diff --git a/server/website/website/tasks/async_tasks.py b/server/website/website/tasks/async_tasks.py
index c686053..849ad32 100644
--- a/server/website/website/tasks/async_tasks.py
+++ b/server/website/website/tasks/async_tasks.py
@@ -152,28 +152,22 @@ def clean_metric_data(metric_matrix, metric_labels, session):
     metric_cat = [session.target_objective]
     for metric_obj in metric_objs:
         metric_cat.append(metric_obj.name)
-    matrix = np.array(metric_matrix)
     missing_columns = sorted(set(metric_cat) - set(metric_labels))
     unused_columns = set(metric_labels) - set(metric_cat)
     LOG.debug("clean_metric_data added %d metrics and removed %d metric.", len(missing_columns),
               len(unused_columns))
-    # If columns are missing from the matrix
-    if missing_columns:
-        for metric in missing_columns:
-            # append a missing column after the last column
-            index = matrix.shape[1]  # pylint: disable=unsubscriptable-object
-            default_val = 0
-            matrix = np.insert(matrix, index, default_val, axis=1)
-            metric_labels.append(metric)
+    default_val = 0
+    metric_cat_size = len(metric_cat)
+    matrix = np.ones((len(metric_matrix), metric_cat_size)) * default_val
+    metric_labels_dict = {n: i for i, n in enumerate(metric_labels)}
+    # column labels in matrix has the same order as ones in metric catalog
+    # missing values are filled with default_val
+    for i, metric_name in enumerate(metric_cat):
+        if metric_name in metric_labels_dict:
+            index = metric_labels_dict[metric_name]
+            matrix[:, i] = metric_matrix[:, index]
     LOG.debug(matrix.shape)
-    # If they are useless columns in the matrix
-    if unused_columns:
-        indexes = [i for i, n in enumerate(metric_labels) if n in unused_columns]
-        # Delete unused columns
-        matrix = np.delete(matrix, indexes, 1)
-        for i in sorted(indexes, reverse=True):
-            del metric_labels[i]
-    return matrix, metric_labels
+    return matrix, metric_cat
 
 
 def save_execution_time(start_ts, fn, result):