7.8 Basic Data Analysis Techniques for APM

After collecting and organizing application data, the next step in the Application Portfolio Management (APM) process is data analysis. This step is crucial for turning raw data into actionable insights that inform decisions such as which applications to keep, invest in, retire, or consolidate. For beginners, it’s essential to focus on simple, effective analysis techniques that provide immediate value without requiring advanced tools or expertise.

1. Purpose of Data Analysis in APM

The primary goals of data analysis in APM are to:

• Identify redundant or underperforming applications.
• Highlight high-cost or low-value applications.
• Align the application portfolio with business objectives.
• Enable informed decisions about rationalization, modernization, or retirement.

Tip: Start with questions like, “Which applications are costing us the most?” or “Are there applications that aren’t being fully utilized?”

2. Prioritizing Analysis Areas

For beginners, focus on areas that deliver the most impactful insights. Common priorities include:

• Cost Analysis: Identifying high-cost applications for potential optimization.
• Usage Trends: Uncovering underutilized or redundant applications.
• Business Alignment: Ensuring applications support key business objectives.
• Risk Assessment: Identifying applications with compliance gaps or technical debt.

Tip: Define clear objectives for the analysis to avoid getting overwhelmed by too much data.

3. Aggregating and Summarizing Data

The first step in analysis is to aggregate and summarize the collected data. Techniques include:

• Using Pivot Tables: Summarize data by key attributes such as cost, usage, or department.
  • Example: Total cost of applications per business unit.
• Creating Totals and Averages: Calculate total costs, average usage, or other key metrics for the portfolio.
• Categorizing Applications: Group applications by lifecycle stage, business function, or criticality.

Tip: Spreadsheets like Excel or Google Sheets are excellent for creating quick summaries and pivot tables.

4. Analyzing Application Costs

Cost analysis helps identify applications that consume the most resources and whether they deliver proportional value. Key techniques include:

• TCO Breakdown: Evaluate total cost of ownership (TCO), including licensing, maintenance, and support.
• Cost vs. Value Comparison: Assess whether high-cost applications provide significant business value.
• Identifying Outliers: Flag applications with disproportionately high costs compared to others in the same category.

Example: An application with high maintenance costs but low usage might be a candidate for retirement.

5. Usage and Adoption Analysis

Analyzing usage data reveals which applications are widely adopted and which are underutilized. Techniques include:

• Identifying Low-Usage Applications: Highlight applications with minimal user activity.
• Correlation Analysis: Compare usage metrics with business value to find mismatches.
• Adoption Trends: Analyze whether application usage is increasing, stable, or declining.

Example: A low-usage application that also has low business value is a strong candidate for decommissioning.

6. Business Alignment Assessment

This analysis ensures that applications support key business goals and deliver value. Techniques include:

• Mapping Applications to Business Functions: Identify which processes or departments rely on each application.
• Scoring Business Value: Use a scoring model (e.g., High, Medium, Low) to evaluate each application’s contribution to organizational objectives.
• Dependency Analysis: Determine which applications are critical for interdependent systems or business workflows.

Tip: Applications with low alignment to business goals may warrant de-prioritization.

7. Risk and Compliance Assessment

Analyzing risk data helps mitigate vulnerabilities and ensure regulatory compliance. Techniques include:

• Vulnerability Identification: Highlight applications with known security risks.
• Compliance Checks: Ensure applications meet requirements for GDPR, HIPAA, or other standards.
• Lifecycle Risk Analysis: Identify aging applications nearing end-of-life that may pose operational risks.

Tip: Use a simple matrix (e.g., High, Medium, Low) to assess application risk levels.

8. Visualizing Data for Better Insights

Visualization makes it easier to communicate findings and spot patterns. Techniques include:

• Bar Charts: Compare costs or usage across applications.
• Pie Charts: Show portfolio composition by category (e.g., business unit, lifecycle stage).
• Heatmaps: Highlight high-cost, high-risk applications.

Tools: Microsoft Power BI, Tableau, or Excel can create beginner-friendly visualizations.

9. Applying Simple Scoring Models

Scoring models simplify decision-making by assigning numerical values to applications based on predefined criteria (e.g., cost, usage, business value).

• Steps to Build a Scoring Model:
  1. Define evaluation criteria (e.g., cost, usage, risk).
  2. Assign weights to each criterion based on importance.
  3. Calculate a total score for each application.

Example:

Applications with higher scores are prioritized for investment or retention.

10. Identifying Quick Wins

Quick wins are opportunities to deliver immediate value with minimal effort. Techniques include:

• Duplicate Applications: Identify and consolidate redundant tools performing the same function.
• Underutilized Licenses: Reallocate or retire licenses for applications with low usage.
• Obvious High-Cost Outliers: Target high-cost applications for immediate review or optimization.

Example: Eliminating a redundant subscription service can save costs without impacting operations.

11. Communicating Findings to Stakeholders

Clear communication ensures that stakeholders understand and support portfolio decisions. Key techniques include:

• Simplified Reports: Summarize findings in simple, actionable terms.
• Visual Dashboards: Use graphs and charts to highlight key insights.
• Actionable Recommendations: Provide specific next steps for each application (e.g., retire, invest, monitor).

Tip: Tailor the level of detail to the audience—for executives, focus on high-level trends; for IT teams, provide detailed analysis.

12. Iterating and Improving Analysis

Data analysis is not a one-time task. As the portfolio evolves, revisit analysis techniques to ensure they remain effective.

• Periodic Reviews: Schedule regular analysis (e.g., quarterly) to track changes and trends.
• Expand Metrics: Add new criteria as APM maturity grows (e.g., innovation potential, customer satisfaction).
• Incorporate Feedback: Refine techniques based on stakeholder input and lessons learned.

Conclusion

Basic data analysis techniques empower organizations to extract actionable insights from their application portfolios, even in the early stages of APM. By focusing on key areas like cost, usage, business alignment, and risk, organizations can identify opportunities for improvement and prioritize actions that deliver the greatest value. As these techniques are mastered, they provide a strong foundation for more advanced analysis in the future.