When analyzing support operations using Helpshift Dashboard Analytics or tools like PowerBI, it’s important to understand the type of dataset you're working with. In our analytics platform, two commonly used datasets encapsulating Issue Analytics are Issues Activity and Issues Object. They serve different purposes and can produce different results depending on how the summarization of the metrics is done across time.

This guide explains the differences, use cases, and limitations of each dataset so you can use the right one for your reporting needs.

Issues Activity Dataset

The Issues Activity dataset tracks all actions taken on issues, such as replies, resolutions, rejections, CSAT scores, and more, based on when those actions occurred, not when the issue was created or was resolved, etc. This dataset is used for the Issues Trends, Smart Intents reports on Dashboard analytics, and Support metrics API.

Best used for:

• Tracking daily trends of actions (e.g., how many issue resolutions happened yesterday)
• Viewing time-based performance across key support metrics
• Powering Dashboard Analytics with fast, high-level summaries

Example:

• An issue was created on Dec 30, but an agent replied on Jan 3.
  • If you're looking at metrics for Jan 1–7, the reply would appear in this window, because the action happened during that time, even though the issue was created earlier.
• The “Issues Resolves Yesterday” metric will include any issue resolutions yesterday, regardless of when it was created.
• Issues can get reopened and resolved again; this dataset will count all the resolutions and attribute them to the time points when they happened.

Limitations:

• No issue-level drill-down: You can only view aggregated metrics, not individual issue records.
• Hard to build flexible custom reports since predefined fields (pivots) are fixed for summarization

Issues Object Dataset

The Issues Object dataset, used primarily in PowerBI Support analytics app, Issue metrics API, Foundational analytics and more, has one record for every issue containing issue-level metrics and the current state of issue data and metadata. The dataset has multiple columns that can be used as time pivots for summarization, eg Issue creation time or Issue resolve time, etc.

Best used for:

• Drilling into specific issues to investigate trends or outliers
• Building reports with custom fields (CIFs) and attributes

Example:
Assuming the report uses Issue creation time as the time pivot for summarization, and an issue was created on December 30th and has a reply on January 3rd, the reply would not be counted in the "outbound message" metric for the date range of January 1st to January 7th. It would show up only when the report is being viewed from December 30th to January 7th.

If an issue created on Dec 30th gets a CSAT rating on Jan 3rd, the CSAT rating for the Avg CSAT metric would be attributed to Dec 30th if the report uses Issue creation timestamp, but would be attributed to Jan 3rd if the report uses CSAT timestamp as summarization time pivot.

Limitations:
Historical trend reporting (like average CSAT or Time to First Response over time) is possible, but summarization can be done using time pivots limited to the available timestamp datapoints, eg Issue creation time, Resolve time, First response time, etc.

Summary of Key Differences

Conclusion

Understanding the differences between Issues Activity and Issues Object datasets is important for effective data analysis and reporting. Depending on your needs and objectives, you may choose to use one or both of these datasets in your data analysis and reporting strategy. By being aware of these datasets and how they impact your analysis, you can make informed decisions and ensure that you are using the right data to measure and track your key metrics.