Business design looks at the enterprise from an outside-in perspective. Business design starts with examining the value customers receive through their interactions with the enterprise and how the products and services meet tangible and emotional needs. A key benefit that business design brings to an organi­zation over traditional EA is the focus that it places on value, and more importantly, on the value proposition that the products and services of the enterprise have for customers and clients.

Our digital transformations need to succeed. In the August/September issue of Cutter IT Journal, we demonstrate how to approach them more comprehensively to ensure that they do. You will discover a couple of key themes across the articles. The first is the necessity of a top-down, business-driven approach to defining, designing, planning, executing, and measuring digital transformation in a coordinated way across the enterprise. The second requires us to rethink the way we manage the constant stream of change within our organizations altogether. Managing change is our new normal, and continuous innovation is no longer a differentiator but a core ability.

Cognitive systems are well suited for use in customer engagement scenarios in general because of their ability to process questions similar to the way people think and to generate tailored recommendations pertinent to customer wants and needs. The Q&A format in which they interface with customers — allowing users to ask questions using human natural language — is particularly useful for establishing a more engaging and personalized relationship between a company and its customers. These capabilities can go a long way toward making customers feel like they are being treated as individuals when dealing with your company.

A Maxim for the Times:

When a digital innovation that could destroy your current business model becomes economically viable, that business model will be destroyed.

Any business model in use exists because it fulfills a need. Destruction of that business model does not mean the need goes away; rather, it is fulfilled by different means.

Agile implementations at scale involve many teams working in a coordinated way with the intent of affecting a broader set of business priorities and capabilities. At the portfolio and program level of such an implementation, it is critical to ensure that the value produced at the team level is aggregated into a broader set of deliverables and business outcomes that support a value stream.

Any portfolio analysis technique should embody the interests of all agents or stakeholders. Product managers earnestly pursue strategic alignment; administrators seek a healthy balance between growth opportunities and business constraints; and implementers work to deliver high-quality products on time and within budget.

A well-known conundrum of Agile is the word "Agile" itself. Agile means different things to different people, and sometimes, depending on the context, the meaning changes even for the same person. For a solutions developer, Agile is straightaway a method to produce a solution.

Big data analytics (BDA) is arguably the hottest information technology phenomenon today. Large and small organizations, in virtually every industry, are enamored with the ability to gather and analyze what would have seemed to be an obscene amount of data only a few years ago. This ability has led to the generation of insights that would not have been possible due to the complexity of the underlying data. As a result, these technologies are reaching the point of being fully diffused throughout public and private organizations worldwide. Even with this broad diffusion, we have only scratched the surface of what organizations can accomplish with analytics.

What is technical debt? Consider the metaphor of ­running through mud. There are two consequences of running through mud. One of them is low speed, because the mud has high friction; therefore it slows you down. The second consequence is that mud is much less stable, making it much easier to injure yourself, such as twisting your ankle or falling. These consequences are metaphors for developing with systems that have high technical debt. Everything else about the systems is harder to do, slower, and more dangerous — there is a higher risk of failure in production, and the systems will be harder to maintain.

Businesses need to face the connected consumer-led economy with clear-cut imperatives that are aligned in lockstep with business and IT. Every prior enterprise transformation journey has been enabled by IT, but the digital technology transformative imperative changes the equation. Most digital transformations lose their way due to the complexity of overlapping boundaries at a high level between traditional and digital IT.

Like it or not, hacking is no longer something to ignore or something that happens to “other people,” and it’s certainly something that decent people now talk about in polite company. If you think hacking is just a shady activity or something to be frowned on, then search online for “hackathon.” You will find some of the most interesting (and positive) development projects today.

We observe from digital transformation in the real world that EA as a discipline has the responsibility to facilitate changing the mindset or culture of the enterprise.

Developing cognitive applications requires training cognitive models to be able learn and reason from their interactions with data and their experiences with users and the environment they operate in.

Of all the scrum roles, the scrum master seems to be the hardest one to grasp in large organizations.

In this Advisor, we look at how teams can lose their coordination and their focus on delivering quality work while at the same time accumulating a significant amount of technical debt. This can be a hard balance to manage: delivering quality outcomes but sacrificing the longer-term success of the team by cutting corners and delaying necessary work until later for the sake of short-term delivery.

The distinction between businesses that focus on a relatively small range of products with a high volume of sales and businesses that embrace either a wide range of products or highly specialized niche products with relatively smaller sales volumes is highly relevant to enterprise architecture.

Traditionally, farmers have applied new techniques — such as new seeds, pesticides, herbicides, and so forth — to a small plot to observe optimal yields. Instead of such empirical analysis, which takes time, farmers are also embracing results from analysis of large, real-world data sets from public sources. Analysis of such big data can produce reliable recommendations much more quickly.

Understanding individuals and how they interact with each other is one of the key priorities of Agile.

Our vision strives for a software-driven approach that improves drones' energy consumption by establishing practices, tools, and metrics for developing and evaluating energy performance at the system level. With this knowledge, we can outline ways to utilize software solutions to measure and optimize drones' energy consumption and critically improve their flight autonomy.

By targeting basic human traits, enterprises can attract more customers, create a network effect, and improve employee productivity and performance.

A truly effective enterprise architecture should be something that senior executives in an enterprise use to manage their business on a day-to-day basis, that guides the implementation of strategy, and that helps them in communicating and implementing change.

A number of trends and developments — stemming both from advances in information technology and rising consumer/social expectations — are driving the commercialization of cognitive computing for consumer and enterprise use.

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.