Service Analytics and Intelligence

SAI

The Service Analytics and Intelligence (SAI) capability is the ability to define and quantify the relationships between IT infrastructure, IT services, and IT-enabled business processes.

Structure

SAI is made up of the following Categories and CBBs. Maturity and Planning are described at both the CC and the CBB level.

AProfiling

Validate the relationships between various businesses and IT layers to support reliable forecasting.

A1Empirical Model: Define and quantify the relationships between IT infrastructure (for example, CPU load or latency) and IT services, business processes, and ultimately, the organization. Populate models with data to act as a basis for all analysis (including historical and projected).
A2Performance Monitoring: Measure, record, and track various service performance indicators for IT infrastructure, end- to-end IT services, business processes, and the organization (based on empirical models).
A3Results Analysis: Identify the key issues relating to IT infrastructure and how they impact or are impacted by IT services, business processes, and the organization, based on empirical model outputs.

BPlanning

Align IT capacity with business requirements.

B1Capacity Trend Analysis: Size the infrastructure based on current and expected business demand — for example, by examining current performance trends or deducing expected capacity needs from the business strategy.
B2Risk Assessment: Assess the probability and impact of IT-related risks on organizational activities — for example, quantification of IT-enabled business value-at-risk.
B3Investment Scenario Planning: Understand existing capacity and identify the need for additional/new IT infrastructure to support growth of business processes.

CBusiness Interaction

Build relationships between the IT function and other business units to facilitate the planning and management of IT assets.

C1Communication Management: Establish lines of communication between the IT function and other business units regarding the management of IT infrastructure and services.
C2Partnership Management: Establish ways of managing and maintaining interactions with business units and stakeholders — these could include informal approaches, such as phone calls, emails, and courtesy visits, and formal written, signed agreements reviewed periodically.

Overview

Goal

The Service Analytics and Intelligence (SAI) capability aims to clarify the link between the performance of business processes and the performance of the underlying IT infrastructure and services — that is, to provide an end-to-end view of IT services.

Objectives

An effective Service Analytics and Intelligence (SAI) capability aims to:

Provide a quantified view of the end-to-end performance of IT services — that is, define and measure the relationship between IT infrastructure and services, and the business processes and services enabled by IT.
Map performance data from discrete IT systems (including networks, finance, voice, data, storage, processing speeds, data centres, and applications) to performance data from business processes and services, to highlight business value-at-risk, gain insights into ways of optimizing IT infrastructure and service configurations, and prioritize future investments.
Establish proactive approaches to resolving IT infrastructure and service quality problems by maintaining profiles of normal infrastructure operational characteristics, and automatically detecting deviations from norms.
Support improved decision-making on the performance of IT services at all levels of the organization — that is:
- Inform operational decision-making relating to service delivery by providing insight into matters such as performance, capacity, availability, cost, and use.
- Inform strategic decision-making by providing insight into matters such as profiling of user populations, understanding the business impact of change, and contingency planning.

Value

The Service Analytics and Intelligence (SAI) capability helps identify how best to configure IT infrastructure and services to meet business demand.

Relevance

Many organizations have installed tools and processes to monitor the performance of individual IT components (such as, for example, storage, network, or processing speeds). However, it is often more difficult to achieve an end-to-end view of IT services performance, based on the collective performance of underlying infrastructure components. And it can be equally, if not more, difficult to understand how the organization's business processes are impacted by the performance (or underperformance) of the IT infrastructure and services. Relevant information can often lie hidden in different formats across technology stacks and software layers. If properly analysed and understood, this data, which is generated at the infrastructure level by IT systems, can reveal emerging trends before they become problems, and help to identify ways of addressing their causes. Appropriate data collection and analytical approaches can enable the modelling of IT services (at all levels) to inform better decision-making¹.

