How to Perform Continuous Data Validation Testing? Simplified 101

on Data Integration, Data Validation • July 1st, 2022 • Write for Hevo

Data Validation Testing FI

Data has become an important fuel for businesses that drives growth. Though, this data needs to be error-free, accurate, and relevant to your business needs. Since data is collected from multiple sources in different formats and goes through several transformations, you need to keep a tab on its quality. To ensure high data quality, you can perform continuous Data Validation Testing. 

In this article, you will learn in detail about Data Validation Testing and the various tests you can carry out to ensure data validity. 

Table of Contents

What is Data Validation Testing?

Data Validation Testing: what is data validation testing
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Data validation testing is the process of ensuring that the data provided is correct and complete before it is used, imported, and processed. It helps to ensure that the value of the data item comes from the specified (finite or infinite) set of tolerances. After executing the Data Validation Testing, you can ensure that data and database can successfully perform the required conversions without loss.  

Benefits of Data Validation Testing

The main purpose of carrying out Data validation testing is to ensure that the data you aggregated from multiple sources is accurate, qualitative, and meets your business requirements. Below are some of the benefits of data validation testing: 

  • Business Value: Executing Data Validation Testing assists you in determining if the unstructured or structured data you have collected from various sources meets your business needs.
  • Data accuracy: Today, unstructured data accounts for more than 80% of the enterprise data and is expected to rise at a rate of 55-65% per year. Analyzing this massive amount of data is unimaginable. Therefore, data validation testing will ensure that this data is accurate before spending more resources on data preparation.
  • Better Decisions: With the right set of statistics, you can get a clearer view of the current state of your business. This will help you make better strategies and expand market goals.
  • Improved Revenue: Chances of better conversions and higher profits rise significantly as you perform business analysis & make decisions based on accurate data.

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Why do you need to perform Data Validation testing?

When working with data, it is crucial to identify the bad data to make your analysis more accurate. You can find Bad Data in any of the following forms:

  • Missing Data: Your data filed may have null or blank values. You will mostly find this type of error in excel, VBA, Sharepoint, and XML files while conducting the validation testing issues can occur.
  • Duplicate Data: Often, you will observe that there are duplicate data entries that happen from collecting data from multiple channels in several stages. You can remedy this by data replication validation.
  • Multiple Formats: It is common to find data from sources may that have different formats.
  • Misspelled: Due to manual data entries, you will find many incorrect spellings.
  • Cluttered Data: It may be hard to go through & search for the required records when the data is cluttered.
  • Dependent Values: It is possible that the data values in a field rely on another field. For instance, consider your product data that relies on the details associated with your suppliers. This means that any error in your supplier data will show up in your product data as well.
  • Invalid Data: Consider a data field where ‘M’ is for males and ‘F’ is for females. Modifying these values can make your data invalid.

Data Validation Testing can help you identify this type of bad data and ensure that it is not corrupted or inaccurate. It also assists in verifying that the data meets all your actual business requirements. 

How to perform Data Validation Testing? 

You plan your Data validation testing into the four stages: 

  • Detailed Planning: Firstly, you have to design a basic layout and roadmap for the validation process. This blueprint will also assist your testers to check for the issues in the data source and plan the iterations required to execute the Data Validation.
  • Database Validation: You can now run the Database Validation process to ensure that data is available from source to destination. This stage compares your Source and target data fields in terms of the number of rows, data size, and overall schema.
  • Data Formating Validation: This step verifies that the target data is meaningful to the user and that it meets all business requirements.
  • Sampling: Finally, you have to now test out small data sets before starting the data processing and testing for larger data sets. Through this you can identify potential errors in the smaller sets, thereby reducing any wastage of your processing power.

Types of Data Validation Testing

There are multiple types of Data Validation Testing to ensure that only the finest data reaches the end-user:

Data Uniformity

These tests help in checking that the actual value of the entity has an exact match at multiple places. You can carry this out in 2 ways:

  • Within the Same Schema: Your data might be present in two tables within the same schema (either source system or target system). For instance, Consider a schema where ProductID is present in OrderDetails and Products table. You can do an exact match verification for ProductId that is available in OrderDetails vs Products table.
  • Checks across Multiple Schemas: There will be cases where your data migrated from the Source to the Target system, thereby 2 different schemas. For instance, consider that the ProductID is present in the Products table in both the source system and the target system. You can perform an exact match verification for ProductId in the Products table of the source system to ProductId in the Products table of the target system and confirm data uniformity.
Data Validation Testing: data uniformity
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Data Accuracy

Data Validation Testing: data accuracy
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This type of Data Validation Testing helps in checking if the data is logically accurate. This is helpful for a tester to identify data quality problems in the source system also. It is always recommended to execute this test in the target system and then check in the source system again for any defects. You can classify this test into 2 categories:  

  • Non-Numerical: This includes checking the accuracy of the non-numerical content. For instance, you consider an example of verifying the emails, pin codes & phone numbers for their valid formats.
  • Domain Analysis: This test is conducted based on the following domains: 
    • Value-Based: First you have to identify a list of values that can be present in the column and then check if column values lie in our list. For instance, checking if a gender column has either M or F in it.  
  • Range Based: According to business logic, you can define a maximum & minimum range for valid data values. You can then check if the column values lie in this range. For instance, you can set the range of 0-120 for your Age column.
  • Using a Reference File: You can use an external data file to validate data in a column. For example, you can verify if your columns have picked up the valid country codes from the reference file.

