Consumers are more digitally savvy than ever before. They – all of us, really – expect more from technology, requiring it to become more intuitive and deliver greater benefits. A very visible, every day example is the rise of voice-based interfaces like Siri, Alexa and Cortana. Similarly, test-led chat bots are now increasingly used to handle consumer-facing customer service inquiries and sales online. Using artificial intelligence and machine learning techniques, organizations of all types are working hard to improve their customer experience, optimize the product offering and, of course, to sell more to customers. One way of doing so is to second-guess what additional products a customer might want or need based on information previously given.
There are many more ways in which predictive analytics is being put to work. Invisible to the consumer eye, our increasingly connected world is reliant on accurate, actionable, automated analytics to power it. The IoT, which is soon-to-be running on 5G networks, is a good example of complex systems with collectively huge processing requirements. Traditional automation techniques would hold it back, creating latency, delays and poor user experience. Instead, intelligence is being programmed into the network capable of delivering real-time insights, responding to evolving traffic levels and optimizing the system as a whole.
As organizations across all industries face opportunities and threats in the new digital era, so they increasingly look to leverage their full power of the data they possess. Data is a treasure trove for enterprises. (For more on the value and importance of ‘good’ data, check out the blog, We Need to Talk About Data).
Big data mining has grown into much more than a sales tool. Advanced, predictive analytics are now used to hone processes, improve product quality and deliver those products more rapidly.
Companies who are serious about digital transformation are turning their attention to predictive analytics techniques as a means of assessing the whole product lifecycle. In test automation terms there are several benefits from a predictive QA approach. Getting the most out of the defect management and test automation data enables you to create better models, optimize testing processes and predict defects like never before.
If you want to learn more about how predictive analytics can help overcome the most common test failures and predict early defects better, view our webinar replay.
As part of our commitment to help organizations digitally transform through Quality Engineering (QE), Infostretch has been leveraging machine learning and advanced analytics techniques targeting both end-product and the design lifecycle as a whole. Predictive QA is a topic you’ll be hearing more on from Infostretch, so stay tuned for further updates in the very near future!
In the meantime, to discover how predictive QA can enhance your digital initiative, contact us to get a free QE assessment.