A mobile first approach
It’s safe to say that mobile devices are not going away anytime soon, and the consumption of applications is exponentially growing at breakneck speeds. The applications available on mobile devices today are by far incredible creations that enrich our lives, and in some cases they can be TOO interactive...dam you PokemonGo!
The PokemonGo! craze reached 100 million downloads and a whopping $10 million in daily revenue worldwide
What made mobile applications the success that they are today, was the introduction of the App stores. This provided the masses with a plethora of selection from business apps, games, education, health care to tracking stars across the sky. Some experts believe we haven’t even remotely scratched the surfaceon the revenue potential of Mobile Applications. Something to keep in mind on the sheer size of the market,Statista research estimates downloads to reach 268.69 billion by 2017!
Today, mobile applications have become a value added core to many business organizations. These applications have been evolving at the speed of human-data-consumption while focusing on delivering greater contextual experiences to its users. The challenge across the Mobile Application development world comes to a crossroad with who is going to dominate and control the back-end where ALL things connect ranging from; mobiles devices, IoT, connected vehicles and homes. This conundrum raises many queries when organizations are looking to either re-vamp the mobile development strategy or inclusively develop an entire new client/user product.
Having a clear approach in defining and selecting a product development strategy does not have to be tedious or cumbersome. Whatever product you intend to build, web development should not be the first until you have a mobile strategy first. With that strategy one has to look at the market and see user areas of interests and related technologies that surrounds them.
In the next few years mobile applications will be impacted by the following trends:
- Chomping on Cross-Platform + Cloud Technologies. Developers taking advantage of cloud technologies get far more tools to synchronize, develop, test and deploy apps and this not only saves time but also money.
- Bringing home the BEACON! The adage to adopting Beacon technologies is agility and flexibility in harnessing greater market opportunities. The connection between venues, environment and things creates spaces in which companies can greater gauge and explore human-connected activity.
- Big Data Servings with a side of juicy Analytics. Big data is turning out to have greater impact on UX while delivering substantial insight around contextual experiences. The analysis gleaned from user behavior closes the gap between user need and product. Those who can convert raw data into accurate informational user stories will have an edge. This vantage point will benefit those who can translate those stories into meaningful experiences back its market.
- Baking across multiple platforms. It’s a prevalent discussion among many product owners and DevOps leaders how to best distribute and operate critical mission applications. Some confide in the magic that APIs provide, while others blend this strategy with cross-platform development tools. Being able to effectively distribute applications to most popular systems enable the organization to virtually globalize in order to reach critical mass quickly. Internally it also provides a way for users to adopt tools quickly and seamlessly synthesize it into its culture.
- Automated Cooking with the Machine Learning. The next phase of software development means that software itself learns from previous experiences by collecting and analyzing all types of user data. It will draw new conclusions in real time during human interactions and very little programming will be needed to complete tasks. Eventually, Machine learning will expand into automated software development and the need of Software Developers will shift into new classes of Data driven Architects and Designers. These types will be focused on how much data is provided to the algorithm: the more data fed, the more accurate predictions and results will be.