories into your ITAM program.

All these forms of data drift can have detrimental impacts on how well you can scale hardware and software asset management—and whether you capture or lose the operational and financial benefits tied to accurate data.

How Data Drift Hurts ITAM Efforts

When you consider how vital accurate, refreshed data is for effective IT asset management, it’s not surprising that data drift—left unaddressed—can throw a wrench into core areas of software and hardware asset management.

Data drift within ITAM processes significantly impacts:

1. Inventory Accuracy

Not-so-spoiler alert: modern IT environments have a lot more moving parts than they did a decade ago. Enterprise landscapes now heavily involve cloud and hybrid environments, with virtual machines and containers that may last only minutes, and SaaS users who frequently churn.

If enterprise IT asset management teams are using legacy tools that run on old models, lag in discovery times, or operate in silos, drift leads to:

• Incorrect inventory counts
• CMDB misalignment
• Out-of-date asset dashboards

And in practice, some of the most common causes of drift stem from small configuration tweaks made by one team without awareness of how those changes affect upstream or downstream processes, resulting in broken monitoring or inaccurate inventories that can persist unnoticed.

2. Security and Compliance Efforts

To maintain strong compliance and security postures, you need to know every asset’s location, configuration, patch state, and network status at any given moment.

As data remains missing or outdated because of drift, risk exposure increases from:

• Shadow IT
• Unpatched devices that incorrectly appear as patched
• Missing assets in vulnerability scans
• Outdated certifications

Security tools often rely on timely, accurate inputs, and even a small amount of drift can create blind spots that expand your attack surface and weaken automated controls.

3. License Optimization

Efforts to control IT costs depend on having accurate device and license usage data, as well as depreciation schedules. Legacy ITAM tools often fail to keep up with remote work, SaaS expansion, and BYOD trends, resulting in drift that degrades financial and compliance accuracy.

When data degrades, you get:

• Oversized and underutilized SaaS contracts
• Poor software cost forecasting
• Inaccurate contract optimization opportunities

This is especially challenging when OEM vendors don’t provide updated usage or compliance reports tied to dynamic user activity, making optimization nearly impossible without automated ways to query and reconcile usage changes.

4. Financial Forecasting + Planning

Finance teams can’t control IT costs without accurate, up-to-date asset data. This becomes more challenging when data drift fails to account for rapidly shifting SaaS spend, cloud consumption, and evolving hardware usage patterns.

You can easily:

• Overspend on unused licenses and hardware stockpiles
• Under-budget for growth areas
• Poorly plan for lifecycle stages and refresh forecasts

This also includes forecasting challenges driven by upstream inputs—such as HR headcount projections—that are often inaccurate. Without a way to reconcile these assumptions against real asset usage, financial planning becomes increasingly disconnected from reality.

5. ITAM Automation

Any automation designed to improve IT asset management depends on real-time, accurate data. Without updated models that enable frequent discovery, reconciliation, and synchronization across all sources, automation will fail to produce intended results.

In fact, Gartner predicts in their 2025 Hype Cycle for I&O Automation that by 2028, 99% of I&O-led investment in agentic AI without system data and operating model improvements will fail to achieve sustainable ROI.

This reinforces that even well-structured automation breaks down quickly when asset data drifts—even slightly—because the assumptions embedded in workflows no longer match the real environment.

What Causes Data Drift in IT Asset Management?

Data drift can occur within ITAM for any number of reasons—sometimes individually, often in combination. The key is identifying where drift originates so you can intervene before it cascades across systems.

There are five core reasons drift emerges in ITAM:

1. Constantly Changing Infrastructure

ITAM now spans hardware, software, hybrid environments, cloud workloads, and SaaS ecosystems—all of which can change daily. Many ITAM tools can’t keep pace with this velocity, allowing drift to creep in.

Legacy tools are often built to manage only hardware or only software, or to integrate with a very narrow set of systems. There’s simply no way to achieve a current, accurate view of your entire infrastructure.

And because one team may adjust a monitoring rule or discovery frequency without realizing how those changes affect downstream systems, drift can be introduced unintentionally and remain undetected until a process breaks.

2. SaaS Velocity

Of all assets within an IT environment, SaaS tools are the most vulnerable to drift. Issues like duplicate records, outdated details, and incorrect utilization data occur because:

• New features alter usage patterns
• Vendors frequently update schemas
• Users churn rapidly
• Access levels change dynamically

In many organizations, optimization is made even harder because SaaS providers often don’t supply actionable license usage or compliance reports tied to real-time user activity. Without automated ingestion and reconciliation, teams lack the visibility needed to continuously right-size software investments.

