The world is hurtling towards a cognitive information and communication future, and the lines between physical, digital and virtual assets are blurring. There is a lot of buzz about how to connect the dots in our digital world to create successful business models. While change is evitable, carefully crafted adaptations are required to survive in the hyper-competitive digital era.
Testing ecosystems have gone through a rapid evolution over the last decade as a result of introduction of new technology and process, virtualization and the focus on digital transformation. Testing has also advanced from being a product validation during the last stage of product lifecycle to a more meaningful continuous testing process across all phases. Test solutions, frameworks and approaches will continue to evolve, respond and align with market demands.
In the last couple of years, we have heard a lot about chatbots. They are being adopted rapidly by businesses in many industries including banking, entertainment, finance, healthcare and media, to name a few. A study by Business Insider shows 80% of the businesses want chatbots by 2020.
The network is the bedrock of nearly every significant innovation that we see around us. For example, the high-speed 4G network is making self-driving cars a reality, one of the biggest innovations of this era. A Deloitte report estimates that self-driving cars, enabled by wireless connectivity, will reduce emissions by 40% to 90%, travel times by nearly 40%, and delays by 20%. A 2013 study estimated that if 90% of vehicles were autonomous, it would save 21,700 lives and $447 billion per year.
DevOps is a combination of practices, tools and philosophies that enables companies to work more efficiently and deliver higher quality applications, products and services to their customers much faster than traditional processes. Faster-to-market means improved customer satisfaction, which gives DevOps organizations an edge over the competition.
Can computers think and make decisions like humans? Can they improve the productivity and efficiency of our day-to-day work? The answer is “yes”, as Machine Learning (ML) drives innovations to make this a reality.