Introduction

This blog will explore the critical distinctions between application testing vs. data testing, common mistakes with data testing, and reveal the consequences of neglecting it.

Testing is a critical step for any software development project. Web applications, or mobile apps are tested to ensure the functionality and the UI works as expected. But what about data-centric projects such as data warehouses, ETL, data migration, and big data lakes? Such systems involve massive amounts of data, have long running processes, and unlike applications, they lack screens. In such projects how does data testing work?

Data Testing vs Application Testing

At a high-level data testing and application testing both share a common goal of ensuring functionality of a system, however on a closer look, it reveals that they have very distinct focuses and methodologies. Here is a quick list of differences for your reference that will bridge the knowledge gap.

Top Data Testing Mistakes, organization do

At the heart of the issue lies a fundamental misunderstanding – the perception that application testing and data testing can be treated interchangeably.

1. Ignore Data Testing: Organizations often neglect Data testing. If you ignore it doesn’t go away. It will manifest in some other format. The common reason for ignorance of data testing. A QA professional with an application background does not understand data testing, while the data engineers are not classically trained in SDLC. They believe the testing concepts somehow do not apply to their data pipelines.
2. Lack of Dedicated Data Testing Team: How many organizations have dedicated data testing COE? The lack of a dedicated team will result in knowledge gaps as well as create continuity gaps in the organizational memory. Many times, the effort is simply outsourced to a consulting company. But that just hides the problem. Since the outsourcing company might not have the resources or tools to test and certify the data processes.
3. Application Testers for Data Testing: Just because someone is skilled in application testing does not mean that the person will have the know-how of data testing. This could be an extremely expensive mistake for both time and money.
4. Manual Data Testing: Automation has become the mantra for efficiency in software testing, but this mantra is often focused more on application testing. Automated UI tests and functional checks take center stage, leaving data testing to be more of a manual process. The absence of automation in data testing not only hampers efficiency but also introduces the risk of human error, especially when dealing with complicated and extensive datasets.
5. Data Sampling: In the absence of automation, organizations resort to manual data testing, a daunting task when faced with millions of records. Manual testing becomes a mammoth task to undertake, prone to errors, inconsistencies, and a significant drain on resources. The sheer volume of data makes it humanly impossible to ensure comprehensive testing, forcing the testing team to resort to testing sample data rather than the entire dataset, leaving critical aspects unchecked.
6. Lame efforts to use application testing tools for data testing: Using application testing tools for data testing is not the right fit. While tools like Selenium and JMeter excel in UI and functionality checks, testing data pipelines demands specialized tools. The mismatch not only results in inefficiencies but also fails to address the unique challenges posed by data-centric projects.
7. Low Budget for Data Testing: One glaring consequence of prioritizing application testing is the improper allocation of budgets. Organizations, in pursuit of flawless user experiences, often channel a significant portion of resources towards application testing tools and frameworks. Meanwhile, data testing, which operates in the complex terrains of data migration testing, ETL testing, data warehouse testing, database migration testing and BI report testing is left with a fraction of the QA budget.
8. Build in-house Scripts or Frameworks: Some organizations realize the distinct nature of data testing and attempt to build in-house frameworks. However, this approach often has more disadvantages than advantages. In-house frameworks, while tailored to specific needs, may lack the scalability required for projects dealing with millions of records and complex data structures. The inefficiencies in this approach become apparent as data volumes grow, leading to bottlenecks in testing processes.

Consequences of Ignoring Data Testing

1. Cost and Time overruns
2. Complete failure of projects
3. Data Quality issues in Production
4. Compliance and regulatory risks
5. Reputation Risks

Conclusion

To summarize the difference, while Application Testing and Data Testing share the overarching goal of ensuring the robustness of a system, they operate in distinct realms. Application Testing spans the broader landscape of application functionality, whereas Data Testing homes in on the intricate dance of data within the system. Understanding and appreciating these differences is crucial for organizations aiming to fortify their digital infrastructure comprehensively. For those delving into the intricacies of both, the journey promises a holistic assurance of both application resilience and data integrity.

Recognizing the critical distinctions between application testing and data testing is the first step towards comprehensive Quality Assurance. Organizations must recalibrate their approach, acknowledging the unique requirements of data testing and allocating resources, budgets, and automation efforts accordingly.

Embracing specialized tools like iceDQ which is a low code-no code solution for testing your data-centric projects is key to building software that stands the test of both user experiences and data integrity.

The divide between application testing and data testing is not just a technical nuance but a critical factor that defines the reliability of data and application. Bridging this gap requires a strategic shift in organizational mindset, ensuring that both receive the attention and resources they deserve. The journey towards comprehensive QA begins with acknowledging the uniqueness of each testing domain and crafting a unified strategy that encompasses both application and data testing.