Creating a comprehensive and actionable application inventory depends on the quality and relevance of the data collected. Defining the essential data points for collection ensures that the inventory provides meaningful insights while avoiding unnecessary complexity. This section outlines the minimum viable data set (MVD) required for effective Application Portfolio Management (APM), helping organizations prioritize what matters most.
1. The Purpose of Data Collection in APM
Data collection is the cornerstone of an application inventory, supporting key APM objectives such as:
- Informed Decision-Making: Data helps evaluate applications for rationalization, modernization, or retirement.
- Portfolio Optimization: Identifying opportunities to reduce costs, mitigate risks, and align applications with business needs.
- Continuous Improvement: Establishing a foundation for tracking performance and measuring APM success over time.
By focusing on essential data points, organizations can balance completeness with practicality, ensuring the inventory is both manageable and impactful.
2. The Minimum Viable Data Set (MVD)
The MVD represents the core data points that must be captured for a functional application inventory. These data points provide enough information to support basic APM activities without overwhelming stakeholders or requiring excessive effort.
1. Identification and Classification
- Application Name: The official name of the application.
- Description: A brief summary of the application’s purpose and functionality.
- Category: Classification based on its primary use (e.g., CRM, ERP, productivity).
- Version: Current version and update history.
2. Business Value
- Business Function: The primary business process or capability supported by the application.
- Criticality: Importance to operations (e.g., mission-critical, essential, non-essential).
- Usage Metrics: Basic metrics such as number of users or frequency of use.
3. Ownership
- Business Owner: The individual or department accountable for the application’s business outcomes.
- Technical Owner: The IT team or individual responsible for maintenance and support.
4. Cost Information
- Licensing Costs: Expenses for software licenses or subscriptions.
- Maintenance Costs: Ongoing support, patches, and upgrades.
- Infrastructure Costs: Hosting or operational expenses.
5. Technical Details
- Hosting Environment: On-premises, cloud, or hybrid.
- Integration Points: Applications or systems the software integrates with.
- Lifecycle Stage: Whether the application is active, under development, or nearing end-of-life.
3. Prioritizing Data Collection Efforts
To prevent data collection from becoming overwhelming, organizations should prioritize data based on:
- Business Impact: Focus first on applications that are mission-critical or high-cost.
- Available Resources: Start with data that is easiest to collect (e.g., from existing records or automated tools).
- Immediate Needs: Collect data that supports urgent decisions, such as identifying redundant or high-risk applications.
4. Avoiding Data Overload
While it may be tempting to collect as much data as possible, excessive detail can hinder the usability of the inventory. Common pitfalls include:
- Too Much Granularity: Collecting unnecessary technical details that provide little strategic value.
- Inconsistent Data: Allowing stakeholders to define data points differently, leading to confusion or errors.
- Analysis Paralysis: Delaying inventory completion due to excessive focus on perfecting the data.
The key is to collect enough data to support actionable insights without overcomplicating the process.
5. Establishing Data Standards
Standardization is critical for ensuring consistency and usability in the inventory. Organizations should:
- Define Data Fields: Use predefined templates or forms to collect data uniformly.
- Establish Naming Conventions: Ensure application names and categories follow a consistent format.
- Set Validation Rules: Verify data accuracy and completeness before adding it to the inventory.
6. Leveraging Tools for Data Collection
Using the right tools can simplify and streamline data collection:
- Manual Tools: Spreadsheets or basic databases for small-scale inventories.
- Automated Tools: CMDB platforms, network discovery tools, and cloud monitoring software for larger environments.
- Hybrid Approaches: Combining manual input with automated scans to capture both technical and business data.
7. Iterative Data Collection
Organizations should treat data collection as an iterative process, starting small and refining the inventory over time:
- Phase 1: Collect basic data for critical applications.
- Phase 2: Expand to include secondary applications and additional data points.
- Phase 3: Periodically update the inventory to reflect changes in the application landscape.
8. Overcoming Common Challenges
Data collection can be challenging due to incomplete records, lack of stakeholder engagement, or resource constraints. Strategies to address these challenges include:
- Engaging Stakeholders: Clearly communicate the purpose and benefits of data collection to gain buy-in.
- Focusing on High-Impact Applications: Prioritize data collection for applications with the greatest cost, risk, or strategic value.
- Leveraging Automation: Use discovery tools to reduce manual effort and improve accuracy.
9. Preparing for Advanced APM Activities
The data collected during this stage serves as the foundation for more advanced APM activities, such as:
- Scoring and Prioritization: Using collected data to evaluate applications for rationalization.
- Governance and Compliance: Ensuring applications meet organizational and regulatory standards.
- Strategic Planning: Aligning the application portfolio with digital transformation initiatives.
Conclusion
Defining and collecting the essential data points for an application inventory is a critical step in building a solid foundation for APM. By focusing on the minimum viable data set, prioritizing high-impact applications, and leveraging standardized processes and tools, organizations can create an inventory that delivers actionable insights while remaining manageable. This streamlined approach ensures that the inventory supports immediate decision-making while laying the groundwork for future optimization and growth.