[DOCS] Anomaly detection: Visualize delayed data (#75098)

docs/reference/ml/anomaly-detection/ml-delayed-data-detection.asciidoc

@@ -18,15 +18,15 @@ cluster.
 
 == Why worry about delayed data?
 
-This is a particularly prescient question. If data are delayed randomly (and
-consequently are missing from analysis), the results of certain types of
-functions are not really affected. In these situations, it all comes out okay in
-the end as the delayed data is distributed randomly. An example would be a `mean`
-metric for a field in a large collection of data. In this case, checking for
-delayed data may not provide much benefit. If data are consistently delayed,
-however, {anomaly-jobs} with a `low_count` function may provide false positives.
-In this situation, it would be useful to see if data comes in after an anomaly is
-recorded so that you can determine a next course of action.
+If data are delayed randomly (and consequently are missing from analysis), the
+results of certain types of functions are not really affected. In these
+situations, it all comes out okay in the end as the delayed data is distributed
+randomly. An example would be a `mean` metric for a field in a large collection
+of data. In this case, checking for delayed data may not provide much benefit.
+If data are consistently delayed, however, {anomaly-jobs} with a `low_count`
+function may provide false positives. In this situation, it would be useful to
+see if data comes in after an anomaly is recorded so that you can determine a
+next course of action.
 
 == How do we detect delayed data?
 
@@ -40,7 +40,16 @@ of the associated {anomaly-job}. The `doc_count` of those buckets are then
 compared with the job's finalized analysis buckets to see whether any data has
 arrived since the analysis. If there is indeed missing data due to their ingest
 delay, the end user is notified. For example, you can see annotations in {kib}
-for the periods where these delays occur.
+for the periods where these delays occur:
+
+[role="screenshot"]
+image::images/ml-annotations.png["Delayed data annotations in the Single Metric Viewer"]
+
+There is another tool for visualizing the delayed data on the *Annotations* tab
+in the {anomaly-detect} job management page:
+
+[role="screenshot"]
+image::images/ml-datafeed-chart.png["Delayed data in the {dfeed} chart"]
 
 == What to do about delayed data?
 
@@ -50,4 +59,4 @@ delayed data is too great or the situation calls for it, the next course of
 action to consider is to increase the `query_delay` of the datafeed. This
 increased delay allows more time for data to be indexed. If you have real-time
 constraints, however, an increased delay might not be desirable. In which case,
-you would have to {ref}/tune-for-indexing-speed.html[tune for better indexing speed].
+you would have to {ref}/tune-for-indexing-speed.html[tune for better indexing speed].