> For the complete documentation index, see [llms.txt](https://docs.inforiver.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.inforiver.com/release-notes/inforiver-writeback-matrix/inforiver-writeback-matrix-march-2025-v4.1.md).
# Inforiver Writeback Matrix March 2025 - v4.1
As part of this release, we've brought performance improvements in Infobridge and new identifiers to capture the last updated user/email for writeback.
### 1. Infobridge - Performance optimization for data input updates
We've optimized the data preparation time for updates to data input values. Large datasets with more than 90k records can be significantly processed faster. Data preparation that previously used to take a few minutes to complete, can now be completed in seconds.
### 2. Writeback for last updated user and email
The Writeback Matrix offers built-in housekeeping columns to capture the last updated user and time for data input fields. However, these fields cannot be exported while writing back.
Instead of using the built-in housekeeping columns, you can leverage the SETVALUE function with the new LOGGEDIN\_NAME and LOGGEDIN\_EMAIL identifiers and store them in a text field. [Learn how to use the SETVALUE function](https://docs.inforiver.com/release-notes/inforiver-writeback-matrix/pages/2kzfFfcvh7IbHib9onSw#id-1.-using-scripts).
In this example, we're capturing the user name when the approval checkbox is changed and the user email when the project status single select is changed.
Capturing the user name in a text field
Capturing the email ID in a text field
The text fields can then be written back to the preferred destination(s).
### Bug fix
* Date data input fields were throwing an "Invalid date" error when the SETVALUE function was used to assign NULL values. This issue has been remediated.
---
# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.
## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.
Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:
```
GET https://docs.inforiver.com/release-notes/inforiver-writeback-matrix/inforiver-writeback-matrix-march-2025-v4.1.md?ask=&goal=
```
`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
