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?

In practice, content and information management systems today haven’t fulfilled their promise. They don’t understand unstructured data, and they can’t directly act upon it. They work well only when people follow defined information governance processes.

One of the hardest things to teach or learn is self-awareness. By self-awareness, I mean the capacity to evaluate oneself accurately from the perspective of others and the ability to detect differences between our behavior and our values. So many leaders are unaware of what others around them truly think about them and at some point in these leaders’ careers, this inevitably leads to trouble. Usually these troubled leaders are constantly interpreting the world around them into their internal logic, thus preventing them from seeing the gap between their own internal understanding of themselves and what others think.

This article addresses the challenge of slicing data warehousing and business intelligence (DW/BI) user stories into small, business-valued deliverables to align with the Agile principle of "Deliver[ing] working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale."

The idea of a metaframework isn’t really anything new. It is simply a way of describing something that enterprise architects need to do.

We hope you will find the five articles on IoT in this issue of CITJ interesting, challenging, and practical. We also hope they will encourage you to experiment with the ideas they present in your own approach to integrating IoT within your business systems and processes. Needless to say, the data generated through IoT-based devices is indeed vast, unstructured, and changing rapidly — all the characteristics of big data. We see this issue as a major contribution to the literature on IoT, with overlaps in identifying the context, incorporating IoT in applications, and merging it with EA. As always, we welcome your comments on these articles.

Last week, Microsoft moved to beef up its own IoT offerings by buying IoT platform and services provider Solair. I like this deal because it gives Microsoft — which has been heavy on technology but short on actual IoT user stories — a company that can point to a good customer base with actual deployed IoT applications across various industries. Acquiring Solair, Microsoft also gets IoT hardware and IoT industry expertise, along with focused applications.

While the rise of financial technology companies and services has been gradual and steady over the years, beginning perhaps with the founding of PayPal in 1998, there’s been a particularly explosive boom in this sector just in the past couple years, with a new FinTech startup sprouting up nearly every week and disrupting the financial game in a major way. Indeed, companies like Stripe, Intuit’s Mint, Payoneer, and even such developments as Apple Pay are offering an array of fundamental changes in the many ways consumers and small businesses interact with their finances.

A frequent complaint we hear from Agile teams is that their self-organization is not respected and their manager routinely overrules their decisions. If you talk to the manager, he or she complains that the team doesn’t respect company policies anymore and makes decisions they’re not entitled to make. What seems to be a battle about power in many cases and like a confusion of self-organization with autonomy turns out to be an unfinished Agile integration into the organization.

In a recent Advisor, we called out an important function of architects: “to grapple with the elements of the enterprise that are disproportionate in their influence, and make them deliver [business value].” What is implicit in that statement is a recognition that “grappling” occurs because complex situations demand that we make the right tradeoffs and choices. It’s all about decisions. Who makes these decisions?

In my research pertaining to trends and developments influencing the market for, and the application of, data-centric security and protection that organizations will want to keep abreast of in 2016-2107, I have explored database threat protection solutions. These facilitate the discovery of all databases in the network (including those undocumented) and continuous monitoring of databases to identify malicious activity. New products employing machine learning (ML) and behavioral analysis to protect databases and facilitate real-time identification of compromised database credentials are available.

Somewhat like the data warehouse architecture before it, data lake thinking has focused mainly on the information/data contained therein — its types and structures, its modeling and usage, and so on. However, as we showed in Part II of this Advisor series, the IDEAL conceptual architecture emphasizes that information is only one of three spaces that require consideration. As we saw in that Advisor, process and people demand equal consideration. In Part IV, we discussed the aspects of process that deal with getting data into the lake and ensuring its internal consistency where required. This Advisor examines the other aspects of process: particularly choreography, as well as its supporting function of organization — the means of creating and managing all the processes of the data lake. We also briefly touch on utilization, which represents the applications that make use of the information to provide business value.

Many large enterprises struggle with annual project budgeting cycles; big up-front planning; arduous governance, portfolio, program, and project management and control mechanisms; and other systemic efforts that, while intending to establish more predictability/control and less risk, often do just the opposite. This can cause Agile initiatives to falter or to collapse under the weight of an unrelenting command-and-control mindset.

The truly game-changing opportunities or challenges we face in our businesses are a blend of the complicated and the complex. Being able to understand the difference between the two domains and manage accordingly is thus the key to success. An inability to differentiate between complicated and complex leads to one of the most fundamental causes of business and technology failure — the illusion of control.

It may be fair to say that many of the problems people see with enterprise architecture come from the name that the profession has given itself. “Architecture” implies a responsibility for the overall structure of the organization, a role that involves controlling and approving any changes to how the parts are put together. By analogy, this places the enterprise architect in a role similar to that of a building architect, with an overall responsibility for the structure of the enterprise.

Many have felt that “urban planner” is a better meta­phor, because the urban planner looks at how all the individual buildings fit together, considers how the structure and layout of a city affect the lives of its inhabitants, and helps to realize the overall vision of the city. I agree that this metaphor is better, and I think the evolution of urban planning provides some additional insights.

If you are considering how to leverage your unstructured data by adding intelligent search and content management to your enterprise content management platform, a good place to start is with a proof of concept (POC). Design your POC around a specific, measurable set of objectives. These objectives should cover a product’s primary content analysis function and how it meshes with your needs.

Emotion recognition platforms are now available that use neural networks and other machine learning algorithms to analyze and measure the facial expressions of subjects appearing in photos and video in order to determine their emotional state. Such analysis may take place on large collections of photo and video files residing in databases. It can also be performed in near real-time on images captured live — for example, for security scenarios or in-store retail applications involving shopper response measurement.