Disruptive technologies unleash tremendous creativity, with effects far beyond the initial change. Successfully creating disruption requires more than just ideas and technical capacity; there's also cultural capacity.

The digital economy calls for inside-out exposure of core business processes across the enterprise to enable the seamless integration of data across partner ecosystems, social computing platforms, hyperconnected customers, and self-learning machines. More than ever, today’s digital world forces enterprises to quickly course-correct their business strategies and operating models to stay afloat. Course-correcting strategies and changes in operating models call for a robust foundation that is nimble and agile enough to adapt to both internal and external changes.

As organizations ramp up their Internet of Things (IoT) initiatives over the next few years, the huge volumes of sensor data generated by connected devices, machines, people, and processes is going to provide considerable opportunities for analytics and automation. Combined with data from enterprise and industrial systems, and mobile, social, and other sources, sensor data, when analyzed, will help companies better understand how customers use their products and respond more appropriately to their needs. But just how experienced are organizations with managing and analyzing sensor data?

Rather than look for champions of the change you've selected to implement, find the champions of any improvement. Actively seek them out. The key is that you have to be willing to not only let your champion work on his or her passion, but you have to actually support your champion's effort. It doesn't matter if the improvement was on your wish list or if it has high-visibility potential. If it will benefit the organization (at any level), and your worker is passionate about making it happen, that is your focus point.

The representation of architecture in the form of architecture artifacts is less important than the act of representation. Architects are no less susceptible than others are to pitfalls of assumptions and beliefs that are suspect and, perhaps, outdated. The act of representing forces architects to think. The goal of architects is not to create works of art that pander to the need for certainty and control, but to deliver frameworks that provide the context to question, poke, and prod. The goal is to surface concerns, bring out possibilities, and expand, at least a little, into the vast darkitecture that envelops the visible enterprise of architecture.

In a culture of innovation, a culture in which "failure" has no useful meaning, a culture where we cannot predict the outcome of our work, we're going to have to get a new idea about efficiency. We will not "get it right the first time." Well, we might, but that would be an accident. No amount of planning and arrangements will guarantee a valuable outcome. What we used to abhor as failure becomes a fact of life, a key feature of our work processes. We must fit our expectations to the fact that of the things, services, and ideas we find innovative, many if not most will not display immediate value.

Consider this: when the IT world wrote its first line of code, there were no methods and we were “flying by the seat of our pants,” so to speak. Then came the structured systems analysis and design methods and entity-relationship modeling for relational structures. Later, the object-oriented methods and the formal project management through Prince-II and PMBOK all provided increasing sophistication in developing solutions but, at the same time, added overheads and bureaucracies to the way in which we worked.

Evolution within security needs to move in a more adaptive direction. The contest between security and intrusion is an arms race like the "Red Queen Hypothesis" — the faster the threat evolves, the faster the response must become, and it is never possible to get ahead of the game.

Recently, I received a 750-page, five-year IT outsourcing contract and was asked to offer my opinion. I didn’t have to read it to express my viewpoint; I just replied, “It’s 700 pages too long.” It was not devised for human comprehension.

I have to admit, I’m not a particular fan of metrics. Many managers still seem to believe that you can manage an organization by setting numerical objectives and tie them to financial bonuses. This approach has utterly failed; most prominently as one of the root causes of the ethical and economic breakdown of major parts of the financial industries in 2008.

Growing up, many of us remember walking to school alone, hanging out with friends in the park, and then, eventually, finding our way home for supper. Today, for children of the same age, such activities are considered dangerous or even illegal. It’s staggering. But norms change. The challenge for most of us is keeping up with the pace of change.

We are seeing a serious push by vendors and organizations to incorporate machine learning (ML) techniques into both consumer and enterprise applications, where the technology is being applied to automate the analysis of large amounts of data, identify patterns, and learn preferences and behaviors.

Wearables (like tablets before them and mobile phones even earlier) are described in terms of what’s already familiar and known. The challenge for developers, marketers, and ultimately users is to look at new devices such as wearables on their own. This doesn’t happen overnight, and it also doesn’t necessarily happen along the lines of a scenario shaped by marketers (or even the media) that follow technology and trends.

The special properties of nanotechnologies are opening up new possibilities in medicine, electronics, photonics, biotechnology, and a host of other industrial applications.

The recently announced VW emissions debacle is something that I take personally.

Clarity, conditions, and constraints must work together to create the right balance of freedom and responsibility for teams. Without appropriate freedom, companies waste the experience, intelligence, and creativity of their employees. Without appropriate responsibility, teams may miss the mark or do foolish things. Conditions make it possible for teams to do work. Clarity and constraints bound autonomy and maintain the balance between freedom and responsibility.

The timing of bringing innovations to market affects the long-term viability of the enterprise. This Advisor calls out this timing issue specifically in order to focus more on opportunity cost, or potential revenue and profit. Creating new sources of revenue is not a mechanical or technical issue. It is a people issue. EA can support or create barriers, or it can help transcend them.

Over the next few years, we should expect to see some stunning new wearable products that are going to profoundly affect our technology, business, social, and legal landscapes.

There has been considerable talk among industry proponents and analysts about the need for standard frameworks and practices for building applications and products for the Internet of Things (IoT). But how do mainstream organizations actually view the importance of industry standards when it comes to supporting connectivity and interoperability between various IoT devices, machines, and applications? A recent Cutter Consortium survey that asked 80 organizations (worldwide) about their plans for the IoT helps answer this question.

The unfolding Volkswagen emissions control cheating scandal has all the ingredients and drama of a great Shakespearean play.

At the heart of the Agile movement is the concept that collaborative problem solving is a better way to work than having people sit in their cubicles working by themselves.

Maybe, just maybe, if we had thought of architects as archists, we would have been in a different future today. But maybe it is not too late? Maybe there are techniques and ways of thinking about the future that can be part of the future of the discipline we happen to call “architecture.”

Location data provides a rich store of accessible data that can be used in a growing range of applications. It is now about to become even more significant as we move into the era of the Internet of Things (IoT) and embedded analytics.

Within a few years, much of what doctors, lawyers, engineers, computer scientists, and professors — among many other professionals — do will be extended and replaced by smart machines. There will be a fundamental change in the definition, development, and deployment of "expertise." After an initial period of resistance (and some hysteria), we will welcome the capability and accessibility of our smart digital friends with open arms. In fact, we already desperately need them.

As a method for practicing continuous improvement on a day-to-day basis, the improvement kata complements Agile methods and integrates with familiar Lean tools.