What's ITSI? IT Service Intelligence Explained

The goal of AIOps is to automate IT operations with intelligence embedded into every step of the process workflow. The reality here is that operations teams in modern enterprise IT environments face a swathe of challenges:

  • Siloed view of information. Data complexity obscures relevant information and actionable insights. Dashboards show an all-green while incidents impact end-user experience and service performance.
  • Too many tools, dashboards and reports. Teams rely on manual collaboration and sharing practices to keep up with the latest updates, this can lead to inaccurate or extraneous data sources.
  • Large volumes of event and incident data logs. ITOps teams remain overwhelmed with event noise, stopping them from identifying and targeting key issues at hand.
  • Slow incident response. Information processing and analysis speed can limit incident response timing.

This complexity also makes the prospect of asset discovery, data aggregation and analysis major hurdles when pursuing AIOps implementation.

To overcome the complexity hurdle, and to address these operational challenges, IT service intelligence has become a catalyst for reaching AIOps goals.

ITSI definition and benefits

IT Service Intelligence (ITSI) refers to the use of AI-powered tools for real-time monitoring and analytics of IT services in complex multi-cloud and hybrid IT environments. ITSI plays a key role in real-time monitoring and analysis for: 

  • Proactive incident management
  • Root cause analysis
  • Service dependability 
  • Risk management

Putting it on paper is a little different than putting it into action, so let’s break down a quick example:

A single metric may seem anomalous, say, high CPU consumption during a given process — but if the CPU is running hot, is it also necessarily a business problem? How does this metric performance impact the overall service health? It may not be possible to make a well-informed decision by looking at a single metric in isolation. 

ITSI adds context to the events data at the data aggregation stage, where data logs from siloed network zones are captured and analyzed within an integrated data platform. The results are displayed on a unified dashboard — removing the need for separate monitoring tools across all siloed regions of the network.

ITSI features & use cases

That’s not where the use cases begin and end, some of the key functions of ITSI include:

Log aggregation

Data logs are generated at network endpoints and nodes across siloed sections of the network and independent application components. This information is captured in real-time and made available for analytics use cases after initial preprocessing.

(Read more about log management & log analytics.) 

Predictive analytics 

ITSI generates notable event insights when log data patterns deviate from acceptable thresholds. Using predictive analytics for anomaly detection, for instance, alerts ITOps teams to take corrective measures proactively.

Asset discovery

ITOps can track applications and service instances that are provisioned dynamically using log data analysis. ITSI enhances this capability by mapping dependencies between application components and services, enabling well-informed decisions regarding financial and infrastructure resource management.

IT automation and control

The scale of network operations makes it challenging for ITOps to manage and operate the vast pool of infrastructure resources manually. 

ITSI allows ITOps teams to combine automation with intelligence, which enables automatic enforcement of security and infrastructure management policies as the health, behavior and performance of the network evolves.

AIOps and ITSI

ITSI is, by definition, nearly inseparable from the world of AIOps. 

AIOps applies insights from big data with analytics and machine learning to automate and improve IT operations. Likewise, ITSI is all about the use of advanced machine learning algorithms to model system behavior and enable metrics-related decision-making based on adaptable thresholds. 

Achieving this kind of decision making requires that teams overcome two related challenges:

Data complexity

In a feature-rich, highly dimensional system — one that captures information on many descriptors, variables and classes — the sheer complexity of the data means that tedious data preprocessing is required. 

To tackle this, a large machine learning model is required to accurately capture the long-term dependencies and behavioral attributes of large-volume metric streams. 

AIOps and ITSI are powerful. but any real-time adaptability and learning of the model is also resource intensive and requires internal expertise to develop and deploy the right machine learning model for the specific analytics and service intelligence use case.

Lack of business alignment

It’s not uncommon for organizations to have too many dashboards and reports, each providing varying levels of business insights and knowledge. This makes it challenging for executives to make data-driven decisions. 

Machine learning algorithms that power service intelligence can keep track of the evolution of metrics, and the adaptability of models make it easier to incorporate changing decision criteria. This knowledge output reflects in a single unified dashboard interface instead of creating multiple versions of truth across all monitoring tools (or dashboards and reports).

Disparate data sources

Organizational data is not only complex, but it also comes from an increasingly wide network of sources. With unique systems across separate teams, vast arrays of IoT devices generating endless data streams and more data available to us in general, ITOps can struggle to visualize everything that’s going on.

As IT service intelligence systems obtain a comprehensive view of all data sources, a simplified and consistent data aggregation and processing framework can be adopted. This leads to an efficient data pipeline process that can easily expand to integrate multiple, distributed and often isolated data sources — contributing to a more accurate contextual view of the IT infrastructure and operations performance of the network.

ITSI and the future of AIOps

In the realm of IT operations, ITSI emerges as a transformative solution to intricate challenges. 

Complexity, tool proliferation, data deluge, and slow response times hinder operations teams. ITSI addresses that data complexity aligns business needs, and consolidates disparate data sources. As organizations strive for efficient, intelligent IT operations, the synergy of AIOps and ITSI paves the way for streamlined, responsive, and resilient IT ecosystems.

What is Splunk?

This posting does not necessarily represent Splunk's position, strategies or opinion.

Muhammad Raza
Posted by

Muhammad Raza

Muhammad Raza is a technology writer who specializes in cybersecurity, software development and machine learning and AI.