Customer expectations have never been higher, and they are forcing companies across almost every industry to rethink their approaches to commerce, marketing, sales, and, in particular, customer engagement, service, and support. The result is that companies are taking a great deal of interest in all things related to the experience customers encounter when dealing with their businesses.

As companies adopt Agile as their standard for software development, they usually encounter resistance from several directions — from other parts of IT as well as from the business. We often see organizations struggling with cultural change, insufficient business involvement, and other aspects of scaling. To overcome these challenges, some organizations use ways that worked for them in the past, but in an Agile context this results in counterproductive outcomes. These anti-patterns are hard to root out and tend to reappear. In this Advisor, we highlight two of the most common Agile anti-patterns.

Data governance, a broad and very expansive subject, potentially applies every time there is a transition between data states (i.e., contextual, temporal, or geographic) or when data is accessed. Currently, adoption of data governance policies tends to be passive and non-active rather than overt or proactive. In other words, data governance is the default, out-of-the-box governance that requires no action. But today we have the opportunity to create more individualized overt and proactive data governance policies that meet the specific needs and requirements of a corporation. Indeed, the issues of responsibility and liability for keeping data while it is at rest or in motion are beginning to dominate many conversations about data.

Applying the principles of “loosely coupled” to master data and containing fragmentation within a framework that governs the collaboration process will lead to the design patterns of solutions that fit the collaboration process. This is designed fragmentation, or “connected architecture.” This framework — a thought process more than a recipe — is described in this Advisor.

Artificial intelligence is greatly impacting manufacturing, especially for adding intelligence to Industrial Internet of Things scenarios. This includes predictive analytics, machine learning, computer vision, and edge computing to leverage data streaming from millions of sensors deployed on vast numbers of connected devices, industrial equipment, buildings, and other infrastructure.

Today, analytics and computerized decision support can help managers make better choices in semistructured and even unstructured situations. Managers should be curious and seek evidence and answers from data for previously unasked and/or unanswered questions. Indeed, as this Advisor explains, they should become data explorers and sophisticated decision support users.

In this Advisor, we identify five key success factors for realizing the benefits of a Breakthrough Incubator implementation.

In this Advisor, the author describes the conflicting demands on today’s CDO, who must cover “operational data management” as well as “data management for analytical insight.” He shows the importance of caring for the quality of the data, understanding its provenance and pedigree, minimizing the transformations, and adding semantic understanding of the data.

Software architecture requires balance. Often, you can focus too much on it, creating robust products that miss customer needs or over-engineer solutions. Conversely, especially in Agile contexts, you can under-engineer things and your product efforts can succumb to relent­less refactoring rework. So there’s a balance to strike in architecture, no matter what methodology you use to create your software. In Agile contexts, that balance is often lost. And it usually leans to less over more. In this Advisor, I describe a rule that has helped me successfully strike the right balance between Agile and architecture: chaos is constant, so continuously refactor.

Line-of-business (LOB) applications are a trillion-dollar invisible industry that powers software in nearly every enterprise, large and small — from hospitals to insurance firms, logistics companies, and even our local department of motor vehicle offices. Yet it’s an industry that has rarely seen any innovation. 

Artificial intelligence has many applications in supply chain and logistics. These range from automating the analysis and reporting on global supply chain activities and optimizing supply chain planning and execution to predictive applications for scheduling, strategic sourcing, and remote monitoring and predictive maintenance. This Advisor describes the major drivers for applying AI in the supply chain, including demand for greater visibility and transparency into supply chain data, processes, and execution, and a need to reduce risk and satisfy customer demands.

The path to becoming a digital company is difficult and the challenges are multifold. It means ensuring customers remain connected even with the drastic changes that may be needed and overcoming resistance to new business models. Becoming digital does not simply mean implementing new technology; it also requires developing new leadership skills combined with connectivity among a company’s people, proc­esses, and data. Cultural changes may also be a challenge if the digital transformation must cut across silos in the organization. This Advisor’s case study examines the implementation of a digital change in a US community bank meant to retain its loyal customer base and to put in place newer ways of monetization.

There is a giant universal mental challenge when it comes to agilifying. Yet the goal of the modern organization is to rebuild itself all the time. Agility can mean different things to different people, and it should. Still, it is important to vividly understand its various meanings and to allow the organization to be aware of these meanings and then prioritize them. This Advisor offers some con­crete ways in which we can both shift our perspectives and act to “agilify” our organizations.

Being hybrid is not new. Not at all. If we look around, we can see the entrenched old and the emerging new sitting side by side in our enterprises. And, in important instances, we have actually thought through, at some level, how the old and the new are tied together, thought of together, and managed together. If we do it right, we can build hybrids that allow us to have one foot in the present and one foot in the future, as we make our way across the skybridge to the new buildings of the future.