Metadata Check

While working with your tables, you also have to ensure that the Table and Column data types or the target are correctly selected, and once the design is final, check if were accurately assigned according to the data model design specifications.

You can categorize it in 2 ways:

  • Metadata Design: Firstly, you have to check if the data model for the target table is designed according to your business needs. This is important as sometimes the Data architects may migrate schema entities or can make changes while configuring the target system. After that, you need to validate if the correct scripts were made using the data models. You can go through the following checks to verify your metadata design:
  • Data Type: For instance, check if the Total Sales column can work correctly with Decimal (8, 16, or 20 bytes) or Double type?
  • Data Length: For instance, you need to consider the data length of the Address field according to all the possible entries in the geography.
  • Index: For instance, if a merger of companies is finalized and that needs a data migration. You would need to check if the Orderid is indexed properly as now the Orders table may grow 100 times in size in the target system.
  • Metadata Check Across Multiple Environments: You need to check if your Metadata is the same for both the QA and Production environments.
  • Delta Change: This identifies any anomalies that may arise when the project is in mid-way & changes are done to the source system’s metadata whereas the same did not get executed in target systems.

Data Integrity Check

Data Validation Testing: What Is Data Integrity?
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This type of Data Validation Testing concentrates on checking the integrity constraints such as Foreign key, Primary key reference, Unique, Default, etc. In the case of the foreign keys, you can verify if there are orphan records in the child table where the foreign key used is not available in the parent table.

For instance, consider a customer table with Customer ID as a Primary Key and an Orders Table as a Foreign Key. There might be a case where there exists a CusomterID in the Orders table that is not present in the Customer table. You can perform the Integrity check here and unearth such scenarios. You can take the help of the Data Mapping table on what tables has these constraints.

Data Completeness 

This type of Data Validation Testing assists in finding out the missing records or row counts between the source and target table. You can classify them in 2 ways:

  • Record Count: This is a quick sanity check to compare the net count of records for matching tables between the source and target system. You can execute them post running of the ETL or Migration job. 
  • Column Data Profiling: This is helpful when you have a huge number of records. By building logical sets of data, you can reduce the record count and then perform a comparison between source and target systems.

Data Transformation Check

In Data Validation Testing, Data Transformation is one of the major tests. Firstly, you have to review the data transformation requirements and then prepare test data in the source systems to check on various transformation cases. Out of the multitude of tests under this category, check out a short list of tests: 

  • Verify your transformations against the ETL code containing logic to reject invalid data. 
  • Check the correctness (technical and logical) of surrogate keys if any. 
  • Check if the joins or splits of field values post an ETL or Migration job are done correctly.
  • Perform tests to check the referential integrity.
  • Often, missing values are inserted via the ETL code, Check the accuracy of these.
  • Check the accuracy of your ETL or Migration scripts that have logic to correct data.
  • Write tests to check if invalid/rejected/errored data is presented to users.
  • Build a spreadsheet of cases of input data and expected results and check these with the business customer.
  • Also, validate the transformation logic around the edge cases. For instance, having a complete range of dates expected – leap years, 28/29 days for February & 30, 31 days for other months.

Data Uniqueness

This type of Data Validation Testing checks the columns if they have unique values as per the data model. You also need to consider the business logic to weed out such data. Firstly, perform tests to check if they are unique in the system & then execute tests to verify the actual duplicates. Data uniqueness can be important in the following use cases:

  • An employee record has the same sibling data twice. You can filter for duplicate data and check if it is authentic.
  • The entries for the user’s phone number should be unique in the system.
  • According to your business needs, a combination of ProductID and ProductName in the Products table should be unique as a ProductName may be duplicated.

Mandatory Fields Check

Check which fields are appointed as Mandatory Fields and check if they have values. In cases there are default values related to a field in DB, check if it is filled correctly when data is not available. For example, you can set CurrentDate as the BillDate, if the BillDate is not entered.

Null Data

In this type of Data Validation Testing, you have to check the validity of null data and verify that a critical column cannot be null. If you identify such columns for business decisions, ensure nulls are not present.

Apart from these tests, you can also go through the following tests:

  • Data Quality Testing: Ensure that bad data is handled well.
  • End-To-End Testing: Final system testing that ensures that in the endpoint you have correct data according to what we started with at the beginning of the data flow.
  • Data Warehouse Testing: Ensure that data go successfully through all points of the system that uses the data warehouse.

Conclusion

In this article, you have learned about Data Validation Testing in detail. To ensure you are working with data that is accurate and in sync with the business requirements, you need to carry out Continuous Data Validation Testing. Bad Data is present in most cases and you need to identify these cases before you start using this data for analysis. You have learned about multiple Data Validation Tests to find out these cases of Bad Data and correct them.

As you continuously collect & maintain massive volumes of data across multiple applications and databases in your business, it is vital to integrate it for a holistic business performance analysis. However, it is a time-consuming and resource-intensive task to monitor the Data Connectors continuously. To achieve this efficiently, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse, BI Tool, or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-based ETL tool such as Hevo Data.    

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