3. Manual Processes

Ah, the bane of ITAM processes everywhere. Many teams still rely on spreadsheets, email approvals, and user-submitted forms to maintain asset records.

Manual actions slow down data ingestion, introduce human error, and degrade data quality over time—making drift unavoidable.

Even small delays in updating records can cause models to diverge from reality, especially when users or devices change states faster than manual workflows can capture.

4. Tool Fragmentation

Endpoints. Cloud systems. SaaS tools. Finance, procurement, security platforms.

They all generate data—and they often operate in silos.

When ITAM tools only ingest partial data from each system and lack the capability to normalize or reconcile that information, drift emerges in the form of mismatched asset attributes and conflicting records.

This fragmentation becomes even more problematic when remediation or reconciliation depends on comparing information across systems that were never designed to share data automatically—resulting in persistent inconsistencies.

5. Shadow IT

Models can’t account for data they can’t see. Shadow IT introduces unapproved SaaS applications, rogue servers, unmanaged endpoints, and unpatched devices that traditional ITAM solutions may never detect.

When segments of your environment are invisible, drift is inevitable—and ITAM teams have no reliable way to address challenges around cost, compliance, or security.

Shadow adoption can also occur through informal channels—such as instructions shared in Slack or other messaging tools—resulting in configurations or purchases that bypass established workflows entirely.

Examples of Data Drift in IT Asset Management

Theory is helpful, but seeing how drift plays out in real scenarios makes the consequences clearer. Here are a few examples.

SaaS Behavior Drift

Let’s say your marketing team shifts from Adobe Photoshop to Figma. While Adobe usage rapidly declines, your legacy ITAM tool still predicts high demand because it updates usage patterns too slowly.

Your renewal workflow assumes outdated usage, and suddenly, you're paying thousands for licenses no one needs.

Endpoint Configuration Drift

A new version of your mobile device management (MDM) configuration is rolled out by one team, but the team responsible for compliance monitoring isn’t notified. They continue tracking compliance against the old configuration profile.

As a result, compliance dashboards appear accurate, but devices running the new profile fail to register correctly. The inconsistency goes unnoticed until an audit reveals a gap between expected and reported configurations.

CMDB Relationship Drift

You decide to migrate your Configuration Management Database (CMDB) to a new server. But because updates must be done manually and require time from individual staff members, certain fields and relationships never get updated.

Workflows run on stale or incorrect data:

• Incident management misroutes tickets
• Change management triggers false dependencies
• IT teams lose trust in CMDB reliability

CMDB drift is especially difficult to detect because teams often assume the data is trustworthy—even when supporting systems show discrepancies.

What Business Impact Does Data Drift Have on Enterprises?

When CIOs, CTOs, and ITAM leaders are fighting to reduce costs, improve compliance, and streamline operations, data drift can undermine those efforts in ways that are both costly and difficult to reverse. If drift limits your ability to maintain an accurate, single-pane-of-glass view of your asset landscape, the downstream effects quickly compound.

Below are the major areas where drift directly harms the business.

Financial Impacts

As we’ve seen, data drift almost guarantees overspending—whether through unused software licenses, mismanaged hardware refresh cycles, or inaccurate budgeting.

Finance teams can’t control IT costs without centralized, up-to-date asset data. It’s why 50% of organizations waste at least 10% of their software spend, and 18% waste at least 20%, according to YouGov research.

What’s worse than not being able to lower costs? Knowing you’re actively throwing more funds out the window.

Beyond waste, drift also makes it harder to anticipate future spend. If financial planning relies on inaccurate demand inputs—such as overly optimistic or outdated headcount forecasts—organizations may under- or over-budget by significant margins, creating uncomfortable mid-year budget conversations and delaying strategic investments.

Security Risks

When drift creates unmanaged, misconfigured, or untracked devices, it leaves your organization exposed. Assets that appear patched may not be. Devices missing from vulnerability scans may be compromised. And unmanaged applications may introduce unmonitored risk.

Threat actors are attacking enterprises at unprecedented rates—with CrowdStrike reporting a 35% year-over-year increase in interactive intrusion campaigns. At the same time, the average cost of a data breach is $4.4 million, and 32% of breaches result in fines, according to IBM.

Even minor gaps caused by drift can become footholds for attackers, especially when endpoint and SaaS data fall out of sync with security tooling.

