Prioritizing data collection is essential to ensure that your Application Portfolio Management (APM) efforts are focused, efficient, and impactful. Attempting to collect all possible data for every application can quickly become overwhelming, especially for beginners. By strategically prioritizing what data to collect and which applications to focus on, organizations can maximize the value of their efforts while conserving resources.
This section explores effective techniques for prioritizing data collection and streamlining the process for APM beginners.
1. Identify Business Objectives for APM
Before beginning data collection, clarify the primary goals of the APM initiative. These objectives will help determine which applications and data points should be prioritized.
- Common Objectives:
- Reducing IT costs.
- Improving operational efficiency.
- Enhancing business alignment of IT.
- Mitigating risks and ensuring compliance.
Tip: Align data collection priorities with the most pressing organizational goals to demonstrate quick wins.
2. Focus on High-Impact Applications
Not all applications in the portfolio are equally important. Start by prioritizing those with the greatest potential impact on the organization.
- High-Cost Applications: Applications with significant operational, maintenance, or licensing expenses.
- Mission-Critical Systems: Applications that support core business processes or customer-facing services.
- Underutilized or Redundant Applications: Candidates for immediate rationalization.
- High-Risk Applications: Legacy systems, applications with known vulnerabilities, or those with compliance gaps.
Tip: Use initial stakeholder input or rough estimates to identify high-impact applications quickly.
3. Use the 80/20 Rule (Pareto Principle)
The 80/20 Rule suggests that 80% of the value or issues in a portfolio often stem from 20% of the applications. Focus data collection efforts on these high-value or high-risk applications.
- Examples:
- Identify the top 20% of applications based on cost.
- Focus on the 20% of applications used by the most employees.
- Prioritize applications that handle 80% of business-critical transactions.
Tip: Reviewing financial reports, usage data, or IT incident logs can help identify the 20% of applications to target.
4. Segment Applications by Category
Group applications into logical categories to make prioritization easier. Examples of segmentation include:
- Business Function: Finance, HR, Sales, Marketing, etc.
- Technology Stack: Cloud-based, on-premises, or hybrid.
- Lifecycle Stage: Development, maintenance, or nearing decommissioning.
Once segmented, focus on the most critical categories first. For instance, prioritize business-critical functions over non-essential ones.
5. Prioritize Data Points by Relevance
Instead of trying to collect every possible data point, focus on those that are most relevant to your goals. Examples include:
- Cost Data: For cost optimization efforts, prioritize data on licensing, maintenance, and operational expenses.
- Usage Metrics: For identifying underutilized applications, focus on user adoption and frequency of use.
- Risk Information: For compliance initiatives, prioritize data on vulnerabilities and regulatory requirements.
Tip: Create a checklist of essential data points aligned with your APM objectives to keep efforts focused.
6. Leverage Stakeholder Input
Involve key stakeholders to help prioritize which applications and data points matter most. Stakeholders can include:
- Application Owners: Provide insights into business-critical systems.
- IT Teams: Highlight applications with high technical debt or frequent incidents.
- Finance Teams: Identify applications with significant financial impact.
Tip: Use surveys or interviews to gather input quickly and ensure alignment with organizational priorities.
7. Start Small with a Pilot Group
Instead of tackling the entire application portfolio at once, start with a manageable subset of applications to test your data collection process.
- Choose a Pilot Group Based On:
- A single department or business unit.
- Applications within a specific category (e.g., customer-facing systems).
- High-priority applications identified through previous techniques.
Tip: A successful pilot can serve as a proof of concept to gain buy-in and refine processes before scaling.
8. Rank Applications with a Simple Prioritization Matrix
A prioritization matrix can help rank applications based on two or more key factors, such as business value and risk. For example:
| Criteria | High | Low |
| High Risk | Prioritize Now | Address Later |
| Low Risk | Monitor or Evaluate | Deprioritize |
Use this matrix to quickly categorize applications and decide where to focus data collection efforts.
9. Automate Where Possible
Automation tools can speed up data collection for high-priority applications, reducing the burden on manual efforts. Examples include:
- Discovery Tools: Automatically collect technical and usage data.
- CMDBs: Leverage existing ITSM tools to gather baseline data.
- Dashboards: Use tools like Power BI to visualize key metrics.
Tip: Start with simple tools that align with your organization’s capabilities and gradually integrate more advanced solutions.
10. Regularly Reassess Priorities
As data collection progresses, revisit your prioritization criteria to ensure they remain aligned with business goals.
- Examples of Reassessment Triggers:
- Completion of a pilot group.
- Shifts in organizational priorities (e.g., budget changes, compliance mandates).
- Discovery of unexpected issues (e.g., critical applications with missing data).
Tip: Build flexibility into your data collection plan to adapt to evolving needs.
11. Monitor and Communicate Progress
Keep stakeholders informed about data collection progress to maintain engagement and alignment.
- Regular Updates: Share summaries of completed and remaining tasks.
- Impact Stories: Highlight quick wins or insights gained from collected data.
- Challenges and Adjustments: Communicate obstacles and how they are being addressed.
Tip: Use simple visuals, such as bar charts or progress trackers, to report progress clearly.
12. Balancing Speed and Accuracy
While prioritizing speed in data collection is important, it’s equally critical to ensure data accuracy. Strive for a balance by:
- Collecting a subset of data that is both accurate and actionable.
- Deferring less critical data points to later phases.
- Conducting periodic validations to address gaps or inconsistencies.
Tip: Aim for “good enough” data quality to drive early decisions and refine as the process evolves.
Conclusion
Prioritizing data collection ensures that APM efforts remain focused and deliver maximum value with minimal effort. By identifying high-impact applications, focusing on relevant data points, and leveraging stakeholder input, organizations can streamline the process and demonstrate early successes. These techniques not only make data collection more manageable but also set the stage for more comprehensive analysis and decision-making as the APM practice matures.
