Are you comfortable with how your IT Service Management (ITSM) platform handles the sheer volume, diversity, and velocity of your hybrid-cloud environment? Are you comfortable enough to bet your infrastructure’s uptime on it?
Most traditional ITSM platforms are structured around legacy configuration management processes. They import lists of Configuration Items (CIs) and map them together. While modern ITSM platforms are incredible systems of action, their underlying ITSM discovery tools are fundamentally flawed. They rely on single-source scanners that cannot keep pace with the breathtaking rate of IT change, severely limiting the accuracy of the Configuration Management Database (CMDB).
The Gap Between “As-Designed” and “As-Operated”
The CMDB serves as the operational hub for the enterprise. Configuration data represents various lifecycle states: as-intended, as-designed, and as-operated.
Enterprise Architecture teams are typically concerned with the as-designed state, while IT Operations desperately needs visibility into the as-operated reality. What continues to remain beyond the reach of most organizations is dependable, real-time insight into how the ecosystem is actually functioning and how automated processes are consuming infrastructure components.
Ironically, it is this exact operational insight that FinOps and AIOps teams need most to identify cost reductions and automate incident routing.
The Flaw of Native ITSM Discovery Tools
The problem in nearly every failed CMDB initiative is that IT depends on a single native discovery tool provided by their ITSM vendor.
Native ITSM discovery tools operate on a legacy assumption: that a single network scanner can find everything. This is a dangerous fiction. The sheer volume of data entering modern IT systems is overwhelming, driven by ephemeral cloud workloads, containerized microservices, and Zero-Trust edge networks. A native discovery probe simply cannot see across all these isolated silos.
Furthermore, basic discovery tools do not natively address critical variables such as false positives, false negatives, or batch-processing latency. This is exactly why Gartner historically reported that 40% of an organization’s IT data is out-of-date or inaccurate. Raw discovery and data quality are two entirely different things.
Decoupling Discovery from Data Intelligence
The good news is that organizations are no longer forced to rely on a single vendor’s scanner. By applying a dedicated Data Intelligence and reconciliation layer above your discovery tools, IT can finally achieve a dependable, real-time snapshot of runtime operations.
By utilizing an automated reconciliation engine, IT can ingest telemetry from every tool in the environment from AWS Config and Microsoft Intune to CrowdStrike and native ITSM discovery probes. The intelligence layer cross-references these streams, eliminates duplicates, and automatically maps dependency relationships based on correlated runtime events.
By decoupling data quality from the ITSM platform, companies gain a highly accurate picture of what technology exists, how it is being consumed, and the exact FinOps cost of generating that value.
The Hard Questions for Your ITSM Vendor
ITSM vendors aggressively push their native discovery tools because it drives vendor lock-in. Before committing your entire infrastructure baseline to a single vendor’s discovery capabilities, you must ask their engineers the hard questions:
- How do you handle multi-source conflict? (If my EDR says a server is Windows 2022, but your discovery tool says Windows 2019, how does the system resolve the clash without human intervention?)
- How do you address batch latency? (How do you track ephemeral Kubernetes containers that spin up and die between your scheduled discovery scans?)
- How do you identify ghost assets? (How does your scanner find a device that is deliberately firewalled off or missing an agent?)
Failing to ask these questions guarantees a polluted CMDB, triggering failed compliance audits, paralyzed AIOps routing, and catastrophic change management outages.
Blazent ensures you never operate in the dark. We act as the independent data intelligence layer, integrating seamlessly with your existing ITSM platform to reconcile your discovery processes and deliver absolute data integrity.
Discover the Blazent Data Intelligence Platform
Are you comfortable with how your IT Service Management (ITSM) processes address the volume, diversity and rate of change present in your IT environment? Comfortable enough to bet your job on it?
Most traditional ITSM systems and their associated offerings are structured around configuration management processes. These import lists of Configuration Items (CIs) from a variety of source systems, and then map the CIs together either manually or through available data. This has been the approach literally for decades, and has not kept pace with the breathtaking rate of IT change. As a result, this model severely limits the number of CI types that can be effectively managed in the Configuration Management Database (CMDB) and the rate at which environmental changes can be reflected. Current ITSM systems, by themselves, cannot keep up with this pace of change.
