Enterprise automation, predictive analytics, and AIOps run exclusively on data. When IT leaders attempt to execute high-speed infrastructure transformations on a polluted CMDB baseline, the initiatives fail instantly.
From the Archives: The Enduring Sources of Bad Data
When we originally published the video briefing below in 2016, it was based on an extensive 451 Research report regarding enterprise data quality. A decade later, the five foundational causes of data corruption remain remarkably identical. However, the stakes have multiplied. While IT has evolved from static databases to ephemeral cloud architectures, the penalty for bad data has escalated from “inaccurate reports” to “paralyzed automated workflows.”
Based on historic analyst data and Blazent’s ongoing enterprise deployments, here are the top 5 causes of poor data quality in modern IT operations:
1. Manual Processes at a Cloud Scale
Historically, the leading cause of data corruption has always been human error during manual entry. Today, the problem isn’t just human distraction; it is the physical impossibility of humans keeping pace with cloud velocity. Attempting to manually update spreadsheet records for ephemeral cloud workloads or high-velocity edge devices guarantees CMDB obsolescence. Relying on manual updates in a dynamic environment ensures immediate data degradation.
2. Complex Migrations and Toolset Consolidation
Migrating data to new systems carries an inherent risk of data corruption. Today, this typically manifests during massive cloud migrations or when consolidating multiple legacy ITSM instances into a single platform (like ServiceNow). During a “lift and shift,” undocumented legacy dependencies, missing fields, and unmapped attributes create massive blind spots. Database systems require strict referential integrity checks that basic migration scripts simply cannot handle.
3. Lack of Normalization Across Multiple Sources
When IT teams ingest telemetry from dozens of disparate discovery and operational tools, naming conventions inevitably clash. For example, your procurement software might list a vendor as “Dell Inc.,” while your network scanner reports “Dell,” and your hypervisor reports “Dell Computer.” Without an automated data quality engine to normalize these string variances, the CMDB will spawn duplicate Configuration Items (CIs), completely breaking automated ITSM routing and asset tracking.
4. Application CI/CD and API Changes
Application developers and Quality Assurance (QA) teams are primarily focused on functional changes and feature releases, not the downstream impact on infrastructure data. In the era of high-speed CI/CD pipelines and microservices, third-party developers or automated scripts frequently change how an application functions or how an API formats its payload. These rapid structural changes can inadvertently corrupt the integrity of the data being fed into the central system of record.
5. The Complexity of Distributed Systems
In legacy IT, monolithic applications ran on isolated servers, making data relatively simple to track. Today, enterprise architectures are vastly more complex, relying on distributed microservices, multi-cloud clusters, and edge networks. As applications grow in architectural complexity and are distributed across thousands of virtual nodes, data synchronization failures and corruptions become exponentially more common and much harder to manually isolate and fix.
Eradicating the Root Causes of Bad Data
While causes of poor data quality are pervasive in hybrid-cloud environments, they are entirely solvable. Extraction, Transformation, and Load (ETL) combined with advanced Data Quality Management (DQM) technologies were engineered specifically to tackle this challenge.
Blazent provides the sophisticated reconciliation algorithms required to ingest data from multiple disparate sources, normalize conflicting attributes, and create an infallible “golden record.” By automating this data purification process, Blazent essentially eliminates the structural issues of poor data quality, empowering your enterprise to automate with absolute confidence.
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