Optimization is an algorithmic procedure for finding the minimum or maximum of an objective function subject to specified constraints. It converts a practical decision problem into a structured model for choosing the best feasible solution. section 1.1.4 section 8.0
A constrained optimization model typically specifies:
- An objective function to maximize or minimize, such as profit, efficiency, priority, or cost. section 8.0
- Decision variables representing the choices available to the organization. section 8.0
- Constraints representing limits or requirements involving budgets, resources, time, or strategic balance. section 8.0 section 8.1.4
- A feasible solution that satisfies every constraint while optimizing the objective. section 8.0
Prioritizing product features
An organization can maximize the sum of feature-priority scores while selecting between nine and twelve features, committing three or four features to each epic, keeping first-year investment below $700,000, and keeping lifecycle investment below $1.1 million. section 8.1.4 section 8.1.3
Table 8.1 Features aligned with the Enhance Data Infrastructure epic
|
Feature name
|
First-year cost
|
Total cost
|
Priority score
|
|---|---|---|---|
|
Automated Data Cleaning Pipeline
|
$50,000
|
$80,000
|
1.23
|
|
Real-time Data Ingestion
|
$70,000
|
$120,000
|
1.29
|
|
Data Privacy and Compliance Tools
|
$80,000
|
$130,000
|
1.19
|
|
Real-time Data Monitoring and Alerts
|
$60,000
|
$100,000
|
1.38
|
|
Integration with External Data Sources
|
$50,000
|
$85,000
|
1.42
|
|
Feature Engineering Automation
|
$55,000
|
$90,000
|
1.45
|
Compare candidate features by first-year cost, total cost, and priority score before formulating the allocation problem. section 8.1.4
