Recently, I had the pleasure of racing in a Spartan Race in San Francisco. For those unfamiliar with the event, it involves running long stretches across uneven terrain until you reach your next “reward” which turns out to be one of twenty grueling obstacles, such as scaling high walls, climbing 20-foot ropes, or traversing uneven monkey bars.
The reason I enjoy this event so much is the unpredictability. You can prepare by running, lifting weights, and executing hundreds of cross-training exercises, but race day obstacles are a known unknown. You know the obstacles exist, but you don’t know their exact type, order, or placement. To succeed, athletes must employ predictive agility based on the data and training at their disposal.
The “Known Unknowns” of Enterprise IT
What does an obstacle course have to do with preventing IT outages?
According to Gartner research, “Poor data quality is a primary reason for 40% of all business initiatives failing to achieve their targeted benefits.” Just as with a Spartan Race, modern IT operations teams must balance strict discipline with the agility to respond to the “known unknowns” that threaten hybrid-cloud environments daily.
The concept of the known unknown surfaces every time a catastrophic, global IT outage makes headlines. When we look at the massive airline groundings, banking disruptions, and cascading cloud-region failures of recent years, a terrifying pattern emerges: these outages are almost always self-inflicted. They are triggered by routine automated updates or botched deployment scripts pushing changes to undetected dependencies.
These failures could have been avoided with the right data architecture. The questions remain the same: Were these organizations making automated decisions based on high-quality data? Did their Configuration Management Database (CMDB) actually map the dependencies they were about to alter?
Data Quality: The Prerequisite for Change Management
In almost every major enterprise outage, the root cause is ultimately linked to a failure in change management. Effective change management relies entirely on Site Reliability Engineers (SREs) and automation engines understanding the exact relationships and dependencies of the infrastructure.
At Blazent, after years of analyzing massive enterprise IT estates, we have found that at any given moment, up to 40% of an organization’s IT data is inaccurate, stale, or incomplete.
IT complexity is scaling exponentially. Enterprise architects know that system failures and zero-day vulnerabilities are lurking around the corner they just don’t know when or where they will appear. To reduce risk, increase CI/CD agility, and survive the known unknowns, IT must operate on a foundation of infallible data.
Rebalancing the Data Equation
Blazent is committed to keeping enterprise IT out of the failure spotlight. To achieve this, the Blazent Data Intelligence Platform acts as an automated reconciliation engine, driving that 40% bad data metric down.
To successfully predict and prevent failure, an enterprise data baseline must be:
- All-Encompassing: It must ingest multi-source telemetry from every network scanner, cloud hypervisor, and security agent. If an asset exists, it must be mapped.
- Real-Time & Actionable: Batch-processing is dead. Data must reflect the immediate, ephemeral state of the network to support automated AIOps and ITSM routing.
- Association-Rich: Raw CIs are useless without business context. The data must explicitly map application dependencies, FinOps ownership, and access controls.
- History-Rich: It must provide an immutable, auditable trail of state changes to satisfy continuous compliance and security frameworks.
When a catastrophic failure occurs, the spotlight shines on IT for all the wrong reasons. What is your IT team doing right now to prepare for the next known unknown? If your data baseline is broken, you are simply waiting for your turn in the spotlight.
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