Given the heightened cybersecurity environment currently faced by organizations, what should you as the CSO provide in response to a board request for assurance that the company is perform­ing its fiduciary duty? Moreover, what information should you provide to assure the board that it is appropriately protecting the company? The answer is confidence — along with the five-section standard presentation template presented in this Advisor. 

In this next-generation world of cloud computing, where the Internet of Things (IoT), artificial intelligence (AI), blockchain, edge computing, and cloud federation are enabling the vast deployment of innovative and resilient solutions, we must begin thinking more clearly about geo-jurisdictions. In this Advisor, we review three examples of how geo-jurisdiction policies and legislation can lead to dysfunctional businesses and economies.

The way we do business is transforming. It’s being pushed and pulled by many factors, most notably by a younger workforce demanding new interfaces and services necessary to perform jobs their way. At the heart of every digital transformation project is an immutable pain wrapped in competing motivations: doing more, doing it faster, and avoiding missed opportunities. How can we reconcile these motivations in our day-to-day business? When we discuss the concept of “a culture transformed,” we can draw many parallels against the way a society votes and spends, but that’s out of scope. What is not out of reach is your influence over your teams and whether you can embrace the change that is upon us all. It’s up to you to set the tone of the conversation within the organization: robots aren’t here to steal our livelihoods; they’re here to make us more productive and lighten the load, right?

Disruptive changes within a company can result either in a spectacular rise if done right or an abrupt decline if not handled well. People are at the center of every change. If employees do not connect with their organization, do not see the need for change, do not buy in to the leadership’s vision, or are not motivated, any change will fail. For employees to see value beyond the defined work parameters, they need to feel connected to the company. Connection, respect, and trust will help a company maintain employee support for the changes that a company wants to implement. In this Advisor, we discuss in brief two such initiatives that brought about drastic increases in employee support and participation.

Capability assessments are a topic of high interest and frequent discussion among business architecture practitioners and leaders but are not executed in practice as often as one would think. This is typically because people do not know where to start or become quickly overwhelmed by the potential options and detail. 

Given an established enterprise with its decades-old IT department, processes, and practices versus the accelerating marketplace — missing out on modern IT practices and being too rigid to react to market trends, and even putting innovation on half-yearly cycles — the hiring of a talented Agile architect can bridge the gap and lead the recently established digital pillar of the company. In this Advisor, we explore the common challenges the architect faces via the story of a day and propose building a “non-blocking” architecture governance practice for Agile development teams.

There is a lot of talk about how AI offers the possibility to transform how organizations operate. I am guilty of throwing around this type of statement myself. Of course, this brings up the all-important question: to what extent is AI currently transforming how organizations operate? Fortunately, the latest results from our ongoing survey examining the adoption and application of AI technology in the enterprise offer some insight into this question.

In this Advisor, we explore the benefits of a promising breakthrough growth model that we have successfully applied in both B2C and B2B businesses. This model delivers major benefits in terms of speed, cost, and likelihood of success. It involves radical collaboration across the innovation ecosystem and covers the entire innovation process from idea to commercialization, including the strategic, commercial, operational, and technical aspects. We call this the Breakthrough Incubator (BI) model. The BI model enables accelerated creation of a new business proposition with new products/services externally — before transitioning it back into the parent organization, thereby overcoming many of the prototype scale-up barriers. In essence, this is the “build, operate, transfer” philosophy applied specifically to innovation and product development.

Recently, the Cutter Consortium editor who facilitates this Advisor series sent me a set of questions frequently asked about Agile transitions. Among the ones I found most intriguing wasn’t really a question but merely a statement, claiming: “Misunderstanding on the part of teams that Agile allows for less discipline, leading to less precise delivery schedules.” There are several elements of this statement that I encounter frequently, so I decided to use it as a basis for this article.

Big data, the IoT, and the cloud are technological innovations that need to demonstrate corresponding business value. While the aforementioned technologies have distinct identities of their own, they are also interdependent. Innovating with these technologies at a business level demands a multidisciplinary, holistic approach that also incorporates an understanding of how to manage risks. In this Advisor, we discuss challenges that arise in real-life scenarios due to lack of interoperability and some practical standards in the IoT and cloud space.

Starting from a data warehouse just makes sense. Of course, the architectural thinking and technology offer valuable intellectual capital to IT. But the real value comes from the decades of experience in information/data governance and management, as well as the interpersonal and organizational skills that data warehousing imple­menters have gathered. As you will see from the articles in this issue, the contributors are on the same path.