- Development •
- Mobile •
Over the past year we have seen the rapid emergence of new technologies, some of which, such as augmented reality, became mainstream at lightening pace. The pace of adoption of new technology is the key theme that runs through this document. As we look toward 2017, we can expect to see enterprises deploying new tech, ranging from machine learning to the internet of things, faster than ever before.
So within this changing business and technology environment, Belatrix brought together its experts to examine what we can expect to see in the world of software development in 2017. For this report there were many candidates, from artificial intelligence, to developments in security and privacy, to integration technologies that help organizations make sense of ever greater amounts of data and information. However we have distilled our thoughts into 4 predictions:
Given the importance of mobile to organizations, it may seem strange that mobile testing is still viewed as a relatively new skill. However, this is the case according to data from the World Quality Report 2016-17, which found that while many organizations are making use of mobile solutions, “mobile testing is still viewed as a relatively new skill in the development lifecycle”. The research found that the number one challenge organizations face with mobile testing is not having the right testing process or method, followed by a lack of mobile testing experts and not having an in-house testing environment.
Other challenges include device and OS fragmentation. This fragmentation, while positive for consumers as it gives them great choice over what handset or OS version they would like to use, creates challenges for developers. For example as of November 2016 Android Nougat is installed on just 0.3% of devices, Marshmallow on 24%, and even version 4 Jelly Bean is still installed on 13% of devices.
Augmented reality, virtual reality, machine learning, and the internet of things (IoT) all came to the fore during 2016. For instance, Pokemón Go brought scale (with over 20 million current daily users in the US alone) and real interest in augmented reality. Reflecting this interest, the analyst company Forrester Research believes that augmented reality is one of the top 5 technologies that will start to change the world within the next 3-5 years.
The challenges being brought about by digital disruption are causing difficulties among enterprises in almost every industry, but there is also tremendous opportunity. Therefore a proactive strategy is to look to what leading organizations are doing to prepare for the upcoming changes.
In November this year Belatrix conducted a survey to better understand how machine learning and artificial intelligence is being used by organizations, as well as some of the challenges they face. You can download the infographic here to see the results in more detail. What was most telling, was that the biggest challenge organizations face is finding people with the right skill sets. This reflects data from the research company Forrester Research which found that in 2016 among business and technology professionals 58% are already researching AI, but just 12% are using AI systems.
Organizations such as Google have been at the forefront of implementing machine learning in the real-world. Already, Google uses machine learning in areas as diverse as improved image analysis, translation, and better scheduling of business appointments. As Andrew Ng, the head of Baidu’s AI team has pointed out, “If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.”
To give some examples of some of the work we have been doing at Belatrix, we have been using neural networks to predict the risk of personnel attrition based on information that already exists in HR, project management and training systems. We have also been helping clients make sense of thousands of IoT devices that are collecting data every few seconds, data which could be used to reduce maintenance costs, improve logistics or react more effectively to outside environmental changes like weather and others.
Meanwhile we believe that some of the most interesting parts of machine learning will involve those parts which we are not yet considering. Particularly we are excited about its potential in “weird” places, that many organizations are not yet considering, but we believe will come to have a transformational impact. For example, what if software could learn to test itself? Or at least learn what the biggest impact of bugs are, or the most common.
Agile development has already found widespread adoption by organizations. Continuous delivery has so far lagged the adoption of Agile but in our experience already the majority of organizations are starting on continuous delivery journeys. Continuous delivery is already the approach driving successful tech teams at organizations ranging from Facebook, Etsy, to Ebay, but more broadly, the level of maturity in organizations varies widely. The ability to make smaller, incremental changes means businesses can improve their agility and responsiveness to customer demands.