It is the enterprise architects’ responsibility to successfully implement and grow big data technology adoption. The real challenge, from my experience, lies in managing the various stakeholders involved and onboarding them successfully onto the big data transformation journey.

Human communication is based on natural language. Teaching computers to understand natural language is obviously a requirement, but our communication uses more than just voice (speech). An important part of human communication is our ability to see and understand what we see. Providing visual recognition capabilities to machines is the next big step in teaching them to understand a person when they speak.

Japan is placing considerable emphasis on the use of AI applications and it will prove an interesting source of new information on upcoming developments in the application of cognitive technology.

Ask any developer and they’ll tell you that they waste most of their time debugging or adding features to code after it is initially written. Many systems have been hacked up so much that fixing one bug can sprout several more and what should take a few hours to fix ends up taking days, or even weeks. But with test-first development, developers find many errors before they can become bugs. Tests instantly catch errors and problems, not only in the current module but also against the whole system. This helps developers write software that is compatible across the whole system.

Knowledge must be discovered and utilized in context. Creating our own route in our own work will enable us to develop our personal theory of problem solving, thus making sense of our deliberations and dis­coveries. Educating learners engaged in professional practice in-the-unfamiliar requires us to abandon the safety of universal theory and instead embrace the principles of personal discovery, reflective practice, sensemaking, and the development of capability.

This Executive Update focuses on the execution of digital strategies and begins with an overview of digitization along with the distinct categories in which transformation programs fall. It highlights the responsibilities and accountabilities of IT leaders in enabling digital transformation. This Update also examines key technology enablers that underpin successful digitization efforts.  

An architectural risk assessment is not a penetration test or merely a vulnerability scan. It is an engineering process with the aim of understanding, defining, and defending all the functional output from customers, line workers, corporate staff, and client-server interactions. Performed correctly, it will empower the technology staff and enable the business to focus less on security and more on customers.

Just as recent global events have given us reason to pause and reflect, the pace of technology emergence and disruption is proving to be a source of inspiration and uncertainty. Transitioning to a digital world is front-of-mind for many business executives, yet finding the right path is an ongoing challenge. So we asked Cutter’s team of experts for their insights on some of the technologies, trends, and strategies that will be relevant in 2017 and beyond. In typical Cutter Business Technology Journal fashion, our call produced a wide range of opinions and reflections worthy of consideration as you chart your business technology journey for the new year.

Artificial general intelligence (AGI) is currently emerging as an area where recent developments are likely to have a major impact on the way organizations do business, societies organize themselves, and even on how we address values and ethics.

The fact is that AGI already exists in our daily life. A common example is the GPS systems present in many new cars manufactured today; and let’s not forget the drones being used to deliver pizzas and cars that drive themselves. While automatic pilots have been used in commercial planes for quite some time, what AGI is about to offer to general business and human activity is well beyond what most of us have seen so far.

Artificial general intelligence (AGI) is currently emerging as an area where recent developments are likely to have a major impact on the way organizations do business, societies organize themselves, and even on how we address values and ethics.

The fact is that AGI already exists in our daily life. A common example is the GPS systems present in many new cars manufactured today; and let’s not forget the drones being used to deliver pizzas and cars that drive themselves. While automatic pilots have been used in commercial planes for quite some time, what AGI is about to offer to general business and human activity is well beyond what most of us have seen so far.

2017 is going to be a year of strange winners, and perhaps the strangest of all will be a giant leap away from technology and back to solutions that don’t rely on 24/7 connectivity. With the onslaught of major hacks and Facebook embarrassment, the antitech crowd may have its best year in decades. 

2017 is going to be a year of strange winners, and perhaps the strangest of all will be a giant leap away from technology and back to solutions that don’t rely on 24/7 connectivity. With the onslaught of major hacks and Facebook embarrassment, the antitech crowd may have its best year in decades. 

One of the most prevalent blockchains in the world, Ethereum, is poised to switch from a proof-of-work (POW) algorithm to a proof-of-stake (POS) algorithm, likely in 2017, with the release of the Casper codebase. Why does this matter? Because blockchain technology is becoming increasingly relevant and prevalent in businesses across the globe. It holds great potential to disrupt how businesses perform basic transactions, from payments, to programmable, self-executing contracts, to identity verification. 

