When people use mobile apps they seldom think about the security implications. They sign up at an app store and agree to let the app have access to inbuilt sensors and communications capabilities; they also permit access to resources that may include personal data on the Web, corporate data and corporate applications, and/or network access to Web resources that might not be secure. Vulnerabilities can appear at any stage of the interaction between the mobile application and its data.

The enterprise players' IoT platforms are, for the most part, comprehensive IoT implementation environments providing platform-as-a-service (PaaS) capabilities. However, in addition to supporting the infrastructure requirements necessary for building, connecting, and managing IoT connected products and applications, they are also designed to integrate with, and take advantage of, the back-end, infrastructure, communications, process management/workflow, and analytics capabilities provided by the various business components of their respective vendors' ERP, CRM, BI, cloud, industrial control, and other enterprise software offerings.

Social business analytics is the most complex form of social media analysis because it involves analyzing unstructured social data in combination with structured data and other content maintained in enterprise sources. This requires an infrastructure for sourcing, managing, and analyzing social and enterprise data, and for integrating the findings back into the organization's enterprise data analysis and decision-support processes.

All friction is suicidal because it is your energy being wasted unnecessarily. We don’t have that much energy to waste in fighting with ourselves.

— Zen Master Osho

There is a new line separating human beings from each other and it is a great technocratic divide. Calling the reaction against this technocratic invasion a Luddite one doesn’t capture the significance of this scientific tsunami. All roads to the future lead through a forest of computing and scientific complexity. There are fewer mentally easy routes to economic prosperity. How the next generation sorts out their place in the 21st-century economy will be quite different than in the 20th-century economy.

The series of articles in the September 2015 issue of the Cutter IT Journal (“The Corporate Impact of Wearable Devices”) stimulated some reflection on the different perspectives taken by the authors. All of these articles, in different ways, discuss the impact of wearables on our relationship with technology.

The key point is that pace is relative — it is likely to be comparatively fast or comparatively slow, but there will always be some EA environments with a mixture of both fast and slow, and some that fluctuate between the two extremes.

When we talk about wearables, most of us have one or two specific devices in mind that we use to add tangibility to our thinking.

Disruptive innovation, a well-known business concept defined by Clayton Christensen, is changing. When he first defined this concept back in 1997, digital technologies already existed, but they were just beginning to make their impact on strategy and the process of disruption.

If you look closely at Agile, it is actually a huge advocate of building in quality and calls for everyone on the team to own quality. Agile/Scrum calls on the product owner to produce clear stories and acceptance criteria, the dev team to test their code, testing staff to be involved with the dev team from the start, and of course have customer involvement whenever possible.

Being true to architecture’s roots in business does a couple of things: it ensures that we stay grounded in things that matter, and it provides a framework of values that guides and validates everything that we do in the name of architecture.

Technology seems to be moving at the speed of light these days, so we decided to ask Cutter’s team of experts for their insights on some of the technologies and trends that are going to be game changers in 2016 and beyond. In true Cutter IT Journal fashion, our call produced a wide range of opinions on what everyone from C-suite executives to technology managers should plan for as they strive to meet their business and technology goals.

Organizations developing Internet of Things (IoT) connected solutions face a number of considerations, including decisions about which wireless and network protocols to use, device connectivity issues, messaging protocols, security, scalability, and data storage and analysis requirements.

A new pilot application developed by US retailer Sears to help customers find and compare tires offers a good example of the types of natural language processing (NLP)–powered customer engagement and customer experience applications we are seeing.

In the difficult global competition today and ahead of us, finding solace in victimhood serves no one. As in the past and in the future, events will be dictated by teams of people believing all things are possible. Setting big goals, establishing metrics, and monitoring performance become important, but the most important metric will be counting the myriad of small successes found in daily challenges and the continual pursuit of even more complex ones. Is your organization so brave as to measure something this mundane?

