Key Performance Indicators (KPIs) | IT Functions (PowerPoint PPTX)

This document provides a collection of 100+ Key Performance Indicators (KPIs) related to the Information Technology functions listed further below.

Keeping in mind that each organization is different, the enclosed KPIs are intended as a general reference and their relevance depends on the specific circumstances of the company. Some KPIs are relevant to more than one function.

The key to effective performance management is not just about choosing the right KPIs, but also about using them in the right way. The document starts off with brief points to keep in mind when applying any KPI.

Contents
1. Application Development and Maintenance
2. Data Management and Analytics
3. Enterprise Architecture
4. Information Security
5. IT Governance and Compliance
6. IT Service Management
7. Networking
8. Software Engineering and Quality Assurance
9. System Administration
10. User Support and Training

For each KPI listed, where relevant, the following elements are covered:
• The name of the function
• The name of the indicator, including common alternate names
• A brief description of the indicator and its importance
• Common measurement approach, interpretation, frequency, unit of measure and others
• Other information about the measure (e.g. suitability for peer bench marking, and others)

This comprehensive guide dives deep into the specifics of each KPI, providing clear definitions, measurement techniques, and interpretation guidelines. Each KPI is meticulously detailed to ensure you can implement and monitor them effectively within your IT functions. The PPT also highlights the importance of peer benchmarking, enabling you to compare your performance against industry standards.

The KPIs covered span across critical IT areas such as Application Development, Data Management, and Information Security. For instance, the "Code Review Completion Rate" and "Software Release Frequency" KPIs are essential for assessing the efficiency and quality of your software development processes. Similarly, metrics like "Data Completeness Rate" and "Mean Time to Resolve Data Issues" are pivotal for maintaining high data quality and operational efficiency.