Last updated: October 22, 2024

How to detect invalid currency codes in a dataset using a data quality check

This sample shows how to use data quality checks to measure the percentage of valid currency codes in a column and view the results on data quality dashboards.

Overview

The following example shows how to verify that the percentage of valid currency code strings in the monitored column does not fall below set thresholds.

PROBLEM

Here is a table with some sample customer data. In this example, we will monitor the valid_currency_code column and verify that each currency code is in the correct format.

The valid_currency_code column contains currency code data. We want to verify the percent of valid currency codes on valid_currency_code column.

SOLUTION

We will verify the data of using monitoring text_valid_currency_code_percent column check. Our goal is to verify if the percentage of valid currency code values in the valid_currency_code column does not fall below the set thresholds.

In this example, we will set three minimum percentage thresholds levels for the check:

error: 80.0%

If you want to learn more about checks and threshold levels, please refer to the DQOps concept section.

VALUE

If the percentage of currency code values falls below 80.0%, an error alert will be triggered.

Data structure

The following is a fragment of the DQOps dataset. Some columns were omitted for clarity.
The valid_currency_code column of interest contains valid and invalid currency code values.

negative	usa_phone	usa_zipcode	valid_country_code	valid_currency_code
91	17400986784222	22803		pound
56	(513)134987523	6641666416	DM	koruna
-67	111111111111111	21541	AR	KHR
156	19472348976???	8604486044	CP	IRR
-3	=13261092976	30683	CO	real
-22	13805414567iowa	61914	CW	euro
3	+1(231)4561289	21520	AL	£
4	(1)5175413241	21536	BS	$
56	1(248)-541-0987	21531	AQ	533
3	(+1)5671239999	66419	GA	shilling
93	16792345678	86024		€
-1	9372346785	2280722807	TZ	denar
20		3060130601	TD	MZN
-1	14195429807	61925	CO	USD
-4	16165240542			kr
-83	13305410987	31803	HR	¥
78		86435	KY	KZT
2	13135678943	21522	AB	PYG
1	18105234567	21561	BD	zł
1	(906)6259999	86045	CM	dollar
-1	15864562433	21550	IO	peso
495	(1)6141118766	22801		$
87	(513)1349876	66552	FR	ZWD
-45	17345213489	215388888		CUP

Run the example using the user interface

A detailed explanation of how to start DQOps platform and run the example is described here.

Navigate to a list of checks

To navigate to a list of checks prepared in the example using the user interface:

Go to the Monitoring section.

The Monitoring Checks section enables the configuration of data quality checks that are designed for the daily and monthly monitoring of your data source.
Select the table or column mentioned in the example description from the tree view on the left.

On the tree view you can find the tables that you have imported. Here is more about adding connection and importing tables.
Select the Daily checkpoints tab.

This tab displays a list of data quality checks in the check editor. Learn more about navigating the check editor.

Run checks

Run the activated check using the Run check button.

You can also run all the checks for an entire subcategory of checks using the Run check button at the end of the line with the check subgroup name.

View detailed check results

Access the detailed results by clicking the Results button. The results should be similar to the one below.

Within the Results window, you will see four categories: Check results, Sensor readouts, Execution errors, and Error sampling. The Check results category shows the severity level that result from the verification of sensor readouts by set rule thresholds. The Sensor readouts category displays the values obtained by the sensors from the data source. The Execution errors category displays any error that occurred during the check's execution. The Error sampling category displays examples of invalid values in the column.

The actual value in this example is 64%, which is below the minimum threshold level set in the error (80.0%). The check gives an error result (notice the orange square to the left of the check name).

Synchronize the results with the cloud account

Synchronize the results with your DQOps cloud account using the Synchronize button located in the upper right corner of the user interface.

Synchronization ensures that the locally stored results are synced with your DQOps Cloud account, allowing you to view them on the dashboards.

Change a schedule at the connection level

With DQOps, you can easily customize when checks are run by setting schedules. You can set schedules for an entire connection, table, or individual check.

After importing new tables, DQOps sets the schedule for 12:00 P.M. (noon) every day. Follow the steps below to change the schedule.

Navigate to the Data Source section.
Choose the connection from the tree view on the left.
Click on the Schedule tab.
Select the Monitoring daily tab
Select the Run every day at and change the time, for example, to 10:00. You can also select any other option.
Once you have set the schedule, click on the Save button to save your changes.

By default, scheduler is active. You can turn it off by clicking on notification icon in the top right corner of the screen, and clicking the toggle button.

Once a schedule is set up for a particular connection, it will execute all the checks that have been configured across all tables associated with that connection.

You might also want to check the Running checks with a scheduler example.

YAML configuration file

The YAML configuration file stores both the table details and checks configurations.

In this example, we have set three minimum percentage thresholds levels for the check:

error: 80.0%

The highlighted fragments in the YAML file below represent the segment where the monitoring daily_text_valid_currency_code_percent check is configured.

If you want to learn more about checks and threshold levels, please refer to the DQOps concept section.

apiVersion: dqo/v1
kind: table
spec:
   incremental_time_window:
      daily_partitioning_recent_days: 7
      monthly_partitioning_recent_months: 1
   columns:
      id:
         type_snapshot:
            column_type: INT64
            nullable: true
      nulls:
         type_snapshot:
            column_type: STRING
            nullable: true
      nulls_ok:
         type_snapshot:
            column_type: INT64
            nullable: true
      unique_count:
         type_snapshot:
            column_type: STRING
            nullable: true
      negative:
         type_snapshot:
            column_type: INT64
            nullable: true
      usa_phone:
         type_snapshot:
            column_type: STRING
            nullable: true
      usa_phone_ok:
         type_snapshot:
            column_type: INT64
            nullable: true
      usa_zipcode:
         type_snapshot:
            column_type: STRING
            nullable: true
      usa_zipcode_ok:
         type_snapshot:
            column_type: INT64
            nullable: true
      valid_country_code:
         type_snapshot:
            column_type: STRING
            nullable: true
      valid_country_code_ok:
         type_snapshot:
            column_type: INT64
            nullable: true
      valid_currency_code:
         type_snapshot:
            column_type: STRING
            nullable: true
         monitoring_checks:
            daily:
               accepted_values:
                  daily_text_valid_currency_code_percent:
                     error:
                        min_percent: 80.0
      valid_currency_code_ok:
         type_snapshot:
            column_type: INT64
            nullable: true

In this example, we have demonstrated how to use DQOps to verify the validity of data in a column. By using the text_valid_currency_code_percent column check, we can monitor that the percentage of valid currency code strings in the monitored column does not fall below set thresholds. If it does, you will get an error result.

Next steps

You haven't installed DQOps yet? Check the detailed guide on how to install DQOps using pip or run DQOps as a Docker container.
For details on the text_valid_currency_code_percent check used in this example, go to the check details section.
You might be interested in another validity check that evaluates that the percentage of valid latitude and longitude values are above the set threshold.
Would you like to add your own connection? Here you can find information about supported databases and how to add new connection.
DQOps allows you to keep track of the issues that arise during data quality monitoring and send alert notifications directly to Slack. Learn more about incidents and Slack notifications.