AI-driven Big Data testing: The pillar of next generation digital transformationCigniti Technologies
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From the initial paper to digital transition to entering a new phase of advancements in digital technology, we have come a long way on the path to digital transformation. From everything being about mobile presence and smart phone apps to embracing of Internet of Things and AI-driven data analytics, we are moving toward the next-generation digital transformation.
There is no doubt that COVID-19 outbreak has accelerated digital transformation for each organization across every industry around the globe. Despite the fact that these organizations vary on their digital maturity curve, majority of them are prepared to adopt emerging technologies to fuel the acceleration further.
Now, digital transformation is not only about having the digital capabilities. Rather, it is about delivering a seamless, digital experience to the end users. For doing so, simply having a digital presence is not enough. Organizations need to fortify their digital offerings through a robust, secure, and seamless platform that provides tailored experiences to the users based on their individual needs.
In this age where digital user experience takes priority, AI-powered automation that facilitates predictive analytics for better decision-making & performance, and next-generation business processes that automatically adapt and learn user’s requirements for optimizing their experience become key to success. These intelligent processes, however, rely on the accuracy of data analytics results and efficiency of the AI algorithms. Therefore, AI-led big data & analytics testing become the pillars that support an organization in this next stage of their digitalization journey.
The need for Big Data applications testing
Artificial Intelligence needs data to learn and evolve, and data requires AI-driven analytics to offer real value. It is safe to say that AI and big data are co-dependent and, at the same time, imperative for an enterprise approaching the next-gen digital transformation.
Over 2.5 quintillion bytes of data is created every day and it is estimated that by the end of this year, 1.7MB of data will be created every second per person. Atul Butte, Chief Data Scientist and researcher in biomedical informatics, says, “Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.”
In order to change the world with powerful decisions, it is essential to have a consistent availability of clean and reliable data, which can be thoroughly analyzed to reveal critical insights. As data without the insights is more or less invaluable to organizations, enterprises need to establish processes for instant data collection, deployment, and analysis. By performing end-to-end testing of the data sources and integrators, enterprises can ensure cleanliness and reliability of the data generated and integrated.
The forward-looking organizations are adopting advanced technologies for accelerating their journey to a smarter future. This may involve migration to the cloud, implementing Internet of Things, among other emerging technologies. While these organizations leave their legacy IT infrastructure behind and migrate to new systems to support this journey, they may face challenges in migrating the mission-critical data. There could be disruption or system downtime, data integration issues, and possibilities of loss of data. Here data migration testing can help them verify all the functional and non-functional aspects of the application after migration.
Since, heaps of data is generated every second, there is very small window for the enterprises to utilize the insights from that data before it gets redundant. There should be real-time integration of the data with the enterprise applications such that the big data matches the level of scalability and data processing system. Thus, they need AI-based predictive data analytics testing that can analyze the insights from the minute patterns in large data sets to support effective decision-making. By testing and certifying the big data feeds and applications in live deployment, organizations can stay assured of the correctness of their decisions. This would involve smarter data sampling, cataloging techniques, & high-end big data performance testing.
Why take an AI-led testing approach for big data
Artificial Intelligence brings in the processing power, in terms of speed and scale, that human brain cannot. As more and more organizations are performing daily deployments with their Agile & DevOps-driven software development approach, intelligent automation of testing becomes the key to releasing quality products. While performing big data & analytics testing, it is essential to automate the requirement traceability and versioning for accelerating the testing lifecycle, reducing cost overheads in test management, while providing high quality governance.
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Taking an AI-driven test case management strategy for big data testing, organizations can achieve faster deployments, govern over test data and test suites, gain better traceability with backward & forward integration, and obtain early feedback with unattended execution
How can AI-driven big data analytics testing support your next-gen digital transformation
By leveraging Artificial Intelligence algorithms for test suite optimization and analytics, organizations can discover smart asset creation using data repositories, learn to identify relationships between test assets and software requirements, predict occurrence of an incident or impact led by analytics and insights, and respond to that incident proactively.
At Cigniti, we follow a comprehensive big data automation testing approach that facilitates live integration of information, facilitates seamless integration & validation of data with systems, identifies requirement ambiguities for review, and ensure accurate ETL testing.
We leverage our experience of having tested large scale data warehousing and business intelligence applications to offer a host of Big Data testing services and solutions such as BI application Usability Testing. Our open–source big data testing tools help evaluate the reporting app for end-user’s adaptability and continuously review the observations with user & dev group, as a part of our Agile and DevOps testing.
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