Many corporations have been doing cloud computing seriously for several years. For example, Proctor & Gamble (P&G), Johnson & Johnson (J&J), Allergan, and Sears Roebuck & Company (Sears) have already moved from adoption to the next stage of use and beyond. Seeing the IT function as a strategic partner, senior business executives in these companies, together with their CIOs, business systems thinkers, and relationship builders, have identified how cloud computing deployment can align with dynamic business strategy over time and how it can be operationalized, including with external service providers, for strategic business advantage.

Introducing Agile causes pain to your organization. This is not because Agile doesn’t work — it works for hundreds of organizations, some of them probably quite similar to yours. This is because Agile shows you the problems and deficiencies in your organization as brutally as the mirror you look into in the morning after a late night out. It is not the mirror that makes you look bad! The short iterations of XP and Scrum and the focus on lead time in Kanban work better the more mature the engineering and management skills in your organization are.

The Internet of Things (IoT) is driving demand for cloud-based platforms designed for building and managing connected solutions and for storing and analyzing the data they generate. This Advisor examines the available products for IoT infrastructure services.

Enterprise architecture has often failed when it’s targeted at modeling the entire enterprise. Thus, organizations must perform the appropriate amount of EA to achieve results. Agile approaches, such as Scrum, can sometimes be used to build small pieces of the EA that deliver exactly what the organization needs. Scrum techniques look at harnessing teamwork, delivering within time slots, and identifying “experts” to help with specific issues.

Let me start by explaining my use of the word “coalition” and set the context for using it with respect to enterprise architecture (EA) and Internet of Things (IoT) alliance or synergy. According to Merriam-Webster, a coalition is “a body formed by the coalescing of originally distinct elements.” While EA and IoT are distinct and unsimilar disciplines, my interest is the inherent synergy between them. In my opinion, this originates from the fact that EA and IoT each supports digital strategies for disruptive business operations.

In the past decade, the field of neural networks has evolved very rapidly as a result of new insights obtained in the mid-2000s, when several researchers published articles on deep learning. The number of algorithms has grown rapidly. For example, there is now a whole class of algorithms often termed deep learning, deep machine learning, or deep neural networks that have become popular. This topic can be a bit confusing as “deep” is currently being used in multiple different ways.

Many popular definitions of leadership emphasize charisma, vision, or position. The darker definitions hint at manipulation or even coercion. In software companies, though, people throughout the organization are smart, well intentioned, and capable of making good decisions. So we need a different definition if we want to make our companies more flexible and smarter.

The challenge for the enterprise architect is to figure out what the enterprise chorus lines are; in other words, what we might think of as the “core” architecture.

The question of ethical algorithms doesn’t just affect autonomous robotic operations. As more devices are being increasingly connected into an “Internet of Things,” how, when, and why should the gathered information be used and to whom should it be made available?

Watson Ads is the first application of Watson to advertising. The goal: make ads more appealing, interactive, and engaging; in effect, heighten the consumer experience by allowing them to make more personalized, informed decisions at the point of consideration. Instead of clicking on an ad and receiving a canned marketing spiel about a product or service, consumers will be able to ask specific questions in plain natural language and receive information with real, contextual relevance to their individual needs.

Agile requires real cultural change. Unfortunately, many people new to Agile don’t see the connection between the principles and the practices. In their eyes, Agile is just another process, something that requires following a checklist of behaviors. Other processes in their experience, such as CMMI and ITIL, never asked them to adopt a new worldview. Therefore, even if they become aware of the important connection between Agile principles and practices (which they may not), they may have no past experience in software innovation for making the connection between them.

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. Most of the publicly available knowledge about IoT challenges relates to high-level issues that are typically addressed through architecture and design decisions. One of our recent successful implementations, an enterprise-wide Remote Energy Management System (REMS), brought us face-to-face with an entirely new set of ground-level challenges, from data ingestion to data storage, to data processing, to data analytics and visualization.

Understanding the driving forces behind digital transformation, its effects, and the role that certain enterprise architecture best practices can play in embracing digital transformation will help organizations benefit in this challenging time.

If we could for a moment ignore the terms "enterprise architecture" and what does or does not constitute one, and "solution or system architecture" and what does or does not constitute one, we might reach some agreement on the concepts that underlie enterprise architecture. In that spirit, here is my take on enterprise architecture, without using the word (until the end).

I have often joked that data warehouses are as unfriendly to business users as physical warehouses are to shoppers. The reality is perhaps grimmer: data warehouse designs took no cognizance of the actual processes at work within human beings when making decisions. Data lakes, despite metaphors of recreational use and dipping in for data, actually pay as little attention to the mental processes involved in decision making as did data warehousing.

This article presents three examples of companies that have successfully pursued a strategy for using technical debt assessment as the basis for better technical and business decisions.

From the school of business, a belief emerged that the needs of business must drive technology, not the other way around. From the school of engineering, there is a belief that technology should shape the business. Naturally, the school of business typically wins. Yet speaking as digital business strategists, this is a gross oversimplification. Every organization has operational needs and line-of-business (LOB) concerns, all of which advance incrementally on decadal cycles.

By asking the CEOs of some of the most successful and influential companies in the world, such as GE and Google, a clear definition of innovation manage­ment emerges. The definition addresses the need to quickly and effectively implement organizational goals and objectives to remain competitive and the desire to strengthen advantages through the adoption of innovative ideas, products, processes, and business models.[1]

Cognitive computing, as the term is used today, does not refer to a specific technique, or even to a closely related set of techniques. It refers to solving complex problems by bringing together a variety of different techniques, tailored for the specific problems we face. One of those techniques involves big data and data mining.

I've been researching how banks and other financial institutions are using cognitive systems. The short story is that cognitive systems like IBM Watson, Expert System's Cogito, and Microsoft's Cognitive Services — as well as industry-specific commercial solutions built upon these and other providers' cognitive platforms — are increasingly being utilized in banking and finance.

The current market for Internet of Things (IoT) platforms is quite unsettled, consisting of a broad range of providers offering a large number (and somewhat confusing array) of IoT implementation products. These range from independent providers marketing comprehensive standalone IoT platforms and services to major enterprise software vendors whose IoT platforms are designed to build on, or integrate with, their various ERP, CRM, database, and other enterprise applications. Products in the standalone category include comprehensive IoT platforms in the form of platform as a service (PaaS) offerings designed to ­provide the infrastructure and facilities necessary for developing, connecting, and managing IoT-connected products and applications.

“Agility” is the facility of quick response — the ability to be nimble. In general, to be agile entails the ability to detect changes in your environment as well as the ability to respond quickly and appropriately. Being “agile” (in the traditional sense) is about excelling in a constantly changing environment, much like a serious athlete who masterfully integrates the aspects of balance, speed, strength, coordination, and reaction to the dynamics on the field. Management has two roles in bringing agile behavior to the organization:

The rapid pace of technology innovation that characterizes the digital economy is altering established competitive landscapes, breaking industry barriers, and redefining the core parameters of customer relationships. Competing in this disruptive, digitally fueled business environment — and realizing the unprecedented growth potential of digital business — requires a level of agility and responsiveness that cannot be delivered by conventional business strategies and operating models.

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.

Those of us born before 1990 know how much the world has changed in only a few years. Among a myriad of changes, we have seen obsolete technologies mercilessly replaced and witnessed a couple of global economic collapses, and several products and services that we could never have imagined before — like the Internet — have become part of our daily lives. Whenever I see new, life-changing technology, I can’t help thinking: how did we get here so fast?