diff --git a/app/commands.py b/app/commands.py
index e04e52a8b..922314d83 100644
--- a/app/commands.py
+++ b/app/commands.py
@@ -30,6 +30,7 @@ from app.dao.fact_billing_dao import (
     get_service_ids_that_need_billing_populated,
     update_fact_billing,
 )
+from app.dao.fact_processing_time_dao import insert_update_processing_time
 from app.dao.jobs_dao import dao_get_job_by_id
 from app.dao.organisation_dao import dao_get_organisation_by_email_address, dao_add_service_to_organisation
 
@@ -57,7 +58,7 @@ from app.models import (
     Domain,
     Service,
     EmailBranding,
-    LetterBranding,
+    LetterBranding, FactProcessingTime,
 )
 from app.performance_platform.processing_time import send_processing_time_for_start_and_end
 from app.utils import DATETIME_FORMAT, get_london_midnight_in_utc, get_midnight_for_day_before
@@ -927,3 +928,78 @@ def process_row_from_job(job_id, job_row_number):
             notification_id = process_row(row, template, job, job.service)
             current_app.logger.info("Process row {} for job {} created notification_id: {}".format(
                 job_row_number, job_id, notification_id))
+
+
+@notify_command(name='load-processing-time-data')
+@click.option('-f', '--file_name', required=True, help='Text file contain json data for processing time')
+def load_processing_time_data(file_name):
+    # This method loads the data from a text file that was downloaded from
+    # https://www.performance.service.gov.uk/data/govuk-notify/processing-time?flatten=true&duration=30&group_by=status&period=day&collect=count%3Asum&format=json  ## noqa
+    # The data is formatted as a json
+    # {"data": [
+    #     {
+    #       "_count": 1.0,
+    #       "_end_at": "2021-01-27T00:00:00+00:00",
+    #       "_start_at": "2021-01-26T00:00:00+00:00",
+    #       "count:sum": 4024207.0,
+    #       "status": "messages-within-10-secs"
+    #     },
+    #     {
+    #       "_count": 1.0,
+    #       "_end_at": "2021-01-27T00:00:00+00:00",
+    #       "_start_at": "2021-01-26T00:00:00+00:00",
+    #       "count:sum": 4243204.0,
+    #       "status": "messages-total"
+    #     },
+    # ]}
+    #
+    # Using the fact_processing_time_dao.insert_update_processing_time means if this method is run more than once
+    # it will not throw an exception.
+
+    file = open(file_name)
+
+    file_contents = ""
+    for line in file:
+        file_contents += line
+    data = json.loads(file_contents)
+    normalised = []
+
+    class ProcesingTimeData:
+        bst_date = datetime(1990, 1, 1).date()
+        messages_total = 0
+        messages_within_10_secs = 0
+
+        def __eq__(self, obj):
+            return isinstance(obj, ProcesingTimeData) and obj.bst_date == self.bst_date
+
+        def set_bst_date(self, value):
+            self.bst_date = value
+
+        def set_m(self, status, value):
+            if status == 'messages-total':
+                self.messages_total = value
+            elif status == 'messages-within-10-secs':
+                self.messages_within_10_secs = value
+
+    for entry in data['data']:
+        bst_date = datetime.strptime(entry['_start_at'][0:10], "%Y-%m-%d").date()
+        status = entry['status']
+        value = entry['count:sum']
+        obj = ProcesingTimeData()
+        obj.set_bst_date(bst_date)
+        if obj in normalised:
+            normalised[normalised.index(obj)].set_m(status, value)
+        else:
+            d = ProcesingTimeData()
+            d.set_bst_date(bst_date)
+            d.set_m(status, value)
+            normalised.append(d)
+    for n in normalised:
+        print(n.bst_date, n.messages_total, n.messages_within_10_secs)
+        fact_processing_time = FactProcessingTime(bst_date=n.bst_date,
+                                                  messages_total=n.messages_total,
+                                                  messages_within_10_secs=n.messages_within_10_secs
+                                                  )
+        insert_update_processing_time(fact_processing_time)
+
+    print("Done loading processing time data.")