This article answers the question of what to do when a target was configured incorrectly and the resulting graph does not reflect the intended data. It explains why updating a target does not retroactively fix historical data, outlines how to correct the target so future data graphs properly, and provides clear options for handling past data—either by re-entering it under the corrected rules or retiring and rebuilding the target—so data integrity, reporting accuracy, and documentation are maintained.
Overview
If a target was configured incorrectly, the graph will reflect the data as it was recorded at the time. Updating a target’s settings does not retroactively change past data. To fix the graph, correct the target for all future entries and choose how to handle the historical data: leave it with a note, re-enter it, or retire and rebuild the target.
What you’ll learn
How to correct a target so new data graphs correctly
Options for handling past data so the graph matches
Safe, step-by-step workflows and documentation tips
Before you start
Ensure you have permission to edit programs and view or edit session data
Optional but recommended: export or screenshot current graphs and data for your records
Video walkthrough
Summary
Correct the target configuration so all future data is right.
Pick a repair path for historical data:
Delete and re-enter past data using the corrected rules
Retire the incorrect target and create a corrected one
Verify the graph and document what changed.
Step-by-step
1) Confirm what went wrong
Open the learner’s target’s detail view.
Identify the mismatch, for example: “affects graph” not selected under a measure, mastery criteria error such as wrong session outcome criteria settings
Note the date range impacted.
2) Correct the target for all future entries
Go to Program Builder or the target’s settings.
Update the configuration that was incorrect
Check the simulation to ensure correct opportunity outcome and session outcome
Save your changes. New data recorded from this point forward will graph correctly.
Important: Changing the program does not alter past data. Prior data points will continue to reflect the old configuration until you take one of the repair paths below.
3) Choose a repair path for historical data
Option A: Delete and re-enter the affected data
Best when accurate historical data are required for supervision, reporting, or decisions.
Identify the sessions or entries with incorrect scoring.
For each affected entry:
Open the raw data
Export or write down dates/data points to re-enter
Delete the incorrect data point(s)
Re-enter the data using the corrected configuration on the learner’s timeline for each session, going from oldest to newest
Verify the corrected values on the graph
Cautions:
Deletions are permanent. Consider exporting a copy first.
Re-entry can be time-consuming for large date ranges.
Option B: Retire and rebuild the target
Best when the structure changed significantly or you want a clean historical break.
Rename the current target to include “Archived” or the correction date.
Set the incorrect target to deactivated.
Create a new target with the correct configuration and a clear name, for example “Skill Name v2 (Corrected Oct 2025).”
Begin recording new data on the corrected target.
Pros: Clean separation of old vs new logic.
Trade-off: History is split across two targets.
4) Verify the fix
Return to Graphs and confirm that new data points reflect the corrected configuration.
Spot-check a few sessions to ensure measures, rules, and totals behave as expected.
If you re-entered data, confirm the historical lines now display correctly.
FAQs
Will updating the target fix old points automatically?No. Past data keeps the configuration it was recorded under. To change it, delete and re-enter, or retire and rebuild.
Do I need to re-enter everything?Only if you require fully accurate historical trends. Otherwise, add a clear annotation and move forward with a new target.
Pro tips
Use the simulation and check the session outcome (what will graph) before introducing new programs/targets
Keep a small “sandbox” learner or a draft program to test rule changes before applying them to live targets.
Export a CSV or take screenshots before deleting data. Data deletions are permanent!