Siloed IT System of Record Reports Are Not Analytics

Siloed IT System of Record Reports Are Not Analytics
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In the midst of the ongoing pandemic, the reliability of systems and applications is a greater priority than ever before. The server loads for products like SaaS applications and teleconferencing software have never been higher. Enterprise networks are also being tested like never before.

IT teams need to be capable of seeing all information available in order to anticipate challenges, dedicate appropriate resources, and keep business activities humming along. Except there’s one major roadblock to this visibility: the platforms IT organizations use tend to generate their own silos, which contributes to organizational myopathy — an inability to see the entire road ahead. 

When valuable data is siloed in various systems, IT leaders may miss out on information that would allow them to act, such as the underlying pain points that drive service dissatisfaction. They may miss key relationships, which could point to problem root causes or process improvement opportunities. Most worrisome of all, they may be unable to create the metrics needed to anticipate looming major incidents and other critical risks.

To ensure business success enabled through a 360° view of all relevant metrics, IT leaders need to be capable of breaking down silos and aggregating all relevant data. Unfortunately, many IT business analytics solutions lack the capability to integrate data across all major platforms. 

A best-of-breed IT business analytics solution can use pre-built data adapters to import and manage data across all vital systems of record. The ability to aggregate data not only eliminates blind spots, but it also allows for the creation of more accurate and actionable metrics that propel business goals.

Why the Reporting Functionality of Your System of Record May Never Be Enough

Key IT platforms tend to have at least some self-reporting capabilities. These reports are often based on pre-built KPIs and common metrics. They will have limited capability to answer new questions on the fly, drill down, slice and dice, or perform other, more custom analyses.

More importantly, these reporting functions tend to only source data used internally by the program. This creates skewed metrics and a limited view, obscuring critically important factors IT leaders need to be visible in order to make optimal decisions. 

Because of these limitations, popular platforms like ServiceNow only provide part of the picture since they only contain some of the relevant data. IT leaders tend to “stack” these limited views to gain more insight, which gives the illusion of control over key performance metrics. In reality, each of these fragmented views can introduce data quality issues that make objective decision-making impossible.

To accurately aggregate all of the data for, say, an “average system downtime” metric, IT groups would need to manually assemble all of the needed metrics from each key system of record into a spreadsheet or custom database. The fact that some IT systems of record may use different methods for calculating individual metrics would create an inaccurate apples-to-oranges comparison in these scenarios. Systems from all stages of DevOps and ITSM need to be capable of “talking to one another” in the sense of being able to intermingle all relevant data. This data can all be standardized according to a canonical data model for truly accurate 1:1 comparisons, and then it is analyzed to reveal KPI levels and produce actionable insights.

Analytics Requires Data From Across the IT System Landscape

Each major system of record plays a pivotal role in IT processes. Each also produces unique data offering a specific view of the domain or activities that the platform concerns. Combining their views using data analytics best practices contributes to a true, omniscient view of IT functions and processes as a whole.

A highly practical use case involves measuring change-related risk. IT leaders can combine activities of change managers with information sourced from the DevOps toolchain, APM, and other monitoring systems — all in order to determine which specific change activities, teams, or individuals contribute the highest risk of change failure. Combining multiple metrics sourced from these can allow the creation of an aggregate “change risk credit score.” 

By referring to this score on a dashboard, IT assignment groups can rapidly assess not only the current level of risk for change-related failures, but they can also investigate the source of this risk down to specific teams, groups, or even individuals. The insights gleaned from these investigations allow for the management of risk and the implementation of targeted improvements designed to alleviate change risk on the micro level.

Robust and expressive KPIs like these cannot be reasonably derived from siloed data. Any appearance otherwise — such as a SaaS application that promises “self reporting” — will provide only a limited or skewed view. Not only are the reporting functions of each individual silo too limited, but they may also introduce errors and biases because of a lack of data normalization.

In sum, true IT business analytics provides capabilities that no mere built-in reporting feature can. Having a full view throughout the organization thanks to these capabilities gives IT leaders more control over outcomes and more tools than ever to lead dramatic improvements.

These service improvements come at a time we people depend on our networks, applications, and systems to provide flawless performance. In a time when most of us are forced to collaborate across great distances, so too must our data be working together seamlessly, rather than sitting ineffectively in isolation.

Eliminating silos and implementing AI tools can augment your teams’ abilities, leading to greater clarity, productivity, efficiency, and capacity to innovate. Find out more about the advantages in our upcoming AIOps webinar: “How AIOps helps IT Change and Service Management be more reliable and nimble

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