diff --git a/nlu/pipe/utils/data_conversion_utils.py b/nlu/pipe/utils/data_conversion_utils.py
index 3741a21e..7743d238 100644
--- a/nlu/pipe/utils/data_conversion_utils.py
+++ b/nlu/pipe/utils/data_conversion_utils.py
@@ -13,18 +13,25 @@
 from pyspark.sql.types import StringType, StructType, StructField
 
 
+class NluDataParseException(Exception):
+    """Custom exception class"""
+
+    def __init__(self, message="An error occurred parsing data with NLU"):
+        self.message = message
+        super().__init__(self.message)
+
 class DataConversionUtils:
     # Modin aswell but optional, so we dont import the type yet
     supported_types = [pyspark.sql.DataFrame, pd.DataFrame, pd.Series, np.ndarray]
 
     @staticmethod
     def except_text_col_not_found(cols):
-        raise ValueError(
+        raise NluDataParseException(
             f'Could not find column named "text" in input Pandas Dataframe. Please ensure one column named such exists. Columns in DF are : {cols} ')
 
     @staticmethod
     def except_invalid_question_data_format(cols):
-        raise ValueError(
+        raise NluDataParseException(
             f'You input data format is invalid for question answering with span classification.'
             f'Make sure you have at least 2 columns in you dataset, named context/question  for pandas Dataframes'
             f'For Strings/Iterables/Tuples make sure to use the format `question|||context` or (question,context) ')
@@ -301,7 +308,6 @@ def to_spark_df(data, spark_sess, raw_text_column='text', is_span_data=False, is
                 # TODO invalid Table Data Format Exception
                 pass
             if isinstance(data[0], str):
-
                 return DataConversionUtils.table_question_str_to_sdf(data, spark_sess)
             if isinstance(data[0], pd.DataFrame):
                 return DataConversionUtils.table_question_pdf_to_sdf(data, spark_sess)
@@ -321,6 +327,8 @@ def to_spark_df(data, spark_sess, raw_text_column='text', is_span_data=False, is
                         return DataConversionUtils.question_tuple_iterable_to_sdf(data, spark_sess)
                     elif isinstance(data[0], str):
                         return DataConversionUtils.question_str_iterable_to_sdf(data, spark_sess)
+            except NluDataParseException as err :
+                raise err
             except:
                 ValueError("Data could not be converted to Spark Dataframe for internal conversion.")
         else:
diff --git a/nlu/pipe/utils/predict_helper.py b/nlu/pipe/utils/predict_helper.py
index d4d4e72d..9a2cb4de 100644
--- a/nlu/pipe/utils/predict_helper.py
+++ b/nlu/pipe/utils/predict_helper.py
@@ -8,7 +8,7 @@
 from sparknlp.common import AnnotatorType
 
 from nlu.pipe.utils.audio_data_conversion_utils import AudioDataConversionUtils
-from nlu.pipe.utils.data_conversion_utils import DataConversionUtils
+from nlu.pipe.utils.data_conversion_utils import DataConversionUtils, NluDataParseException
 from nlu.pipe.utils.ocr_data_conversion_utils import OcrDataConversionUtils
 
 logger = logging.getLogger('nlu')
@@ -364,12 +364,14 @@ def __predict__(pipe, data, output_level, positions, keep_stranger_features, met
         try:
             return __predict_standard_spark(pipe, data, output_level, positions, keep_stranger_features, metadata,
                                             drop_irrelevant_cols, return_spark_df, get_embeddings)
+        except NluDataParseException as err:
+            logger.warning(f"Predictions Failed={err}")
+            raise err
         except Exception as err:
             logger.warning(f"Predictions Failed={err}")
             pipe.print_exception_err(err)
             raise Exception("Failure to process data with NLU")
 
-
 def debug_print_pipe_cols(pipe):
     for c in pipe.components:
         print(f'{c.spark_input_column_names}->{c.name}->{c.spark_output_column_names}')