Artificial intelligence (AI)-based cybersecurity solutions offer significant advantages with respect to threat detection, response time, and, most important, reduction in false positive alerts.

Many aspects of software have changed in the last few decades, particularly since the advent of personal computers in the 1980s, the World Wide Web in the 1990s, and the widespread use of mobile and cloud computing in the last decade or so. Other key changes have been the massive trend toward outsourcing and offshoring in the 2000s and the widespread use of social media beginning in the 2010s. We explore some of these trends in this Advisor.

Recently, we described the application of Scrum at Riccardo’s Restaurant in London. This environment is similar to that of Lean hardware Scrum, in that shifts are repeatedly creating and delivering products in short cycles with high quality. Process efficiency and cycle time become the key metrics for production.

Digital transformation has hit a wall. The need for reinventing how we think about and approach archi­tecture is becoming ever more prevalent, especially if an organization is to truly become Agile.

It’s clear that organizations are going to have to extend their enterprise data protection practices to be more transparent and flexible if they hope to comply with the various (changing) requirements of data protection and privacy legislation.

Cybersecurity incidents lead to huge loss or severe damage to industrial assets. To mitigate cybersecurity threats, it is essential to understand the cycle of infor­ma­tion security governance and control: preparation, prevention, detection, response, and learning. This Advisor closely examines the security management cycle.

Digital transformation is not an end point; it is just a beginning. By going digital, your organization is only entering the game. In this Advisor, we share some laws of software evolution and the market forces at play.

The communication to design teams of new information that causes rework results in design oscillations. Since knowledge about how to complete a project is incrementally “consumed” by team members, design osc­illations are a natural metabolic byproduct of knowledge-foraging behavior. Design oscillations represent the key engine of the project metabolism. The problem lies in the timing of and team cooperation in synchronization of efforts related to design oscillations. 

It may be my narrow experience, but most Agile teams I encounter develop few to no diagrams or high-level views of the architecture they’re implementing. Instead, they allude to “being Agile,” where architectural docu­mentation is unnecessary, which implies that you simply collaborate around the code and magic (emergent architecture) occurs. In this Advisor, I suggest documentation as a good tool for striking the right balance between architecture and agility.

Despite the emphasis on and investment in cyber security, traditional approaches are failing to protect businesses and their customers. In this Advisor, we explore the benefits of adopting a new, unified approach that brings together technology and risk management processes, enabling organizations to better protect themselves against cyber threats and safeguarding their businesses, data, and revenues.

To some extent, pursuit of new technologies without a clear problem-to-be-solved is natural for basic research, such as for developing new materials. For applied R&D, defining the need to be met is essential, yet many times teams are not able to begin with a clear understanding of the user’s problem. Integrating the end customer into the team would clearly be ideal, but this is often not possible in large industrial development work (for software and non-software teams). So what’s a team to do to span the gap between its work and the customer market?

In this Advisor, we explore some of the areas in which specific Industry 4.0 technologies can create advances within regulated industries.

In 2011, the developer of the Netscape browser and cofounder of the Silicon Valley venture capital firm Andreessen Horowitz stated that “software is eating the world.” I remember thinking at the time that this was a memorable aphorism, but while it captured the increasing importance of software, it seemed somewhat cryptic or vague. Little did I realize that, over the next 10 or so years, it would come to articulate a profound transformation of the world we live in and, especially, the enterprises we lead and operate within.

There is no single career path for the business architecture professional today. Today’s business architecture professional requires educational career options that provide the flexibility needed to enable multiple career paths and choices.

It’s always interesting to examine what the big tech giants are doing because their efforts can have a significant impact on consumer expectations and trends and may serve as a wake-up call for other industries. This is certainly the case when it comes to big tech projects in fintech. And nowhere is this more apparent today than when it comes to big tech developments centering around the use of blockchain-based cryptocurrencies as a payment platform and for creating new financial services offerings.

The more organizations shift toward Agile, the more they need to redesign how they work with employees, how they search for new employees, and how they nurture employees’ development and careers. This Advisor describes the fundamental shift HR needs to make to support agility. Indeed, in an Agile organization, HR must shift its focus to the overall employee experience, choosing an employee-centric approach over the governance role that traditional HR departments often hold.

For decades, designers have used design thinking to develop products or services, but only in the last decade has the wider business community applied the approach. In this Advisor, we describe three overarching themes in the design thinking process and further break these down into six key steps to follow on your design thinking journey. 

Choosing a dissent strategy is difficult and the strategy chosen will vary from organization to organization. This Advisor helps software architects craft an effective dissent strategy.

This Advisor describes one way of establishing a non-blocking architecture governance practice for Agile development teams. 

Effective decision support requires ongoing innovation and refinement. As decisions become more complex and as data increases in quantity and variety, systems must undergo refinement and enhancement. Consequently, decision support requires a continuous and iterative design and development process.

Recently, I have had dreams of waking up in the middle of the woods in central Canada with no cellular signal, no Wi-Fi, no computers, no cell phone, nothing. And I am happy. Very happy.

In today’s competitive business environment, ethical issues arise frequently. Business partners may not respect contracts, or competitors may attempt to steal business secrets. With Industry 4.0, the situation becomes much more complex. In this Advisor, we explore some of these issues that may arise.

This Advisor looks at the top two benefits that organizations are interested in achieving with their customer experience efforts.

An understanding of the interplay of human users with automation, the underlying system actors (business applications, data, etc.), the business process, and the overall value chain is complex. What better way to keep human users at the center of auto­mation design than the use of design thinking? Design thinking has already been established as the best way to create solutions for wicked problems that cannot be solved by reasoning alone. By adapting design thinking for smart automation, we can analyze the problem space better and incrementally improve on the solution, moving toward success while considering human factors. The design thinking process has five stages: empathize, define, ideate, prototype, and test. Let’s investigate how we can adapt it for smart automation initiatives.

This Advisor focuses on the quest for explainable AI and examines some current tools and techniques for addressing transparency and fairness in machine learning applications.