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Data Warehouse Testing

Data Warehouse Testing

Erroneous data affects your business more than you can imagine. Companies lose about 15% to 25% of their overall revenue to bad-quality data every year. Further, a study by LeadJen found that sales and marketing departments lose approximately 550 hours and as much as $32,000 per sales representative from using bad data.

Get DataQ's data warehouse testing solution for accurate and reliable data that saves you from the heavy consequences of poor quality data.

Data warehouse testing is a method in which the data inside a data warehouse is tested to ensure its reliability, accuracy, and consistency with the company’s data framework. Valid test cases are built and executed to identify data quality issues. With the increasing emphasis on data analytics to make significant business decisions, the need for data to be trustworthy cannot be stressed enough.

The process of data testing begins even before the data reaches the warehouse. Tests are inbuilt in the data pipeline itself, where the data undergoes extraction, transaction, and load (ETL) operations before they are deposited in the warehouse. Testing data at intermediate stages makes it possible to isolate and resolve inaccuracy early on.

Essentially, data warehouse testing combines data warehouse ETL testing and business intelligence testing (BI).

Challenges in Data Warehouse Testing

Challenges in Data Warehouse Testing

Though essential, the process of data warehouse testing is fraught with many challenges, as listed below:

Data Sampling

A data warehouse is a repository of a huge amount of data. While sampling is one way to deal with the high data volume, it also poses the risk that the items selected in the sample do not represent the entire data population. Some scenarios are bound to be left out while sampling.

Testing across Multiple Environments

Data in the warehouse needs to be tested in multiple environments such as DEV, QA, and UAT. The process can become very time-consuming and duplication of work is inevitable.

Comparing Data across Heterogeneous Data Sources

In the absence of proper data warehouse testing tools, comparing data from various disparate sources can be quite difficult. Manual comparison of heterogeneous data always leaves room for error.

On-Time Identification

Identifying and resolving issues at the source is extremely challenging, especially if there is a lack of automation. This leads to further problems down the line.

Benefits of Data Warehouse Testing

Benefits of Data Warehouse Testing

Though the data warehouse testing process is challenging, it carries enormous value for your business. A slight compromise on data integrity can lead to catastrophic results affecting the entire organization.

The benefits of warehouse data testing are many:

  • High-quality data is available for analytical models that assist in decision-making.
  • Defects are identified earlier when they are faster and easier to resolve.
  • Financial loss due to unreliable data is reduced.
  • Compliance with regulatory requirements of testing can save huge penalties.
  • Poor data can put the reputation of the entire organization at stake. This can be prevented through regular data testing.
  • Inaccurate data renders the entire data warehousing system ineffective. It only makes sense to invest in data testing to justify the amount spent in setting up the warehouse.

Data Warehouse Testing Process

Data Warehouse Testing Process

With such benefits to reap, you can hardly ignore the data warehousing testing process. This process consists of four fundamental steps:

Identify Various Entry Points

The data warehouse testing process accommodates the entire pipeline, from the data entry point to its final destination. The errors found at entry points are easier to resolve since the root cause can be easily identified here. Hence, it’s essential to find out the various data entry points so that testing can be done at each of those stages.

Some of the examples of entry points include the source of data, the various stages of ETL, and the BI engine, which runs over the data stored in the database.

Prepare the Required Collaterals

The two essential collaterals required for data testing are data schema representation and mapping documents. Schema testing is done to validate the formats associated with the database. You need to ensure that the formats are compatible with the formats of the user interface.

Mapping is usually done on a spreadsheet where each source database column is mapped with the destination database. You can use high-level SQL queries to foster comparison between the two data. The mapping document can then be used as an input to design testing cases.

Design an Elastic, Automated, and Integrated Testing Framework

With new data flowing into the organization every day, testing data should be a continuous process instead of a one-time activity. An integrated testing framework needs to be designed to accommodate heterogeneous data from diverse sources. The framework should be agile enough to handle high data volumes and work seamlessly.

You can further automate the testing framework to increase the efficiency of your staff.

Adopt a Comprehensive Testing Approach

As already discussed, testing data on a sampling basis poses a risk of coming to inaccurate conclusions. The testing approach should thus be comprehensive, covering the entire data warehousing process. You need to scrutinize data on many levels, including checking for duplicates, completeness, accuracy, and correctness.

Along with data testing, the application components also need to be included in the testing framework for accurate data processing. It’s best to design multiple testing approaches such as unit, integration, functional, and performance testing. Completing all these four tests will instill your staff with confidence in the workings of the application software.

Get Data Warehouse Testing Services Today

Get Data Warehouse Testing Services Today

Database testing is a necessity rather than a luxury for businesses today. Accurate and reliable data can fuel the growth of your business, enabling it to reach new heights. DataQ's data warehouse testing services deploy various tools to quickly identify data issues, vastly improving data accuracy. Request a demo to see how it works.