diff --git a/doc/design_ros_2.md b/doc/design_ros_2.md
index 525200c..66a439f 100644
--- a/doc/design_ros_2.md
+++ b/doc/design_ros_2.md
@@ -647,9 +647,19 @@ tracetools_analysis <-- ros2trace_analysis
 @enduml
 ```
 
-## Analysis architecture
+## Analysis
 
-With profiling as an example implementation.
+### Design
+
+Generally, for a given trace data analysis objective, the following classes are extended: `EventHandler`, `DataModel`, and `DataModelUtil`.
+
+A user/developer can implement an `EventHandler`, which defines callbacks for specific events. Those callbacks get called by the `Processor`, and end up putting slightly-processed data into a `DataModel`, which is a data container that uses `pandas` `DataFrame`s.
+
+Meaningful data can be extracted from the `DataModel`. However, a `DataModelUtil` can provide common utility functions so that users don't have to re-write them. This meaningful output data can then be presented through a Jupyter notebook (e.g. plots) or a normal Python script (e.g. tables).
+
+### Architecture
+
+With profiling as an example implementation:
 
 ```plantuml
 @startuml
@@ -669,7 +679,7 @@ abstract class Dependant {
  +dependencies(): list {static}
 }
 abstract class EventHandler {
- +handler_map
+ +handler_map(): map
  +data(): DataModel {abstract}
  +process(events) {static}
 }
@@ -684,6 +694,7 @@ EventHandler <|-- ProfileHandler
 Processor o-- EventHandler
 
 abstract class DataModel {
+ +print_data() {abstract}
 }
 class ProfileDataModel {
  +times: DataFrame
@@ -693,6 +704,7 @@ DataModel <|-- ProfileDataModel
 ProfileDataModel o-- ProfileHandler
 
 abstract class DataModelUtil {
+ +print_data(): DataModel
  +convert_time_columns(df, columns): df {static}
 }
 class ProfileDataModelUtil {