Configuration Management and the CMDB are the core of most IT Service Management processes. They serve as a hub to drive critical operational insights by representing the various pieces present within a business technology ecosystem and how the pieces relate to each other. Configuration data is represented using a series of lifecycle states including “as-intended,” “as-designed,” “as-implemented,” “as-operated” and “as-used.” Enterprise Architecture is typically concerned with the “as-intended” and “as-designed,“ with traditional ITSM processes and systems focused on the “as-implemented” state. Creating these states often involve significant manual efforts of inventory, mapping and modeling the components present in the IT environment.
What continues to be beyond the reach of most organizations are quality, dependable insights into how the ecosystem is actually implemented and how users and business processes are consuming various components. Ironically, it is these operational insights that organizations need most to identify cost reduction and/or value-creation opportunities.
The problem in nearly every instance is that IT depends on discovery tools to determine the breadth and depth of its service offerings. Discovery tools originated during the early 2000’s, which in Internet time may as well be a thousand years ago. The IT world has changed dramatically, and continues to do so at ever accelerating rates. The sheer volume of data entering any configuration management IT system is overwhelming, not to mention the variety and range of data sources. This entire scenario is also about to become much worse; it isn’t just that mobile technology is creating billions of entry points, but that the number is about to increase by trillions with the rise of sensor networks and the Internet of Things.
Discovery tools are meant to discover, they don’t address variables such as false positive, false negatives, lack of contextual references driven by fingerprinting capabilities, latency or granularity. These are the parameters that define the modern IT system, and the current range of discovery tools is not prepared for the task. This is why Gartner stated, “40% of an organization’s data is out of date or inaccurate” (sic). Discovery and the associated quality needed to be effective at providing a reliable referential framework are at the moment two different things.
The good news is that recent developments in the application of Data Quality Management (DQM) methods to Discovery technology are finally creating dependable snapshots of runtime operations of the ecosystem – which is a frightening prospect for many ITSM vendors. By applying DQM to Discovery, IT is able to identify CIs present in the operating environment, quickly classify them, and map dependency relationships based on both technical connections and time-based correlation of runtime events.
The value this hybrid technology offers IT organizations is tremendous. By eliminating the need for manual inventory and mapping of CIs, the Configuration Management system will now be capable of capturing and maintaining a larger volume and wider variety of ecosystem components, making this data available for more granular analysis. The addition of DQM to Discovery also supports a greater diversity of CI types by reducing the need for pre-defined classifications and structured relationships. CI types that are recognized can automatically be validated. In addition, previously undiscovered CI types can be captured and classified based on embedded data within the component as well as the context in which the component is being used within the environment.
Perhaps, the most important value of applying DQM to Discovery and configuration management (and a risk to ITSM vendors) are the capabilities to accommodate frequent changes in the operational environment (enabling the CMDB to be updated at the speed of business) and to discover relationships at runtime between users and technology components (enabling a more accurate, real-time view to be put into use). By having these combined capabilities, companies will have a much more accurate picture of what technology pieces exist within their environment, how they are being consumed and the value being created from them, including the cost of generating that value.
Most ITSM systems are consumed on a per-seat basis, and therefore the vendor’s revenue is directly tied to the number of human users – providing a disincentive to replace manual processes with automation. DQM with Discovery has the potential not only to eliminate many manual configuration management activities, but also during the future serve as a stepping stone towards automated event management, incident management and AI/Bot-based diagnosis and remediation activities (significantly reducing the need for human helpdesk agents). It is understandable that ITSM vendors don’t want their customers to know about Discovery and DQM technology. This is perhaps the single most disruptive technology threatening their future revenue potential.
Before committing to your ITSM vendors current Discovery capabilities, start asking the hard questions.
- How do they contextualize data?
- How do they address the batch disconnect?
- How do they recognize false positives or negatives?
- How do they track fingerprinting and account for it?
- , etc.
Failing to ask these types of questions leaves you in the 60% club. If you were being graded, you’d earn at best a D- grade. Do you want to be a D student, or would you rather be an A student? The current batch of discovery tools is depressing an entire industry grade curve. Blazent can quickly and easily help you ascend the curve by integrating Data Quality Management into your existing discovery processes.
To learn how to improve your grade, you can 1) visit our resource page, or 2) contact us directly at info@blazent.com