Connecting business expectations and value generation through enterprise scenarios envisioned by these storyboards can carry several benefits that help narrow the gap between Agile teams and business users by ensuring a mutual understanding of the business and technical vision.

The most common problem we see in RFPs for cloud services is the reuse of tendering materials that were designed for the outright purchase of physical objects. Problems that arise include buyers being unnecessarily specific in the definition of their requirements, buyers trying to impose what they would do in their small data center to an exascale cloud provider servicing thousands of diverse clients, and many others. The best way to spot these problematic approaches is by way of analogy.

In this Advisor, I discuss how we addressed, in building our own private cloud, the security concerns that companies considering a migration to the cloud often face.

Succeeding as a digital business requires organizations to fundamentally transform their operating model in order to infuse the required level of flexibility and data-driven responsiveness at every level of the enterprise. Establishing an operating model geared for competing in a digital economy involves three key capability areas, as discussed in this Advisor.

It truly takes a village to build a nimble (Lean) and flexible (Agile) enterprise architecture (EA) program in this digital era. EA as a discipline may not have to change drastically to address digital transformation, but EA does have to play a much bigger role in embracing the change swiftly and facilitating the change across the enterprise.

Organizations seeking to incorporate effective analytics programs will likely encounter several challenges along the way. Whereas many of these can be dealt with in the short term, others will require solutions that we do not know to exist at the present time.

Big data has already demonstrated many successes, and experts assert that cognitive computing systems can actually make the context behind decision making “computable,” acting as a proxy for human intuition. It is that convergence — human creativity supported by relevant information — that offers the greatest potential.

It is hardly necessary to explain or justify that security is a concern when we think of applying Internet of Things (IoT) technology to industrial applications, but it is useful to consider how it differs in this context from the consumer domain.

Successful digital transformation enables the organization to embrace innovation, to develop new products and models, and to rapidly realize those to create value. Although there are no blueprints for success, we will examine three best practices that can allow businesses to increase speed and reduce cost, reach customers with an excellent user experience, and experiment with new products, features, and models.

Scientific insight, connectivity, computing, and technology have all reshaped civilization. We are officially in a Knowledge-Machine Age that is still going through its angst of childhood while its parent, the Industrial-Information Age, has withered away into senility, no longer able to guide the new child. Even the term "post-Industrial," which denotes the shift from a manufacturing to a service economy, is inadequate here. What we have facing us is the full-blown use of advanced computation driving all things in the economy: from manufacturing to all services.

When designing products and services for the Internet of Things (IoT), organizations should make defining procedures for ensuring customer privacy an initial priority to avoid embarrassing and potentially costly surprises down the road.

Business process change and cognitive computing do not necessarily overlap, but in business environments, they often do. Put simply, we will increasingly use cognitive computing techniques to improve business processes.

The key to Agile management is that the organization and analytics that implement the interlocking sense-and-respond loops up and down the organization enable overall organization agility.

Consumers enamored of mobile and social media are forcing banks and other financial institutions to optimize their interactions with customers and transform the way they conduct marketing, sales, and service. Today’s consumers expect financial companies to know them individually and deliver personalized interactions and self-service options.

How do you compute the valuation of something that has no clear definition? There are a couple of ways to respond: accept that it is good enough to have a very general value proposition for activities that somewhat fit under an “architecture” umbrella, or delineate specific architecture strategies and initiatives whose value can be measured in a manner similar to other investments that enterprises are used to.

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.