diff --git a/src/ert/_c_wrappers/analysis/analysis_module.py b/src/ert/_c_wrappers/analysis/analysis_module.py
index f31e672392d..06ec2ec6629 100644
--- a/src/ert/_c_wrappers/analysis/analysis_module.py
+++ b/src/ert/_c_wrappers/analysis/analysis_module.py
@@ -210,7 +210,8 @@ def set_var(self, var_name: str, value: Union[float, int, bool, str]):
 
                 except ValueError:
                     raise ConfigValidationError(
-                        f"Variable {var_name!r} with value {value!r} has incorrect type."
+                        f"Variable {var_name!r} with value {value!r} has "
+                        f"incorrect type."
                         f" Expected type {var['type'].__name__!r} but received"
                         f" value {value!r} of type {type(value).__name__!r}"
                     )
diff --git a/src/ert/analysis/_es_update.py b/src/ert/analysis/_es_update.py
index aab1cf62fda..eb37395cdd4 100644
--- a/src/ert/analysis/_es_update.py
+++ b/src/ert/analysis/_es_update.py
@@ -5,7 +5,7 @@
 from dataclasses import dataclass, field
 from math import sqrt
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Callable
 
 import iterative_ensemble_smoother as ies
 import numpy as np
@@ -369,9 +369,13 @@ def analysis_ES(
     iens_active_index = [i for i in range(len(ens_mask)) if ens_mask[i]]
 
     progress_callback(Progress(Task("Loading data", 1, 3), None))
+    start = time.time()
     temp_storage = _create_temporary_parameter_storage(
         source_fs, ensemble_config, iens_active_index
     )
+    end = time.time()
+    elapsed = end - start
+    print(f"Time to run _create_temporary_parameter_storage: {elapsed}")
 
     ensemble_size = sum(ens_mask)
     param_ensemble = _param_ensemble_for_projection(
@@ -383,8 +387,9 @@ def analysis_ES(
     truncation = module.get_truncation()
     # Looping over local analysis update_step
     for update_step in updatestep:
+        print("Loading responses and observations...")
+        start = time.time()
         try:
-            print("Loading responses and observations...")
             Y, (
                 observation_values,
                 observation_errors,
@@ -400,6 +405,9 @@ def analysis_ES(
             )
         except IndexError as e:
             raise ErtAnalysisError(e) from e
+        end = time.time()
+        elapsed = end - start
+        print(f"Time to run _load_observations_and_responses: {elapsed}")
 
         # pylint: disable=unsupported-assignment-operation
         smoother_snapshot.update_step_snapshots[update_step.name] = update_snapshot
@@ -446,14 +454,17 @@ def analysis_ES(
                         np.linalg.inv(Sigma_A) @ C_AY @ np.linalg.inv(Sigma_Y)
                     )
                     c_bool = c_AY > correlation_threshold
-                    # Some parameters might be significantly correlated to the exact same
-                    # responses, making up what we call a `parameter group``.
+                    # Some parameters might be significantly correlated
+                    # to the exact same responses,
+                    # making up what we call a `parameter group``.
                     # We want to call the update only once per such parameter group
                     # to speed up computation.
                     param_groups = np.unique(c_bool, axis=0)
 
                     # Drop the parameter group that does not correlate to any responses.
-                    row_with_all_false = np.all(param_groups == False, axis=1)
+                    row_with_all_false = np.all(
+                        param_groups == False, axis=1
+                    )  # noqa: E712, E501
                     param_groups = param_groups[~row_with_all_false]
 
                     for grp in param_groups:
@@ -519,9 +530,13 @@ def analysis_ES(
                 _save_to_temporary_storage(temp_storage, [parameter], A)
 
     progress_callback(Progress(Task("Storing data", 3, 3), None))
+    start = time.time()
     _save_temporary_storage_to_disk(
         target_fs, ensemble_config, temp_storage, iens_active_index
     )
+    end = time.time()
+    elapsed = end - start
+    print(f"Time to run _save_temporary_storage_to_disk: {elapsed}")
 
 
 def analysis_IES(
@@ -581,7 +596,7 @@ def analysis_IES(
                 f"No active observations for update step: {update_step.name}."
             )
 
-        noise = rng.standard_normal(size=(len(observation_values), S.shape[1]))
+        noise = rng.standard_normal(size=(len(observation_values), Y.shape[1]))
         for parameter in update_step.parameters:
             iterative_ensemble_smoother.fit(
                 Y,