Emerging Trends for 2017
Connectedness, immersion, and machine learning are among the traits that have formed the perfect storm for users and devs alike. See how to position yourself to benefit.
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In this short post I present my thoughts on trends that are likely to be important considerations for enterprises in 2017. A few years ago everyone was talking about ‘SMAC’, which stood for Social, Mobile, Analytics, and Cloud. So not to be outdone, I’ve punnily organized the trends as VICTIM:
Voice Interaction: The prevalence of voice interaction in consumer technology, now spreading beyond smartphones to devices such as the Amazon Echo, has overtaken the mainstream enterprises who thus far have largely failed to see beyond IVR for call center optimization. Advances in NLP and processing power may indicate that more tedious or erroneous data entry tasks are ripe for revolutionizing. Voice interactivity is also good for accessibility.
Connectedness: Not only for mobile devices (though mobile apps will still require an offline mode) and Internet-connected ‘things’, but also data. Native graph databases, such as Neo4j, have a data model that is designed for highly related data and make it efficient to traverse adjacent nodes.
Telemetry: Adaptive businesses will start to collect more data points about their processes to enable greater insight and optimization. Event-driven architectures and technology such as Apache Kafka (or AWS Kinesis) can help harness the streams of business events for persisting and analyzing.
Immersive: Augmented Reality (AR), where computer generated imagery is superimposed onto a video stream, gained greater consumer awareness in 2016 through Pokémon Go. However, the barrier to entry to move from AR to Virtual Reality (VR) has been significantly lowered through mobile-powered VR. Whilst Google Cardboard is appealing from a hobbyist perspective, the Samsung Gear VR headset is aimed at high-end mass market devices and was heavily advertised in the run up to Christmas 2016.
In 2017, I expect immersive experiences to see more consumer adoption and from there start to permeate B2C. Obviously, anything that can typically be represented in 3D is much easier to envisage how it can be translated into VR. Imagine consumer applications that can help prospective customers visualize a new bathroom or kitchen, a new extension to their house or a garden layout. From there it isn’t a great stretch to industrial applications. However, I don’t expect board slide decks containing charts to be replaced by VR data visualisations in the next 12 months.
Machine learning: This field is especially important in spotting patterns from all the telemetry data or determining what your user is likely to want to do next. Apache Spark is now the basis for a number of machine learning frameworks including Apache Mahout and of course Spark’s own MLlib.
For developers, if you manage to get all of those onto your CV in 2017, then you’re either a fashion VICTIM or really pushing the boundaries of making applications more engaging and usable! Exciting times await…
Published at DZone with permission of Robin Bramley, DZone MVB. See the original article here.
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