One of the most prevalent blockchains in the world, Ethereum, is poised to switch from a proof-of-work (POW) algorithm to a proof-of-stake (POS) algorithm, likely in 2017, with the release of the Casper codebase. Why does this matter? Because blockchain technology is becoming increasingly relevant and prevalent in businesses across the globe. It holds great potential to disrupt how businesses perform basic transactions, from payments, to programmable, self-executing contracts, to identity verification. 

Let’s take a look at a technology poised for a breakout year: cognitive computing. There is a considerable amount of innovation in the development and application of cognitive computing across almost every industry. Consequently, companies should start examining how they can benefit from applying cognitive computing, focusing on five key domains: (1) research and discovery; (2) decision support and advisory; (3) customer engagement/customer experience management; (4) Internet of Things (IoT); and (5) cybersecurity.

2016 surprised most of us with two big political decisions that we didn’t expect. Britain had a referendum that returned a vote to leave the European Union — popularly known as “Brexit.” And the US voted for a business and property magnate who had never held elective office as its next president.

Commentators around the world have analyzed these two results, and the consensus seems to be that these political battles have ushered in a new era of politics in which the truth and rational debate are less important, and emotion, lies, and propaganda are more likely to sway voters. What does this mean from an enterprise architecture perspective?

Process of Things (PoT) is the new way of tapping into the worlds of IoT and business process. PoT ensures that things will not function as silos but contribute collectively to offering value-added services to enterprises such as developing smart applications around connected things and reaching out to more customers through adaptable things. 

IT project performance used to be characterized by runaway projects, late delivery, exceeded budgets, reduced functionality, and questionable quality that could translate into cancellations, reduced scope, and significant rework cycles. To combat this less-than-favorable track record of project delivery, the UK’s Association for Project Management has formed a rather challenging vision of a world where all projects succeed by the year 2020. On my wish list for 2017, though, is the more modest aspiration for a new kind of better-informed and more successful project management.

Today’s work environment is changing rapidly, so much so that the ever-increasing velocity of innovation and technology will create a workplace five years from now that looks nothing like the one of today. The big three factors in this coming change are: the “gig” economy, or project work, becoming the norm; moving from networks to ecosystem; and augmenting human work with AI systems

Today’s work environment is changing rapidly, so much so that the ever-increasing velocity of innovation and technology will create a workplace five years from now that looks nothing like the one of today. The big three factors in this coming change are: the “gig” economy, or project work, becoming the norm; moving from networks to ecosystem; and augmenting human work with AI systems

Last year, I declined to make specific predictions about specific technologies, what they’d disrupt, and their impact on society — a decision based on a mix of humility and cowardice. Instead, I made three predictions about challenges in the environment in which IT exists. These challenges have only become more real, making the predictions at least directionally accurate. In this article, I present updates to those predictions.

Last year, I declined to make specific predictions about specific technologies, what they’d disrupt, and their impact on society — a decision based on a mix of humility and cowardice. Instead, I made three predictions about challenges in the environment in which IT exists. These challenges have only become more real, making the predictions at least directionally accurate. In this article, I present updates to those predictions.

There has scarcely been a day the past few years in which there hasn’t been a breathless press release or article touting some new technological advance and how it will “literally change the world as we know it — forever.” We’ve seen in quick succession big data, 3D printing, and the Internet of Things (IoT), to mention just a few, given the mantle of being the next big societal ­revolutionary change agent.

There has scarcely been a day the past few years in which there hasn’t been a breathless press release or article touting some new technological advance and how it will “literally change the world as we know it — forever.” We’ve seen in quick succession big data, 3D printing, and the Internet of Things (IoT), to mention just a few, given the mantle of being the next big societal ­revolutionary change agent.

Every year pundits, hardware vendors, software vendors, consultancies, academics, and even government agencies publish their technology “picks” for the following year. One cannot help but notice that just about all of the lists identify the usual suspects; cloud computing, AI, predictive analytics, wearables, and augmented reality are among about five others that make nearly everyone’s “Top 10.”

The approach I take here is a little different. I look at clusters of technologies. I do this because of the overlap among the usual suspects and because of the interrelationships among the technologies. The identification of clusters is also useful because it acknowledges the interdependencies among technologies as well as the integration and interoperability required to optimize whole technology clusters and meaningfully impact business rules, processes, and models. In other words, it’s operationally impossible to acquire, pilot, deploy, and support technologies individually because they’re always dependent upon each other and always connected.