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Software Testing in the World of Big Data, AI, Smart Machines, IoT, and Robotics

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Software Testing in the World of Big Data, AI, Smart Machines, IoT, and Robotics

The coming year will bring more transformations and disruptions in the tech space. How do businesses gear up for this kind of technology upheaval?

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Emerging technologies are no longer a foresight; they are a reality, even in terms of our routine activities. Businesses are busy leveraging these technologies to enable digital transformation for achieving desired consumer experience. Every sector, in all possible ways, is empowering themselves with these technologies by implementing digital transformation initiatives and progressing in the competing marketplace. We could say that the coming year will bring more transformations and disruptions in the tech space.

How do businesses gear up for this kind of technology upheaval?

In one of its announcements, Gartner mentions,

"In January 2016, the term 'artificial intelligence' was not in the top 100 search terms on gartner.com. By May 2017, the term ranked at No. 7, indicating the popularity of the topic and interest from Gartner clients in understanding how AI can and should be used as part of their digital business strategy. Gartner predicts that by 2020, AI will be a top five investment priority for more than 30 percent of CIOs."

Now, this can be massive, and in many ways, AI will be implemented fo conduct a range of activities, including interaction with the end users/consumers. This holds true for almost every technology that has been gaining popularity and enabling businesses — big data, smart machines, IoT, and robotics. While it is important for enterprises to leverage these technologies, it is also necessary for them to adopt it with full confidence and ensure its relevance for their business. New technologies will work for a business only when they are mapped against its business goals.

Quality assurance and software testing help enterprises adopt technologies with an objective to bring business value. In this context, organizations evaluate how Agile development can help them with their digital transformation efforts, why implementing DevOps is becoming a top priority, and how it can enable them to understand their consumers better and address their requirements.

AI in the Land of Software Testing

AI is definitely gaining momentum and is being implemented across diverse industries. AI helps systems perform tasks that would traditionally need human intellect. A computer can be fed with a huge amount of datasets that then add logic and patterns to come up with relevant inferences. QA and testing are very much required for establishing a valid connection between similar input and output pairs.

Automation testing is needed to ensure that the results derived are relevant and in line with business objectives. For instance, AI bots can successfully communicate by giving human inputs and doing a whole range of activities. It can prove absolutely beneficial for various tedious and recurring tasks. However, its performance will totally depend on the input of the right data and effective processing. Software testing helps confirm the consistent performance of these technologies.

Growing Need for Big Data Testing

Going by Gartner's estimate:

"Global revenue in the business intelligence (BI) and analytics software market is forecast to reach $18.3 billion in 2017, an increase of 7.3 percent from 2016, according to the latest forecast from Gartner, Inc. By the end of 2020, the market is forecast to grow to $22.8 billion."

Diverse Analyst reports even state how enterprises lose millions of dollars due to poor data quality and inadequate optimization of business data.

The core objective of big data testing is to ensure data completeness, enable data transformation, confirm the quality of data, and automate analytical activities. The overall technology movement and effectiveness depends massively on the exchange of data. Whether it is robotics, machine learning, smart devices, or Internet of Things (IoT), big data is at the core of it.

Moreover, big data testing ensures that data derived from diverse datasets bring business value and profitability in the long run. For instance, marketing teams will need quick analyses of consumer data to substantiate their claims and understand consumers much better.

Robotics and the Changing Dynamics

Robotic process automation (RPA) is implemented to help employees configure computer software or robots to process a transaction, work on data, prompt responses, or compute other systems. This is one of the many examples in which robotics is being implemented to ease human efforts and automating mundane tasks.

In an environment such as this, performance and functionality can be ensured only when the expected results are tested rigorously and authenticated under varying conditions. Performance testing, functional testing, security testing, and various other types of testing help enterprises validate and establish a pattern for expecting a response or behavior.

Dependability on IoT

Today, consumer brands and industries functioning across various domains leverage the capabilities of IoT to innovate and offer new experiences. The overall functioning of IoT totally depends on how effectively data is exchanged and applied in real environments. Nevertheless, this functioning needs to be authenticated, as it might affect overall performance and impact human lives in some way.

IoT systems need to be checked for security, performance, functionality, and availability across the consumer lifecycle. QA and testing have been enabling enterprises to ensure this under varying pressures and conditions. This helps increase the businesses' dependability on IoT devices for delivering desired consumer experience.

In Conclusion

Digital transformation is impossible without the adoption of new and emerging technologies. Organizations that don't leverage these technologies and fail to follow the trending waters will end up way behind in the race.

The consumer market is dynamic, and businesses need to experiment and innovate to hit the right chord with the end-user/consumer. This can be done with conviction only when these technologies are well-tested against numerous odds and under various conditions. QA and testing can be an absolute enabler in this context.

TrueSight is an AIOps platform, powered by machine learning and analytics, that elevates IT operations to address multi-cloud complexity and the speed of digital transformation.

Topics:
software testing ,big data ,ai ,smart machines ,iot ,robotics ,machine learning ,digital transformation

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