By establishing an effective Service Analytics and Intelligence (SAI) capability, an organization can facilitate a data-driven decision-making culture2. Service analytics and intelligence enhances the detail and scope of IT insight, and goes beyond a one-to-one mapping between the different technology stacks and software layers to reveal the true nature of the relationships between different performance variables. Understanding performance relationships between IT services and business services can enable more timely responses to emerging service issues or potential outages. The IT function can act as an exemplar for the whole organization in adopting business intelligence data and analytics to improve operational and strategic decision-making.

Scope

Definition

The Service Analytics and Intelligence (SAI) capability is the ability to define and quantify the relationships between IT infrastructure, IT services, and IT-enabled business processes.

Improvement Planning

Practices-Outcomes-Metrics (POM)

Representative POMs are described for SAI at each level of maturity.

2Basic

Practice
Apply component-level Service Level Agreements (SLAs).
Outcome
The performance of IT infrastructure components is monitored and improved.
Metrics
- Mean time between failures (MTBF).
- Mean time to resolution (MTTR).
Practices
- Implement component monitoring processes to identify when actual performance deviates from the normal.
- Escalate if the level of deviation breaches the limits set in the corresponding service level agreement.
Outcome
Infrastructure performance metrics improve, leading to overall better predictability.
Metric
Number of incidents/SLA breaches per type/component.
Practices
- Map the infrastructure component landscape.
- Perform root cause analysis for each incident.
Outcome
Recurring incidents are minimized, with the result that the performance of the infrastructure is more predictable.
Metrics
- Number of incidents by type or service.
- IT components' availability/response time/capacity.

3Intermediate

Practices
- Map IT services to infrastructure components.
- Undertake root-cause analysis of IT service problems, examining the underlying IT infrastructure component issues that impact on IT service performance, and taking remedial actions to avoid recurrence.
Outcome
The performance of the IT environment is more predictable, and potential threats to performance are identified before they impact users.
Metrics
- Service latency/availability/utilization.
- Mean time between failures (MTBF).
- Mean time to resolution (MTTR).
Practice
Explore flexible options for sizing the IT infrastructure to match the demand for IT services.
Outcome
Investments and divestments are prioritized based on business demand and cost per service.
Metrics
- IT service cost per user.
- IT asset utilization.
- Frequency with which a risk threshold is breached.
Practices
- Conduct regular service performance reviews between IT service owners and business customers.
- Include discussions on expected business demand.
Outcome
Collaboration results in increased understanding of the business needs of the customer, and ensures that fulfilment plans are mutually agreed.
Metric
Business customer satisfaction levels with IT services.

4Advanced

Practice
Map business processes to underlying IT services to develop understanding of performance interdependencies and to facilitate root cause analysis.
Outcome
Areas are identified where IT services are constraining business performance.
Metric
Business process hours/transactions lost due to IT failures or breaches of service level agreements.
Practice
Develop the business case for investment in IT by emphasizing the relationship between business processes and the underlying IT services and infrastructure.
Outcome
Investments are prioritized to meet business process requirements.
Metric
Business process hours/transactions gained because of addition/expansion of IT services and infrastructure.
Practice
Conduct scenario planning of business value-at-risk, based on IT service and infrastructure data.
Outcome
Investments in IT can be prioritized according to their potential for mitigating business risk.
Metrics
- Cost per transaction.
- Business process hours/transactions lost due to IT failures or breaches of service level agreements.

5Optimized

Practice
Conduct ongoing validation of monitoring and modelling, comparing actual with forecasted, and amending models as required to improve accuracy.
Outcome
Planning is continually improved to support optimized resource allocation.
Metric
Cost of excess capacity. (Opportunity) cost of under capacity.
Practice
Develop an enterprise-level model of all IT-enabled business processes, thereby establishing the link from the organization and its business processes to the IT services and infrastructure.
Outcomes
- Business-level scenario planning across the entire organization is facilitated.
- This enables the impact of IT investments on the business and the impact of business plans on IT services to be holistically understood and quantified.
Metric
Percentage of enterprise processes covered by the model.

Reference

History

This capability was introduced in Revision 16 as a new critical capability.