Metrics and data collection form the foundation of effective Application Portfolio Management (APM). They provide measurable insights into the performance, cost, usage, and business value of applications, enabling data-driven decision-making. Without reliable metrics and accurate data, organizations risk mismanaging their application portfolios, leading to inefficiencies and missed opportunities for optimization.
In this section, we’ll explore the key metrics used in APM, methods for data collection, and how to leverage this information to drive actionable insights.
3.9.1 The Importance of Metrics in APM
Metrics serve as the quantitative indicators of an application’s value and health, helping organizations:
- Evaluate Performance: Identify which applications are underperforming or exceeding expectations.
- Optimize Costs: Uncover opportunities to reduce expenses and improve ROI.
- Support Strategic Alignment: Ensure applications are aligned with business objectives and deliver measurable value.
- Measure Progress: Track the success of rationalization, modernization, and other APM initiatives.
3.9.2 Key Metrics in APM
The following metrics are essential for understanding the health and value of an application portfolio:
- Cost Metrics:
- Total Cost of Ownership (TCO): Captures all costs associated with owning and maintaining an application, including licensing, maintenance, and infrastructure expenses.
- Cost per User: Measures the cost of supporting each active user, helping identify inefficiencies in resource allocation.
- Cost Trends: Tracks changes in costs over time to identify increases that may warrant investigation.
- Usage Metrics:
- Active Users: The number of users actively using the application within a specific time frame.
- Utilization Rates: Measures how frequently or intensively the application is used relative to its capacity.
- Adoption Metrics: Tracks the rate at which new users adopt the application, particularly after deployment or enhancements.
- Business Value Metrics:
- Revenue Contribution: Quantifies the application’s direct or indirect impact on revenue generation.
- Operational Efficiency Gains: Measures time or cost savings enabled by the application.
- Business Alignment: Evaluates how well the application supports key business processes or strategic goals.
- Technical Health Metrics:
- Application Age: Tracks the time since the application was implemented or last updated.
- Performance Metrics: Includes uptime, response time, error rates, and other indicators of technical reliability.
- Technical Debt: Quantifies unresolved issues or inefficiencies that hinder performance and maintainability.
- Risk and Compliance Metrics:
- Security Vulnerabilities: Tracks known issues, patching status, and risk exposure.
- Compliance Gaps: Identifies applications that fail to meet regulatory or internal policy requirements.
- Incident History: Logs past outages, breaches, or failures that impact business continuity.
- Lifecycle Metrics:
- Stage in Lifecycle: Indicates whether the application is in the introduction, growth, maturity, or decline phase.
- Planned End-of-Life: Tracks retirement dates to ensure timely planning for replacements or decommissioning.
3.9.3 Methods for Data Collection
Effective data collection is essential to ensure metrics are accurate and actionable. Common methods include:
- Automated Discovery Tools:
- Tools like Flexera, ServiceNow, or Lansweeper can automatically scan the IT environment, collecting data on application usage, costs, and dependencies.
- Surveys and Stakeholder Interviews:
- Engage application owners, business units, and IT teams to capture qualitative insights and context that tools may miss.
- Integration with Existing Systems:
- Leverage data from financial systems (e.g., cost tracking), configuration management databases (CMDBs), and monitoring tools.
- Monitoring and Analytics:
- Use application performance monitoring tools (e.g., Dynatrace, AppDynamics) to gather real-time metrics like uptime, response times, and usage patterns.
- Manual Audits:
- Conduct periodic reviews of application documentation, contracts, and historical data to fill gaps or verify automated findings.
3.9.4 Challenges in Data Collection
- Incomplete Data:
- Applications in shadow IT or undocumented systems can lead to gaps.
- Solution: Use discovery tools and cross-functional collaboration to uncover hidden applications.
- Data Silos:
- Metrics scattered across different systems or teams can hinder comprehensive analysis.
- Solution: Consolidate data into a centralized repository or dashboard.
- Low Data Quality:
- Inaccurate or outdated information can distort insights.
- Solution: Validate and cleanse data regularly, establishing clear ownership for updates.
- Resistance from Stakeholders:
- Business units may be hesitant to share data due to concerns about control or transparency.
- Solution: Communicate the benefits of data-driven APM and involve stakeholders early in the process.
3.9.5 Using Metrics to Drive Insights
- Establish Benchmarks:
- Use metrics to set performance benchmarks for applications, comparing them to industry standards or internal targets.
- Categorize Applications:
- Apply frameworks like the TIME model (Tolerate, Invest, Migrate, Eliminate) using collected data to determine appropriate actions.
- Identify Quick Wins:
- Highlight applications with obvious inefficiencies or redundancies for immediate rationalization or improvement.
- Measure Progress:
- Track how metrics evolve over time to evaluate the success of APM initiatives and inform future efforts.
3.9.6 Best Practices for Data Collection and Metrics
- Start with the Basics:
- Focus on collecting a manageable set of critical metrics initially, expanding as the organization’s APM maturity grows.
- Leverage Automation:
- Use tools to reduce manual effort and ensure data accuracy.
- Collaborate Across Teams:
- Involve IT, business, and finance stakeholders to capture diverse perspectives and data points.
- Establish Governance:
- Define processes for data collection, validation, and reporting to maintain consistency and reliability.
3.9.7 Key Takeaways
- Metrics and data collection are essential for evaluating application performance, cost, and business value.
- Focus on collecting key metrics, including cost, usage, business value, technical health, and risk.
- Leverage tools and cross-functional collaboration to ensure accurate and comprehensive data.
- Use metrics to drive actionable insights, such as identifying rationalization opportunities and measuring progress.
In the next section, we will discuss Common Frameworks and Standards for APM.
