State of Data Testing

Data is the backbone of your business. ETL testing ensures that your data is accurate, reliable, and trustworthy.

Innovative trends linked with digital customers, such as mobile commerce, e-commerce, and social media, have resulted in a plethora of data stored in social media communications, emails, blogs, videos, and phone calls. Enterprises face a huge challenge with processing such large and diverse data sets to better understand customer behavior. According to recent studies, this difficulty will continue to expand in future years.

What is Data Testing?

Data testing is a type of software testing that verifies whether the data stored in a system or database is accurate, complete, consistent, and conforms to the expected format.

In simple terms, data testing ensures that the data entered a system is correct and valid. It checks for any errors, inconsistencies, or missing information in the data, and ensures that it is stored in the right format and in the right location.

For instance, if a healthcare system is being tested, data testing could involve verifying that the patient records have accurate and complete information such as name, age, medical history, diagnosis, and treatment plan. Data testing would also check for any missing or incorrect data in the records, such as incorrect spelling or wrong medication dosage.

Data testing is essential to ensure the integrity of the data and prevent any errors that could occur because of incorrect or incomplete data. It is a critical step in software development and is commonly used in various applications, such as financial systems, e-commerce websites, and other data-intensive systems.

What is ETL Testing?

ETL testing is a type of software testing that is performed to ensure that the data is extracted from the source system, transformed according to the business rules, and loaded into the target system correctly.

In simple terms, ETL testing is used to verify that the data is correctly processed during the ETL (Extract, Transform, Load) process. The ETL process involves extracting data from various sources, cleaning and transforming the data to match the target schema or format, and then loading it into the target system such as a data warehouse or database.

For instance, if a retail company wants to perform ETL testing on its sales data, the ETL testing would involve verifying that the data is correctly extracted from various sources such as Point of Sale (POS) systems, transformed according to business rules such as converting currencies or aggregating sales by region or product, and loaded into the data warehouse.

ETL testing ensures that the data is consistent, accurate, and reliable and is essential for decision-making processes. It helps to identify any data quality issues, such as incorrect or missing data, and ensure that the data is valid for reporting and analysis purposes. ETL testing is commonly used in various industries such as banking, healthcare, and retail, where data processing and analysis play a critical role in decision making.

Four Facets of Data Quality

To understand data quality, we can think broadly of four facets: accuracy, completeness, consistency, and integrity.

Data Accuracy is the most easily understood measure and refers to how close the underlying data matches the real world.

Data Completeness refers to how much data is available in the database. You would want every customer and every order captured, not just a subset.

Data Consistency refers to the consistency of the data concerning itself. Names of entities should be the same across tables, but even columns should have consistent naming conventions.

Data Integrity refers to the relationships between different tables within a database. A simple example: Every order should have a customer, and every customer that made an order should exist.

Big Data is the trend that is revolutionizing society and its organizations due to the capabilities it provides, to take advantage of a wide variety of data, in large volumes and with speed.  

Sogeti's approach to data testing can help you verify and validate your business and data requirements implementations.

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  • Satyabrata Dash
    Satyabrata Dash
    Senior Consultant, Quality Engineering & Testing at Sogeti Ireland