The three challenges described in this Advisor represent those that Agile teams face when attempting to follow enterprise architecture (EA)-level planning and design documents as well as the issues faced by Agile teams when transitioning their products to the operations team.

IT portfolio management is the continuous process of selecting and managing the optimum set of project-oriented initiatives to deliver maximum business value.

By putting the science of numbers, data, and analytical discovery to work we can find out if our assumptions about what is contained in the data are only opinion or truly fact.

Systems design has two perspectives: (1) blackbox (the encapsulated system is seen as a whole from the outside; the blackbox view is how the system performs when interacting with its context) and (2) whitebox (the system is seen as a set of interacting parts that collaborate to produce the blackbox behavior).

As the population of Southeast Asia grows exponentially, so does the reliance on smartphones and the Internet. This expansion of ecommerce is proving irresistible to the major technology players, which are jockeying for position at the top. This leaves little room for smaller players in banking, retail, transportation, and other services. Enterprises from Southeast Asia need to catch up with technological advancements and plan for the future as digitization becomes a top priority.

In this Advisor, I introduce the Agile Leadership Canvas, a rubric for guiding the Agile transformation conversation.

By implementing a service platform, our case study company combined complementary capabilities and resources from its existing business with insurtech capabilities — an essential part of the digitization of the insurance company.

As we do each year, we asked Cutter’s team of experts to weigh in on some of the technologies, trends, and strategies that will truly make waves in the months to come. We hope the articles in this issue of Cutter Business Technology Journal help clarify your organization’s path forward in today’s digital economy.

AI, in all its various forms — machine learning (ML), natural language processing (NLP), speech recognition, cognitive systems, intelligent agents, chatbots, and robotics — is generating intense interest across every industry. In effect, AI has sparked what can only be described as an explosion in technological innovation worldwide — innovation that is going to accelerate throughout the foreseeable future.

Endowing the old change management frameworks with the additional dimensions of technology and innovation could help change a company’s course. Along with process, people, leadership, and business, a new-age digital change management framework (DCMF) should emphasize developments in technology and innovation as well.

Practical application of big data requires a pathway that shows the activities and threads (or lanes) for actual implementation. The Big Data Framework for Agile Business (BDFAB) contains roles, deliverables, and business parameters that provide a sound basis for big data adoption.

Big data is not going anywhere soon. In the next few years, businesses of all sizes will be using some form of data analytics to make business decisions. More­over, as the Internet of Things (IoT) becomes more widespread, high-speed data processing, analytics, and reduced response times will be deemed more critical than ever. Meeting these requirements through the current centralized, cloud-based model faces a multitude of challenges. This is where fog computing can enhance and complement the cloud.

What we often regard as intractable problems are also ripe for disruption. While intractability is its own momentum, it’s also an invitation to change. 

In this Advisor, we illustrate a five-step neural network development process. Neural networks are systems of nodes, “neurons,” or processing elements arranged in multiple layers. The nodes are connected by links that, at any given moment, either pass information or don’t pass information from one node to the next. Upon the passing of information, certain connections become stronger. As specific outcomes or results are reinforced, pathways become stronger and the network comes to identify specific pathways with particular outcomes. This process is called training. As the network becomes exposed to more data and as it is reinforced, it continues to modify its outcomes and “learns.” 

The hunt for a suitable insurtech, which not only brings the desired business value to the table but is also the least painful to align with from an IT perspective, needs to be approached in a systematic manner.

Applying cognitive computing to a business ecosystem is by no means far-fetched, with the concept of the cognitive enterprise evolving in theory and practice.

There are multiple challenges that emerge as an enterprise moves toward an Agile development approach, both at the team level and when scaled to the program and portfolio level. Here are five pieces of advice from my personal experience with Agile development at the team and enterprise level and how enterprise architecture leaders can influence and impact the ability to deliver value.

One trend that will impact Internet of Things (IoT) development in 2018 is a significant growth in edge computing.

Senior management behavior is vital to the foundational change of establishing an enterprise culture for the digital era.

In this Advisor, we examine four common misconceptions that stand in the way of digital transformation.

Autonomous vehicles continue to make headlines.

Moving EA into a position of strategic importance involves making the case that enterprise architecture provides value, and that the business stakeholder can directly and immediately benefit from that value. Under­standing the services EA provides, and the value proposition for each one, is a requirement for making that argument.

Today’s digital technologies are driving continuous disruption. Survival is based on an ability to sense changes early — and adapt quickly. However, most top-down, hierarchical organizations have structures and processes that attempt to treat continuous changes as discrete, one-time events. Simply put, organizations are using change management approaches that don’t work with digital transformation. Digitally-driven change is messy and unpredictable. Unpredictability unsettles everyone. Successful orga­nizations of the future will embrace the mess — and fundamentally change their organizational culture.

A small number of cutting-edge companies have effectively exploited big data’s potential. These companies have a strong focus on data gathering, recognize the potential value locked in their data, and effectively use machine learning to convert potential value into real, measurable business value. In this Advisor, we outline examples of three such companies and the way in which they utilize ML in practice.

This Advisor examines the use cases surveyed organizations indicate as being most viable for applying blockchain technology.