When a company adopts new ways of working and embeds them throughout the organization, it is imperative that all new employees, in addition to having the skills necessary to do the work, are also either already practicing the company's ways of working or are very willing to embrace them. Without this, the company runs the risk of diluting its Agile "gene pool." All change initiatives are fragile, and introducing additional critical or dissenting opinions can upset the balance and be detrimental to the change effort.

I’ve been an enterprise architect since 1984, and the main thrust for EA over all those years has been about giving direction to architectural evolution.

In looking at some of the key trends and developments affecting the market for, and the application of, data management and analytics that organizations should track in 2016, the Internet of Things (IoT) promises to be exceedingly disruptive to almost every industry. It’s a given that companies must consider how they can take advantage of connected products and services and plan for the significantly increased data workloads that will come with deployment of sensor-enabled products.

Business continues to change under the increasing impact of IT and globalization. In today's world, cloud computing makes it possible for a small company to access the resources such as accounting and management tools just like those used by larger companies at a relatively small price. Outsourcing makes it possible to create a business from an assembly of services, with innovation itself often outsourced. Crowdfunding is creating specialty startups outside the normal bounds of investment, and enormous growth in mergers is making acquisition a critical and routine source of new business ideas.

Traditional technology adoption models describe a process that’s phased and driven by validated requirements analyses. These traditional models have defined the technology adoption process for decades. Emerging hardware, software, and networking technologies like the Internet of Things (IOT), automated reasoning, cashless payment systems, real-time analytics, augmented/virtual reality, 3D printing, and always-on tablets are changing the way business operates.

It's one thing to espouse the virtues of Agile by giving examples of young organizations such as Amazon, Netflix, Spotify, and others that are built on new architectures and tools. It's entirely another scenario when speaking to older enterprises that must manage significant technical debt, as well as business and technical architectural complexities and interdependencies, often with strict regulatory requirements, across simultaneous legacy and cloud projects. All this adds up to an environment fraught with fragility and risk.

For the IoT to be successful, it is essential that connected devices provide a friendly user experience. This is especially important when it comes to consumer products for such market segments as smart homes, health and fitness, connected cars, and gaming. But I think it also holds true — although to a lesser degree — for the wider deployment of connected equipment and smart machines in business environments. Simply put, consumers and businesses will resist using devices, appliances, and equipment that are too difficult to set up or too confusing to operate. Consequently, for many IoT scenarios, we can expect to see connected devices increasingly utilize voice- or speech-powered interfaces that offer hands-free operation for the user. However, voice and speech will not replace other forms of UIs like touch and gesture; certainly not any time soon. Rather, they will be combined with these technologies to provide multi-modal interfaces designed to optimize interaction with connected devices.

API is among the current IT buzzwords (or, should we say, buzz acronyms?). You can spot such hot acronyms from time to time because they are on everyone’s lips, and everyone appears to know what they stand for so there’s no point in explaining them. Yet it’s sort of like running into an old friend — someone you know you know — and carrying on a casual conversation while you try to figure out who in the world that person is, where you’ve met, and what that person’s name is.

The Internet of Things (IoT) promises to cause disruption in almost every industry. Companies need to examine how they can take advantage of connected products and services and plan for the significantly increased data workloads that will likely come with the deployment of sensor-enabled products. However, an expected surge in product innovation also means that companies should carefully consider how they will deal with the potential rise of new, more agile competitors whose business models will be based primarily on IoT products and services. Here are some points about the IoT I've been discussing with colleagues that organizations may want to consider as we head into 2016 and beyond.

Today we are witnessing a new round of interest in AI and knowledge-based computing that is currently being called cognitive computing. This new round of commercial AI is based on the hugely more powerful computing platforms we have available today. Moreover, it is based on new AI techniques — with the emphasis on neural networks rather than business rules. Business rules provided the basis of some great expert systems, but they didn’t provide a basis for software systems that could learn and adapt — and neural networks do provide these advantages.