The Role of AI in Predictive Analytics for Big Data Testing

 

predictive-analytics-for-big-data-testing

The success of digital transformation for any business or entity is based on the seamless digital experience it offers. It is not only about capabilities, but how the elements of digital transformation, including the platforms, function as per the needs of individual users. In a day and age where businesses burn the proverbial midnight oil to optimize user experiences using intelligent processes, the role of big data becomes crucial. In fact, with digitization being embraced by organizations across domains, platforms, and geographies, data management has become critical to remaining competitive. 

If diverse, remotely located and distributed devices and processes generate a humongous quantity of data, it needs to be analyzed to make informed business decisions. For example, IoT devices generate a continuous stream of data in real-time, which should be analyzed in nanoseconds to make an informed decision. A driverless car can sum up everything. Here, data generated by sensors needs to be analyzed in split seconds to determine whether the car should apply brakes or accelerate.

This is where AI-led big data automation testing facilitates decision-making for next-gen businesses. Here, the focus of businesses is to conduct predictive analytics and learn about user behavior and requirements, and optimize the latter’s experience. However, any kind of predictive analytics by AI-led tools would depend on the accuracy of data analytics. Hence, big data and analytics testing is the key to taking intelligent decisions about facilitating user experience and driving the digital transformational journey of businesses.

Characteristics of big data

Unlike traditional forms of data, big data exhibits a few characteristics:

·         Originate from a variety of sources – business information systems, sensors, social media, weblogs, websites, emails, etc

·         No fixed source or structure – Facebook records millions of posts, photo uploads, and likes every minute

·         Analysis and managing big data needs a specialized framework such as Hadoop

Why big data analytics?

The humongous growth of data, specifically big data, can be attributed to the exponential growth of various digital devices and processes. Big data (in terabytes and petabytes) is mostly generated in real-time from a multitude of sources and can be classified into structured, unstructured, and semi-structured forms. These data sets are termed big as they are too large for traditional databases and processing systems to handle. Also, their large and complex characteristics require advanced management, storage, visualization, and analysis technologies.

For data centers, the task of managing the variety, volume, and velocity of data streams is challenging. But thanks to improved capabilities of storage, computing, and analysis of such data sets, businesses have begun to leverage them to derive crucial business intelligence. Such intelligence can let them make informed decisions about business strategies and improve ROI. 

If such large datasets are tapped into using predictive analytics, businesses can make intelligent decisions on the following aspects:

·         Customer behavior or preferences while using a product, service, application, website, or device

·         Robustness of processes generating data

·         How to gain a competitive advantage

Why predictive analytics?

If businesses are able to determine what makes customers root for a particular product or service, they can suitably tweak the product or service and drive sales. This is where predictive analytics using AI can work wonders in discovering meaningful patterns of data. As a progression from data mining and business intelligence, predictive analytics can derive meaningful insights from data sets in real-time, and help businesses strategize better. And to derive meaningful outcomes from predictive analytics of big data, big data automation testing should be utilized. It is only through big data test automation that the accuracy, completeness, and integrity of data being tested in real-time can be assured.

Why use AI for predictive analytics of big data?

With businesses taking an intelligent big data testing approach, AI has become the essential tool to accelerate the testing lifecycle, provide high-quality governance, and reduce cost overheads. Thanks to the quantum leap AI can achieve in terms of processing power, scale, and speed, it is a convenient tool to perform big data analytics in real-time.

Conclusion

To enable big data and analytics testing, AI algorithms can be of help in ensuring test suite optimization and creating smart assets. In fact, AI can derive smart business insights to predict the occurrence of an event or its likely impact on the business. This way, AI-based predictive analytics for big data testing can respond proactively to any business challenge or requirement. It can help businesses benefit from emerging business opportunities or safeguard them from adverse situations.

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James Daniel is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results and always happy to create valuable content & share thoughts.

Article Source: datafloq.com

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