Soon, all the organisations will not only be digital but would ace at it, then what next? It is time for beyond just analytics, it is time to ride the algorithm wave, are you ready?
By 2025, every industry will be transformed by digital business, as per Gartner, then what is going to be give one organisation edge over the other is not when one adopted digital but how well they donned this inevitable change.
It is the time when CIOs and other IT leaders can inspire their organization and help it become a leader; but how? May be these top 10 technology trends can brush-up the CIO-skills and help them decide ride the post-digital, post-app – algorithmic wave, already!
Digital Mesh - The device mesh brings together traditional desktop-centered computing, mobile computing, the IoT and cloud computing in a common, connected framework of endpoints and supporting services. the implications of the expanding set of endpoint devices, encompassing traditional, mobile and IoT devices. This set shifts the focus from mobile devices to mobile people surrounded by an ever-shifting set of devices.
Ambient user experience - The ambient UX ultimately ushers in the postapp era in which people have access to their own personal cloud of services dynamically provided through intelligent-agent-based interfaces. The major technology shifts affecting the design of the UX, moving from isolated apps on devices to a mesh of devices with an immersive experience through and across devices.
3D-printing materials - A major gating factor in 3D printing is the materials that can be used and the ability to print a single item with multiple materials and these advances in materials will expand the use of 3D printing across a wider range of industries.
Information of everything - Massive amounts of data from traditional systems, cloud sources and the IoT create an overload that must be addressed by more-advanced analytics integrated into the fabric of applications, business processes and routine user activities.
Advanced machine learning - Machine learning enables computers to act without being explicitly programmed. Massive amounts of data, unprecedented advances in machine-learning algorithms and new hardware platforms delivering massively parallel compute power are accelerating machine learning. Rapid evolution of machine learning can be seen as the next step in data science and the foundation for creating smart machines and the algorithmic economy.
Autonomous agents and things – The information of everything and advanced machine-learning algorithms, supported by advanced system architectures, are leading to more intelligent software and hardware-based solutions. These are creating new market segments and enhancing existing ones. IT leaders have a broad range of
opportunities to exploit machine learning. These opportunities offer the potential to deliver autonomous and semiautonomous agents and things, including robots, autonomous vehicles, smart vision systems, virtual customer assistants, smart agents and natural-language processing.
Adaptive security architecture - Adaptive security architectures recognize that traditional access control and perimeter defense are insufficient, and we need a full range of tools. Security must start with application design, extend through robust application testing, and follow through with runtime application self-protection for operational systems. In addition, user and entity behavior analytics using contextual analysis and machine-learning algorithms will deliver real-time monitoring and active protection for internal systems.
Advanced system architecture - The system needs to reach an advanced level to support the needs of smart machines and algorithmic business. Advances in system architecture — especially chip architectures to support parallel processing — have helped ignite the growth of smart machines.
Mesh app and service architecture - Cloud computing principles and adaptive, layered applications that span an ever-changing sea of client endpoints provide the foundation for the digital mesh. Software-defined approaches that emphasize the creation of microservices with rich, layered APIs and delivery of services using OS containers provide greater deployment flexibility to support the dynamic nature of the digital mesh. Application architecture must also deal with the full range of potential endpoints, with an increasingly dynamic and intelligent UI layer assembling service components as needed.
IoT architecture and platforms - Enterprise architects must consider security, privacy, cost, ease of access, agility and performance to determine the best architecture for their IoT initiatives. An IoT platform enables enterprises to monitor and control IoT endpoints and build applications to meet digital business requirements.