Successful IoT use cases can emerge only by moving away from a product-centric approach that focuses on one-time sales and toward enabling an ecosystem of collaborating devices and services working in concert to build a longer-term, value-based customer relationship.

Roboethics owes its existence as a new discipline to robots and algorithms, but these are not themselves the real ethical threat. Rather, the threat comes from robotic and algorithmic approaches to situations where the human edge is critical to ensuring results that are fair and beneficial to individuals and society at large. Computers may or may not be involved; it’s the approach that matters. Addressing the threats needs to happen at multiple levels.

Traditional Agile does not consider enterprise architecture as a key part of the process but assumes that architecture guidance is being provided in the background. Traditional enterprise architecture (EA), however, has also failed to evolve and the majority of EA teams are under pressure due to the increased adoption of Agile within enterprises. Thus, the traditional role of EA has been under attack by the emergence of Agile within enterprises and its adoption beyond the IT domain.

In practice and training, we should use EA methodologies as a way to assist human thinking, not as a mechanical, technology-driven form of “box-checking.” Above all, we should understand that we create EA models so that we can use them as a basis for human analysis. The models are the beginning, not the goal.

It is up to the industry to take security into account from the design phase of IoE devices — and up to the users to demand it. We are confident that the articles in this issue will trigger ideas and provoke thoughts in this direction.

All that has been said and written about the challenges associated with the Internet of Things (IoT) does not quite prepare you for the practical difficulties that crop up as you start implementing and deploying IoT solutions.

Establishing the organizational foundation for service leadership where everyone is the customer should be a high priority. In my leadership experience and interactions with service leaders around the world, I have learned that being an effective leader comes down to one thing: care and they will care.

Connected consumer devices and Industrial Internet applications can generate incredible amounts of data from sensors and other operations — data that can be difficult to process using traditional data management and BI tools due to the diversity and size of the data sets involved. Achieving business value from this massive data stream requires the use of big data storage and analysis technologies that can scale to meet the ­constantly increasing demands placed on organizations.

For innovation to be valuable to an enterprise, it must have real tangible business impacts and be well positioned within the organization. Linking it to the underlying enterprise architecture (EA) demonstrates how ideas have evolved. Enterprise archi­tecture needs to be able to address real innovation. If the architecture changes, what effect does this have on innovation and its related strategies? Innovation management and enterprise architecture go hand in hand. Although each can be successful in their own right, it’s when they are used in conjunction with each other that the full benefits are realized.

Every so often, a technology innovation emerges out of nowhere that appears to fundamentally challenge the prevailing social and economic paradigms, enabling a leap forward into the future. The steam engine and railroads drove the Industrial Revolution, while electricity and telecommunications fueled the engine of growth during the last century. The Internet of Things (IoT) and blockchain technology are the latest core elements of a similar “revolution platform” that arrived with the fixed Internet 25 years ago and has evolved with mobile and analytics technologies during the last decade.

Chatbots and intelligent virtual assistants are receiving a lot of interest from companies across various industries wanting to add capabilities to mobile apps and popular messaging systems that will enable customers to conduct common interactions in a conversational manner via speech and natural language-text-powered interfaces. This Advisor looks at the trends and developments in this area.

Digital transformation occurs in economies, sectors, and industries. Businesses can choose to embark on a journey that makes them more than just mere observers of the transformative changes. So what happens on this journey? And how can the businesses transform?

Agile isn’t going to help if we continue applying outdated change models to how we transform; that is, a bunch of change people — either Agile coaches, change management folks, or the vendor — gather in a room and create the plan.

Inherent in the effective design of future technologies is the need for a speculative story assumed by the designers about how the future will unfold and the role that technology will play in that future world.

If we can stay focused on the intent and spirit behind the words “agile” and “architecture,” maybe we can avoid the cycle of hype and despair, at least as far as agility is concerned. This requires discipline with both words and actions because they are both part of the real world.

Self-service BI can lead to increased, widespread dissemination of BI/analytics tools and practices across the organization; in effect, helping to promote a data-driven culture. It allows employees of all types — at least in theory — to more easily locate, access, and work with a range of information and data. This is accomplished in several ways, including via highly visual, intuitive self-service BI tools and automated data preparation and workflows that provide advice on various steps of the analytical process.

In essence, a Jeopardy!-playing application posed two different problems: understanding natural language (NL) so as to be able to identify the right question and then searching a huge database of general information for an answer that fit the question. Searching a huge database quickly was a more-or-less physical problem, but parsing general questions in English and then determining which of several possible answers was the right match for the question being asked were serious cognitive problems.

The idea that an organization should change from a particular language, such as one of those named above or any of the myriad of others that may be currently in use, to find DevOps success is a slippery slope and is not necessary to implement the DevOps principle of increased deployment frequency. Any language will work well with the right principles and practices in place.

Enterprise architecture plays a growing role in strategic discussions and decision-making, and many EA components are no longer contained within a single enterprise because they form part of a much wider environmental, social, or human ecology. In other words, we are at a crucial tipping point for EA, where the decisions that enterprise architects make play a vital role in our collective destinies and futures.

“Big data” and “analytics” are among the most overhyped and abused terms in today’s IT lexicon. Despite widespread use for almost a decade, their precise meanings remain mysterious and fluid. It is beyond doubt that the volume of data being generated and gathered has been growing exponentially and will continue to do so, intuitively validating the big moniker. However, other vital characteristics of today’s data, such as structure, transience, and — most disturbingly — meaning and value, remain highly ambiguous. Analytics also remains troublingly vague, as it is prefixed with ­adjectives ranging from operational to predictive.

Interest in smartbots and intelligent virtual assistants employing AI, natural language processing (NLP), speech recognition, and other cognitive techniques for automating and enhancing customer interaction and experience is increasing.

It's not an overstatement to say Lean thinking is all about customer value. Anything that does not create customer value is waste and should be eliminated.

I think I have finally have come up with a good theory to explain why there have been so many stories of government projects in trouble recently: the inhabitants of Talos IV are managing them.

Establishing a successful, sustainable business architecture practice is a journey that takes time, especially within large organizations. While there are always many ways to achieve an outcome, this Advisor shows a practical and well-tested path to accelerate the process of establishing and maturing a practice.​

The coming of age of on-demand cloud computing has led to a surge in the adoption of infrastructure as a service (IaaS) because both the benefits and economics of cloud computing are so compelling. However, the haphazard way we buy cloud resources creates a new issue around trying to manage cost without compromising on newfound business agility.