There are few fields as well funded and simultaneously dysfunctional as software, and the opportunity for experimentation is huge. Every software project is unique, in terms of teams, business objectives, and so on. The system that builds, delivers, and manages software is as complex and unpredictable as any other. As we explore in this Advisor, if real complexity can be managed better by exposing the builders to complexity theory, then we can have noteworthy results.

AI is now being used in the legal field in such areas as research and document management, contract analysis, and e-discovery. Additionally, we are seeing significant developments with commercial AI applications in the form of cloud-based solutions specifically designed for handling such legal needs. These commercial applications, for the most part, are not intended to replace legal practitioners, but rather to assist them in performing various tasks associated with the legal process.

The main value of being increasingly agile is to allow the organization to realize its potential visions more quickly, with less investment, and with greater chances of success. To realize those visions, one must have abilities — both non-digital and digital. Digital abilities are information systems–enabling abil­ities that allow for agility. In this Advisor, we define digital abilities through a partial list of key abilities in the form of technologies, attitudes, and approaches we should adopt to become agile.

Despite general agreement among researchers and academics of the need for board-level involvement in IT governance, it appears that in practice this is more the exception than the rule. Given the prevalence of this issue, we have sought to answer the question, “What is the state of the art of the research domain of board-level IT governance?” In this Advisor, we share a few of our findings on the various determinants, theories, and outcomes surrounding board-level IT governance.

An architecture is often thought of as one uniform thing that underlies an enterprise. However, if an enterprise itself is not uniform, is it reasonable to think of its underpinnings as being consistent everywhere? Would we not expect an ideal architecture to mold itself around the curves and ragged edges of the real outlines of the enterprise, guiding it, and being guided by it? This Advisor suggests a three-dimensional framework intended to help in grasping the critical spatial dimensions of enterprises, and to assist in seeing the lines and jagged edges of a specific enterprise, along those dimensions. Architecture needs a ground to stand on – that ground is the enterprise architecture. It behooves architects to grasp what lies beneath their feet.

The cloud computing market has already achieved some major milestones since its inception. Yet it still remains one of the hottest markets in terms of growth, spending, and revenue generation. Indeed, it is still making its way up a steep growth curve, and as more features and functionality are added that allow businesses to embrace new business models and enable innovation, the growth shows no signs of slowing.

Much effort has gone into convincing various organizations within a government to start producing open data. However, the outcomes of such efforts have merely created a disparate landscape of a few leading — some following, some nonengaging — organizations. Hence, we cannot ignore the importance of the cultural and change management dimension when setting up an open data program.

This Advisor reflects on the technology evolution of data warehouses and data lakes as well as the evolution of computer-assisted decision making, including data exploration, analytics, and decision support. Arguably, the data warehouse still serves an important purpose and will continue to have a significant place in the IT infrastructure. It is important, how­ever, to consider new technologies to support the need for real-time data, dynamic data warehouses, self-service business analytics, and algorithmic decision making.

For a scrum team to be successful, it is important to learn of and solve problems as they occur. As we work together, we express how we’re doing, what’s in our way, and our concerns so they can be addressed. It’s an ongoing process of improvement from sprint to sprint. There are as many team dynamics as there are teams, so sometimes getting started is awkward if people feel uncomfortable opening up. As we explore in this Advisor, sustained success demands a brave willingness to be “all in.”

Gone are the days that an organization could plan for sustainable competitive advantage and build a five-year (or even three-year) strategic plan. The business environment has become ever-more chaotic, dynamic, and disruptive. Enter agility, as the new capability to develop transient competitive advantage with shorter planning and execution cycles. Welcome to the age of “agilification.” In this Advisor, the authors touch on the important interplay among leadership, culture, business architecture, and digital architecture.

“Value” is not a substance ladled into a product by some parts of the production process but not by other parts. Instead, value arises from an act of valuation on the part of the user of a product. So, in determining whether some activity “adds value,” the right question to ask is not, “Does the user of this product value the activity?” The user of the product does not value any production activities whatsoever, in and of themselves. What the user values are the features of the final product, regardless of how they were brought about.

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.