Data Quality Testing

Data quality testing is the pivotal process of validating essential dataset characteristics, ensuring alignment with anticipated expectations before consumption.

Data Quality Test

Monitor quality of data being Ingested or at rest using DQ rules & AI - DataOps DQ Monitor

Benefits of Data Quality Testing Tools

Discover the advantages of employing data quality testing tools, essential for validating accuracy, consistency, and reliability. Checks on errors, ensuring reliable insights and usable data.

Improved Decision Making

Good quality data results in better decision making

Better Compliance

In regulated industries, having trustworthy data reduces fines by governing bodies

Enhanced Operational Efficiency

Enhancing Data Quality benefits the Supply Chain, CRM, HR,  and other enterprise functions

Key Features

Completeness

This represents the degree to which data is usable or complete. If the percentage of missing values is high, it may result in sub optimal decisions. Completeness refers to the existence of all required attributes in the population of data records

Data element is

• Always required (or)
• Required based on the condition of another data element.

Example:

• Person record with a null First Name
• The person record is missing a value for marital status.
• The married (Y/N) field should have a non-null value of ‘Y’ or ‘N’ but is populated with a “null” value instead.

Completenes
Uniquenes

Uniqueness

This accounts for the degree of duplicate data in a dataset. Uniqueness refers to the singularity of records and or attributes. The objective is a single (unique) recording of data. Data element is unique — there are no duplicate values.

Example:

• Each Person should only have one record, but there are two instances of the same Person with different identifiers or spellings. • SSN should be unique, but there are two Person records that have the same social security number.

Data Quality Testing

Provides built-in data quality test for common data quality checks with the ability to create complex data quality rules from scratch or by using our Query Builder to define rules to verify that the data conforms to your quality standards.

Validity

This dimension measures how much data matches the required format for any business rules. Formatting usually includes metadata, such as valid data types, ranges, patterns, and more.

Validity is determined by

How closely data values correspond to reference tables, lists of golden values documented in metadata, and value ranges, etc. All data values are valid in relation to reference tables.

Example:

1. Country Code should be a valid value from the reference data for countries

2. Age for a Person should be less than 100 years old

Validity
Timeliness

Timeliness

This dimension refers to the readiness of the data within an expected time frame. For example, customers expect to receive an order number immediately after they have made a purchase, and that data needs to be generated in real-time.

Timeliness references

Timeliness references whether the information is available when it is expected and needed.

Example:

3. For quarterly reporting, data must be up to date by the time the data is extracted
4. Last Review Date for the policy must be within the last three years

Accuracy

This dimension refers to the correctness of the data values based on the agreed upon “source of truth.” Since there can be multiple sources which report on the same metric, it’s important to designate a primary data source; other data sources can be used to confirm the accuracy of the primary one.

Accuracy Reference

For Example, tools can check to see that each data source is trending in the same direction to bolster confidence in data accuracy. Accuracy refers to the degree to which information accurately reflects what’s being described. It can be measured against either original documents or authoritative sources and validated against defined business rules.

Example:

5. US Zip Codes should match a list of legal US postal codes
6. Person name is spelled incorrectly

Accuracy
Consistency

Consistency

This dimension evaluates data records from two different datasets. As mentioned earlier, multiple sources can be identified to report on a single metric.

Using different sources to check for consistent data trends and behavior allows organizations to trust the any actionable insights from their analyses. This logic can also be applied around relationships between data.

References

For example, the number of employees in a department should not exceed the total number of employees in a company.

Consistency means data across all systems reflects the same information and are in synch with each other across the enterprise. The absence of difference, when comparing two or more representations of a thing against a definition.

Example:

7. Employee status is terminated but pay status is active
8. Employee start date cannot be later than the Employee end date
9. N number for a Person record must be the same across systems

Fitness for Purpose

Finally, fitness of purpose helps to ensure that the data asset meets a business need. This dimension can be difficult to evaluate, particularly with new, emerging datasets.

Reference

Fitness/Conformity means the data is following the set of standard data definitions like data type, size, and format. All data values conform to the requirements of their respective field.

Example:

10. Date of Birth is listed as “26/05/1990” but should be in the format “mm/dd/yyyy“
11. “Zip Code“ contains letters but it should be numeric

Fitness for Purpose

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Optimize ETL with our data quality checks.

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Products
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DataOps Suite

End-to-End Data Testing Automation

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ETL Validator

Automate your Data Reconciliation & ETL/ELT testing

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BI Validator

Automate functional regression & performance testing of BI reports

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DQ Monitor

Monitor quality of data being Ingested or at rest using DQ rules & AI

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Test Data Manager

Maintain data privacy by generating realistic synthetic data using AI

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