Compliance Contradictions

Data drift can leave entitlement, ownership, and configuration records incorrect—even when organizations believe they are compliant. The consequences are real:

• Failed Audits: Fines may range from thousands to millions of dollars and can lead to reputational damage or legal action.
• License Penalties: Vendors may issue fines, suspend services, or revoke licenses.
• Fines for Inaccurate Reporting: Even unintentional reporting errors can result in certification loss or increased regulatory scrutiny.

Drift amplifies these risks by masking noncompliance behind dashboards that appear accurate, preventing teams from discovering discrepancies until it’s too late.

How Do I Identify Data Drift in ITAM Processes?

Spotting drift early is critical to avoiding these business impacts. While identifying drift manually is challenging, there are several categories of detection methods.

Statistical Methods

Technical teams may use data science techniques to detect drift, such as:

• KL Divergence: Measures how distributions shift over time.
• Population Stability Index (PSI): Detects significant changes in dataset stability.
• Chi-Square Tests: Identifies categorical changes like device type distribution.
• Kolmogorov-Smirnov Test: Finds shifts in usage patterns (e.g., login frequency).

Behavioral Monitoring

This approach identifies drift by tracking changes in:

• Login frequency
• Applications launched
• Network activity

These variations often signal shifts in user behavior or system configuration.

Data Anomaly Tracking

Drift often reveals itself through anomalies such as:

• Duplicate asset records
• Missing or stale data fields
• Unexpected drops in detected devices

Schema Change Detection

Drift occurs when metadata or structural definitions change. Watch for:

• Newly added fields
• Renamed or deprecated attributes
• Device category updates

Importantly, drift may also emerge when upstream systems evolve without proper communication to dependent teams. Detecting schema change quickly helps IT teams avoid outdated monitoring or reporting that no longer matches the real environment.

But Let’s Be Honest…

You likely don’t have time to manually run statistical tests, examine usage trends, or double-check every system dependency.

And the reality is that many of the tools IT teams rely on are contributing to drift themselves—either by providing incomplete data, ingesting it too slowly, or operating in disconnected silos.

This leads us to the last part of the story: how modern ITAM platforms, like Oomnitza, address data drift at the source.

Oomnitza Solves the Data Drift Problem

Oomnitza’s unified IT asset management platform continuously gathers, normalizes, and federates asset data across your connected systems to prevent drift and maintain a single, accurate source of truth.

Where other systems require complex configurations or manual intervention, Oomnitza automatically synchronizes data across your ecosystem using more than 1,500 turnkey connectors. As Oomnitza pulls, ingests, and reconciles data from systems like ServiceNow, Active Directory, finance tools, procurement solutions, and security platforms, you get a consistently accurate and trustworthy data foundation.

Even more importantly, Oomnitza exposes discrepancies between systems in near-real-time—enabling teams to quickly identify and address drift at its source rather than masking it behind outdated dashboards or incomplete datasets. This allows organizations to restore confidence in their CMDBs, ITSM processes, and automated workflows while reducing the manual effort typically required to diagnose data inconsistencies.

With Oomnitza, your IT asset data is continuously governed across every strategic area:

• Accurate, Real-Time Inventory: Automatically synchronized and enriched data gives you a reliable picture of your assets at any moment—without delays or blind spots.
• Closed-Loop Remediation: Changes in status, ownership, or configuration trigger automated reconciliation workflows that reduce drift and prevent rework.
• Continuous Audit-Readiness: Timestamped, defensible records provide audit-proof evidence across the entire asset lifecycle.
• Improved Cost Optimization: Real-time insights into asset assignment, usage, end-of-life, and reclamation help you eliminate waste and make informed financial decisions.
• Strengthened Security Posture: Assets are governed at every stage—even those not covered by traditional security tools—closing gaps that would otherwise increase risk.
• Unified Asset Governance: By bridging IT, procurement, security, and finance systems, Oomnitza delivers consistent asset ownership records and reduces costly inconsistencies.

Ditch the Drift with Oomnitza

With Oomnitza, you get the continuous data ingestion, normalization, and reconciliation that IT leaders need to prevent drift, maintain accuracy, and support strategic initiatives with confidence.

Instead of battling inconsistencies across sprawling systems, Oomnitza gives you visibility into where drift originates—and the automation to resolve it quickly and sustainably.

Contact our team to learn how you can maintain an accurate, single-pane-of-glass view of your entire IT asset landscape.