3 Critical Disciplines to Keep in Mind While Testing Data

“Poor data quality costs businesses an average of $15 million per year.” -Gartner 

Good data is the foundation of any successful business. Forecasting, planning, or tracking a company’s long-term performance is difficult without good data. Real-time information provided by data supports decision-making and helps save time and money. So, it becomes essential to test the data better to draw accurate insights.

Organizations should typically keep these three critical disciplines in mind while testing data:

1. Data warehouse testing
2. Big data testing
3. Data migration testing

Let’s elaborate on the three critical disciplines while testing the data below.

Data Warehouse Testing

A data warehouse is a repository to store historical data from various sources. This historical data includes transactional databases, flat files, and external sources. It is a central repository of data for data collection and data analysis. It supports various business intelligence activities. These business intelligence activities include reporting, data mining, and online analytical processing (OLAP). We can transform the data and load it into the data warehouse. Here we can organize the data to facilitate analysis and reporting.

“Data warehouses are essential for organizations to gain insights into their operations and make better decisions.” -Gartner

Data warehouse testing verifies the accuracy and completeness of the data stored in the data warehouse that tests the various components of the data warehouse, ETL processes, the data models, and the data itself. Data warehouse testing is critical as inaccurate or incomplete data can lead to incorrect decisions.

Some common types of data warehouse testing include:

1. Data completeness testing: This involves verifying that all the required data is available in the data warehouse.
2. Data accuracy testing: This involves verifying that the data is accurate and consistent with the source data.
3. Data transformation testing: This involves testing the ETL processes. It ensures that data is correctly transformed and loaded into the data warehouse.
4. Performance testing: This involves testing the performance of the data under various loads and conditions.
5. Security testing: This involves testing the existing security measures in place that protect the data.

Data warehouse testing ensures that the data warehouse functions as intended and provides accurate and reliable information to support business decisions.

Big Data Testing

Big Data refers to large and complex data sets. It is beyond the ability of traditional data processing tools and techniques. The data sets may be structured, semi-structured, or unstructured. Data engineers generate big data from various sources such as social media, sensor networks, online transactions, scientific research, etc.

According to Forbes, experts believe 463 exabytes of data will be created daily worldwide by 2025.

Big data testing verifies the quality and accuracy of data being processed and analyzed in data systems. It involves testing the data at various stages of the big data processing lifecycle. The stages involved in the big data lifecycle are data ingestion, storage, processing, and output. The primary goal of big data testing is to ensure that the data is consistent, accurate, and valid.

Key challenges associated with big data testing include the volume, velocity, and variety of the data. Additionally, big data testing requires specialized testing tools and techniques. Testers need to handle the complexities of big data systems such as distributed computing, parallel processing, and cloud computing.

Effective big data testing requires a thorough understanding of the underlying big data technologies. Some big data technologies are Hadoop, Spark, Hive, and Pig. Testers need to be aware of data processing algorithms and statistical models and have experience with big data testing tools like Apache JMeter, Apache Kafka, and Apache Storm. Big data testing is critical to ensure the quality and reliability of data systems.

Data Migration Testing

Data Migration refers to moving data from one system or location to another. Data migration can be of the following ways:

1. Movement of data from the old legacy system to the new system
2. Consolidating data from multiple systems into a single system
3. Moving data from one physical location to another

According to Gartner, 83% of data migrations fail or exceed their budgets and schedules.

Data migration can be complex and time-consuming, especially when dealing with large volumes of data. The process involves data profiling, cleansing, mapping, transformation, loading, and verification.

Data Migration Testing ensures an accurate migration of data and verifies that the transferred data is correct, complete, and consistent with the original data.

The Data Migration Testing process can involve several types of testing, including:

1. Functional Testing ensures that the migrated data performs the same functions as the original data.
2. Data Integrity Testing ensures that the data transfer is accurate and there are no data integrity issues.
3. Performance Testing ensures that the data migration process does not adversely affect system performance.
4. Regression Testing ensures that the data migration process does not cause any existing functionality to break.

Data Migration Testing is an essential step in the data migration process that ensures the migrated data is accurate, complete, and consistent with the original data.

However, the following challenges are faced while performing the above three data testing practices:

1. The volume of data
   Most firms test far less than 1% of their data by sampling their data. Thus, at least 99% of the data remains untested. Since bad data exists in all data stores, firms should test & confirm as much data as possible. And as quickly as possible to guarantee that this critical information is accurate. It is time for organizations to take a hard look at the data that their management is relying on. This helps in improving the decision-making process.
2. A large number of data sources
   According to IDC, “79% of organizations have more than 100 data sources.” Data is present in different systems throughout an organization. Having a test strategy and solution in place helps achieve data accuracy.
3. Finding Testing Expertise for Data Projects
   Data testers need a unique skill set that differs from testing software applications. Data testers need to understand data architectures, data sources, and SQL. Finding the proper resources to manage and conduct an organization’s data testing is critical to achieve success.
4. How to integrate data testing as part of an organization’s data pipelines
   Organizations are eager to test their data quickly and in real-time. Achieving this requires a certain maturity level of their data testing effort. Automate the tests and initiate the process as part of data pipelines. Analyze the results in real-time and distribute the feedback of the tests to act upon. Many organizations have goals to reach this level of maturity. These organizations are looking for solutions to help them meet these goals.

Cigniti – QuerySurge Partnership

Today, organizations require a full range of solutions to handle their data. Cigniti – QuerySurge partnership addresses the clients’ needs in shifting left and testing their data. Cigniti, a leader in quality assurance, is a powerhouse in offering Data Engineering and Insights services. In partnership with QuerySurge, Cigniti provides an end-to-end ecosystem of data solutions – from capturing the correct data to generating meaningful insights and shifting left towards world-class testing.

Our Data Engineering team helps to extract, transform, and load the data to the data warehouse. We also help manage, store, and analyze extremely large and complex data sets that are beyond the ability of traditional data processing tools and techniques. We migrate the data effectively from old legacy systems to new systems, consolidate data from multiple systems into a single system, and move data from one physical location to another.

QuerySurge adds tremendous value to data warehouse testing, big data testing, and data migration testing. The joint solutions will help you to

1. Continuously detect data issues in the delivery pipeline
2. Dramatically increase data validation coverage
3. Leverage analytics to optimize your critical data
4. Improve your data quality at a speed
5. Provide a huge ROI
6. A good test strategy in place by experts
7. Automate data tests with large volumes
8. Integrations with ETL and pipeline tools to deliver CI/CD for data projects

Need help? Read more about Cigniti’s Data Engineering & Insights Services and QuerySurge Data Testing Solutions to learn how we can speed up your data warehouse, big data, and data migration efforts.