The recent rise of artificial intelligence (AI)/machine learning (ML) in the cloud — and related capabilities such as predictive analytics — has started to push DevOps organizations to explore the implementation of a new data analysis model that relies on mathematical algorithms.
If you’re wondering how wide-spread application state can be coordinated throughout such a dynamic landscape, you’d be right to do so. Coordination is needed not just across internal business services, but also across disparate services that may or may not be connected to a common messaging platform up and down the application layer stack.
Today’s businesses are looking to integrate applications and IT services to achieve new business models. Digital transformation requires standardized applications and protocols that allow everything to work together. Speedy time to market drives business value, and in this dynamic digital environment, users won’t wait for an app to catch up, or for everyone to get on the same standard.
By shifting the digital storefront from websites to APIs, these “API-driven” companies are accelerating digital business. As a result of moving their product development to APIs, digital businesses are essentially positioning their business interfaces across multiple clouds and PaaS environments in all industries.
To build internetworked software components and gain the controls required for digital business success, organizations must transform their application development to an API- and interconnection-centric approach. Doing this will allow them to fully realize the benefits that digital services can provide for users.
Business is booming for professional services as enterprises look to outsource more functions, so they can accelerate digital transformation. It’s exciting stuff, but the pace of change in this market is making it a challenge for many established players to keep up with newer, sometimes more agile competitors.
Data from multiple sources and various associated data services/applications all need to come together in real-time. As a result of orchestration, data policies and service levels can be better defined through automated workflows, provisioning, and change management. Critical data management processes can also be automated, including data creation, cleansing, enrichment and propagation across systems.
Trying to predict how many Internet of Things (IoT) devices will go online over the next decade is like trying to predict the growth rate of rabbits in the wild. It suffices to say that 2017 could be the year in which IoT devices exceed the total human population, based on a Gartner forecast of 8.4 billion IoT connected devices, or one device for each of the 7.5 billion people, plus just under a billion more to spare.
Data is increasingly becoming the currency of the global digital economy. And, it is the direct, private data exchange between and among businesses that is driving global economic growth. For enterprises to monetize their data and extract the most value from it, they must re-think their data architectures to find the best ways to aggregate, exchange and manage data at the edge—and at scale.
Enterprise security has become infinitely more difficult to achieve. Cybercrime is growing at the same pace and sophistication as evolving technologies within digital business, including those undisclosed computer-software (“Zero-day”) vulnerabilities in systems, applications, data and networks that hackers love to exploit.