Software Testing in the World of Big Data, AI, Smart Machines, IoT, & RoboticsCigniti Technologies
Emerging Technologies are no more 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 the desired consumer experience. Every sector, in teall possible ways, is empowering themselves with these technologies, by implementing digital transformation initiatives and progressing in the competing marketplace. So, we can 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 for conducting 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 to 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 to perform tasks that would traditionally need human intellect. A computer can be fed with huge amount of data sets, which then adds 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 the business objectives. For instance, AI bots can now successfully communicate by giving human inputs and do a whole range of activities. It can prove to be absolutely beneficial for various tedious and recurring tasks. However, its performance will totally depend on the input of right data and its effective processing. Software Testing helps to confirm 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 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 the data derived from diverse data sets bring business value and profitability in the long run. For instance, marketing teams will need quick analysis of consumer data to substantiate their claims and understand the consumer much better.
Robotics and the changing dynamics
Robotic process automation (or RPA) is implemented to help employees of an organization to configure computer software or a robot for processing a transaction, working on the data, prompting responses, or computing other systems. This is one of the many examples where robotics is being implemented for easing 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 to validate and establish a pattern for expecting a response or behaviour.
Dependability on IoT
Today, consumer brands and industries functioning across various domains are leveraging the capabilities of IoT to innovate and offer new experiences. The overall functioning of IoT totally depends on how effectively the data is exchanged and applied in real environments. Nevertheless, this functioning needs to be get authenticated, as it might affect the overall performance and impact human life in some way.
IoT systems need to be checked for security, performance, functionality, and availability across the consumer lifecycle. QA and Testing has been enabling enterprises to ensure this under varying pressures and conditions. This helps overall in increasing the dependability of businesses on IoT devices for delivering desired consumer experience.
Digital Transformation is impossible without adoption of new and emerging technologies. The organizations that do not leverage these technologies and fail to go with 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.
As part of Cigniti’s Advisory Services, we provide an implementation roadmap to help our clients improve time-to-market and cost of quality while keeping their organizational and business goals in mind. We use our proprietary Assessment Frameworks, based on industry best practices and standards, to assess clients’ testing maturity focusing on People, Processes, Tools, and Infrastructure. Collaborate with our experts to understand and improve all QA focus areas – people, tools, and infrastructure across the delivery lifecycle.