Introduction

Product Backlog Item (PBI) sorting is a critical feature designed to help Product Owners order work effectively, ensuring that the most important backlog items are addressed in the right order. This sorting process leverages advanced rules to automatically organize PBIs based on dependencies, regulatory requirements, and logical task sequencing, saving your team time and improving efficiency.

Beta Notice

Spark-powered Product Backlog sorting is a beta function. Artificial intelligences like Spark are continuously growing and learning and, as a result, how your product backlog is sorted depends on what Spark has learned so far. If you sort the same Product Backlog multiple times, you may get slightly different results each time. Regardless, Spark will never know as much about your product backlog as you do, so making adjustments after the sorting is complete is not uncommon.

Why and When to Use PBI Sorting

Why Use PBI Sorting:

• Value and Goal Focus: To ensure that high-value, goal-critical backlog items are completed first.
• Dependency Management: To automatically identify and respect task dependencies, ensuring that prerequisite tasks are completed before dependent ones.
• Regulatory Compliance: To handle tasks that must adhere to standards or regulatory requirements, guaranteeing that these “preferential dependencies” are prioritized correctly.
• Efficient Workflow: To streamline the backlog, reducing the cognitive load on your team by automatically organizing tasks into a logical, actionable order.

When to Use PBI Sorting:

• Sprint Planning: Before planning your sprint, use sorting to ensure that the highest priority items and those with dependencies are organized effectively.
• Backlog Refinement: During backlog refinement sessions, sorting helps maintain an organized backlog that reflects current priorities and dependencies.
• Release Planning: When planning a release, sorting ensures that all critical tasks are identified and ordered appropriately, aligning with release goals and regulatory requirements.

What to Expect During Sorting

When you initiate a PBI sort, Spark will:

1. Analyze Each PBI:
   • Spark evaluates each PBI using a set of predefined criteria, including the product goal, logical dependencies, regulatory requirements, and backlog item sequencing rules.
   • A well-stated product goal plays a crucial role in this analysis. If your product goal is clearly defined and aligned with your backlog items, Spark can more accurately order backlog items that align with the product goal. This makes a significant difference in how effectively the backlog is ordered.
2. Examine Titles and Descriptions:
   • Spark uses the title and description of each PBI to gain a fuller understanding of the task. To optimize sorting, it’s important to put the most critical information at the beginning of the title and description. This ensures that Spark captures the key aspects of each backlog item right away.
   • Consistent language is also vital. Using the same terminology across your backlog items (e.g., always referring to a “nurse” as “nurse” instead of alternating with “caregiver” or “practitioner”) helps Spark maintain clarity and avoid confusion during the sorting process.
3. Identify Dependencies:
   • Spark flags any dependencies between tasks, such as research backlog items (“spikes”) that must precede implementation or backlog items that are successors to others. This ensures that prerequisite backlog items are prioritized and completed before dependent ones.
4. Recognize Preferential Dependencies:
   • Spark attempts to identify any backlog items that must be completed due to regulatory or standards requirements. These “preferential dependencies” are automatically ordered to ensure compliance and prevent delays in related work.

Exclusion of Certain Backlog Items

Backlog items that are already included in an active sprint are not included in the PBI sorting process. This exclusion ensures that ongoing work is not disrupted and that the sort focuses solely on the remaining items in the backlog.

Why Exclude Sprint Items:

• Maintaining Focus: By excluding items already in a sprint, the team can maintain focus on the committed work without changes in priority affecting the current sprint.
• Stability: This approach preserves the integrity of the sprint, ensuring that the sorted backlog only influences future planning and does not interfere with ongoing tasks.

What to Expect:

After sorting, you will see an ordered list that reflects the optimal sequence for the backlog items that are not currently in a sprint. Items in the sprint remain unchanged and continue to be managed within the sprint framework.

What to Check After Sorting

After the sort is complete, it’s important to review the ordered list to ensure it meets your expectations and aligns with your project’s needs:

1. Review Top Priorities: Ensure that the items near the top of the product backlog, especially those critical to the product goal, appear early in the list.
2. Verify Dependencies: Check that all dependencies are respected—prerequisite backlog items should be listed before dependent backlog items (e.g., spikes before implementation).
3. Confirm Preferential Dependencies: Make sure that backlog items identified as preferential dependencies (e.g., those required by regulations) are placed correctly before related work.
4. Identify Successor Relationships: Look for any backlog items flagged as successors and verify they are ordered appropriately after their predecessors.
5. Adjust if Necessary: If any backlog items seem out of place, you can manually adjust them, or re-run the sorting process if significant changes are needed.

Handling High Load Periods

During times of high load, Spark may occasionally take longer than expected to process your PBI sort request. In rare instances, if Spark doesn’t reply within the maximum time limit, the sorting process will automatically continue based solely on the value and size of the backlog items.

What This Means:

• Limited Sorting Criteria: If Spark times out, the system will still complete the sort but will only use the value and size of each backlog item to determine their order. This may result in a less nuanced prioritization, as dependencies, preferential dependencies, and the product goal won’t be fully considered.

What You Can Do:

• Retrying the Sort: If you notice that the sort was completed during a high load period, you have the option to try the sort again at a later time. By doing so, you can ensure that all factors—including logical dependencies, the product goal, and preferential dependencies—are properly considered, resulting in a more accurate and effective backlog order.

Recommendation:

• If possible, schedule your sorting operations during periods of lower activity, or be prepared to re-run the sort if you suspect that Spark timed out. This will help ensure that your backlog is prioritized according to all relevant criteria, providing your team with the most effective order of tasks.

Conclusion

PBI sorting is a powerful tool to help your team prioritize and organize the backlog efficiently, ensuring that all tasks are completed in the right order. By understanding the sorting process and reviewing the results, you can ensure your team stays focused on what’s most important, adheres to necessary regulations, and works efficiently towards your product goals.