Ensuring data integrity is crucial for accurate decision-making and regulatory compliance. Poor data quality costs businesses an average of $15 million annually. This highlights the need for robust analytics testing. This article explores the importance of analytics testing. It discusses key use cases and common challenges. Strategies to improve analytics testing processes will be provided. These will help maintain data integrity without expanding the in-house QA team.

Product analytics helps you understand how users interact with your product, offering insights that drive growth and improve user experience. In this guide, you’ll learn about essential metrics, strategies, and tools for effective product analytics. By leveraging these insights, businesses can make informed decisions, optimize product features, and enhance overall user satisfaction.

TDD refers to Test Driven Development and BDD refers to Behavior Driven Development; are fairly similar development approaches that both emphasize testing and collaboration, yet have major differences. TDD is more developer-centric, revolving around code correctness, with programming language-specific frameworks. On the other hand, BDD is more user-centric and revolves more around system behavior. In this article, we will explore in-depth what is TDD and BDD. So, let’s dive in!

End-to-end (E2E) testing and regression testing are critical aspects of software testing that play very different roles. E2E Testing evaluates the entire application, simulating real user experiences. In contrast, regression testing checks if recent updates or changes have inadvertently disrupted existing functionality. Choosing the right testing strategies is as important as testing frequently.

Engineering and product leaders often struggle with balancing speed, quality, and cost in software testing, especially with the rapid evolution of technology and increasing demands for efficiency from executives. To address these challenges, many are turning to Testing-as-a-Service (TaaS). This outsourcing model uses expert partners to scale testing capabilities dynamically without requiring extensive in-house resources.

Not long ago, the thought of Artificial intelligence (AI) products becoming mainstream seemed far off. Fast forward to the widespread adoption of ChatGPT, Gemini, and other large-scale apps, and it’s easy to see that the world has changed drastically. AI isn’t just the future anymore – it’s the present, and every business is hopping on the bandwagon. From chatbots streamlining customer service to algorithms predicting market trends, AI is reshaping how businesses operate.

Earlier this year, Yellowfin celebrated its 20th anniversary. While we have been around for a while, some things remained constant - our focus on customer and innovation. Yellowfin was borne out of the frustrations felt by our founders, and millions of other information workers, in accessing the data they need to do their jobs effectively, a frustration just as relevant today as it was 20 years ago.

Building and consuming Application Programming Interfaces (APIs) has become an essential skill for developers. APIs are the glue that enables seamless integration and interaction between different software systems, including those that leverage AI. As the demand for artificial intelligence (AI) grows, the monetization of AI APIs has emerged as a critical concern for businesses looking to capitalize on their technological investments. Almost all AI functionality that companies build is exposed as an API.

Choosing the right software development vendor is essential to ensure your project succeeds. A suitable partner can deliver high-quality solutions, manage projects efficiently, and align with your specific requirements. Moreover, they will contribute significantly to your digital transformation efforts by implementing the latest technologies and best practices. So, where should you look for the best software development vendors?

Every application, web or non-web-based, has to deal with data. Even though there are static websites, where data is somehow embedded, most sites and applications rely on dynamic data stored somewhere and somehow. What testing can you do around data handling? Can you use exploratory testing to uncover problems related to data? What type of problems? In this article, you’ll get some guidelines for your exploratory testing sessions around data.