diff --git a/src/insight/metrics/metrics_usage.py b/src/insight/metrics/metrics_usage.py
index f0740da3..66f6a758 100644
--- a/src/insight/metrics/metrics_usage.py
+++ b/src/insight/metrics/metrics_usage.py
@@ -12,15 +12,6 @@ class OneColumnMap(DataFrameMetric):
     Mapping of a metric to each column of a dataframe.
     """
 
-    def summarize_result(self, result: pd.DataFrame):
-        """
-        Give a single value that summarizes the result of the metric. For OneColumnMap it is the mean of the results.
-
-        Args:
-            result: the result of the metric computation.
-        """
-        return result["metric_val"].mean(axis=0)
-
     def __init__(self, metric: OneColumnMetric):
         self._metric = metric
         self.name = f"{metric.name}_map"
@@ -30,11 +21,20 @@ def _compute_result(self, df: pd.DataFrame) -> pd.DataFrame:
             col: self._metric(df[col], dataset_name=df.attrs.get("name", "") + f"_{col}")
             for col in df.columns
         }
-        result = pd.DataFrame(data=columns_map.values(), index=df.columns, columns=["metric_val"])
+        result = pd.DataFrame(data=columns_map.values(), index=df.columns, columns=[self.name])
 
         result.name = self._metric.name
         return result
 
+    def summarize_result(self, result: pd.DataFrame):
+        """
+        Give a single value that summarizes the result of the metric. For OneColumnMap it is the mean of the results.
+
+        Args:
+            result: the result of the metric computation.
+        """
+        return result[self.name].mean(axis=0)
+
 
 class CorrMatrix(DataFrameMetric):
     """Computes the correlation between each pair of columns in the given dataframe
@@ -100,14 +100,6 @@ class TwoColumnMap(TwoDataFrameMetric):
     """Compares columns with the same name from two given dataframes and return a DataFrame
     with index as the column name and the columns as metric_val"""
 
-    def summarize_result(self, result: pd.DataFrame):
-        """
-        Give a single value that summarizes the result of the metric. For TwoColumnMap it is the mean of the results.
-        Args:
-            result: the result of the metric computation.
-        """
-        return result["metric_val"].mean(axis=0)
-
     def __init__(self, metric: TwoColumnMetric):
         self._metric = metric
         self.name = f"{metric.name}_map"
@@ -121,9 +113,15 @@ def _compute_result(self, df_old: pd.DataFrame, df_new: pd.DataFrame) -> pd.Data
             )
             for col in df_old.columns
         }
-        result = pd.DataFrame(
-            data=columns_map.values(), index=df_old.columns, columns=["metric_val"]
-        )
+        result = pd.DataFrame(data=columns_map.values(), index=df_old.columns, columns=[self.name])
 
         result.name = self._metric.name
         return result
+
+    def summarize_result(self, result: pd.DataFrame):
+        """
+        Give a single value that summarizes the result of the metric. For TwoColumnMap it is the mean of the results.
+        Args:
+            result: the result of the metric computation.
+        """
+        return result[self.name].mean(axis=0)
diff --git a/tests/test_metrics/test_metrics_usage.py b/tests/test_metrics/test_metrics_usage.py
index fd02313a..a5fe6a52 100644
--- a/tests/test_metrics/test_metrics_usage.py
+++ b/tests/test_metrics/test_metrics_usage.py
@@ -44,11 +44,12 @@ def test_two_column_map(data):
 
     col_map = TwoColumnMap(emd)
     emd_map_df = col_map(df1, df2)
-    assert col_map.name == f"{str(emd)}_map"
+    expected_column_name = f"{str(emd)}_map"
 
-    assert set(emd_map_df.columns.to_list()) == set(["metric_val"])
-    assert all(not np.isnan(emd_map_df["metric_val"][cat]) for cat in categorical_cols)
-    assert all(np.isnan(emd_map_df["metric_val"][cont]) for cont in continuous_cols)
+    assert col_map.name == expected_column_name
+    assert set(emd_map_df.columns.to_list()) == set([expected_column_name])
+    assert all(not np.isnan(emd_map_df[expected_column_name][cat]) for cat in categorical_cols)
+    assert all(np.isnan(emd_map_df[expected_column_name][cont]) for cont in continuous_cols)
 
 
 def test_two_column_map_with_ksd(data):
@@ -60,11 +61,12 @@ def test_two_column_map_with_ksd(data):
 
     col_map = TwoColumnMap(ksd)
     ksd_map_df = col_map(df1, df2)
-    assert col_map.name == f"{str(ksd)}_map"
+    expected_column_name = f"{str(ksd)}_map"
 
-    assert set(ksd_map_df.columns.to_list()) == set(["metric_val"])
-    assert all(not np.isnan(ksd_map_df["metric_val"][cat]) for cat in categorical_cols)
-    assert all(not np.isnan(ksd_map_df["metric_val"][cont]) for cont in continuous_cols)
+    assert col_map.name == expected_column_name
+    assert set(ksd_map_df.columns.to_list()) == set([expected_column_name])
+    assert all(not np.isnan(ksd_map_df[expected_column_name][cat]) for cat in categorical_cols)
+    assert all(not np.isnan(ksd_map_df[expected_column_name][cont]) for cont in continuous_cols)
 
 
 def test_metric_matrix(data):