To realize value in big data, organizations must establish a proper quality function around the unique characteristics of big data.This Advisor explores five strategic considerations in developing a quality environment in a big data initiative.

We have a growing body of evidence that points us to what makes a business architecture team successful. In this Advisor, we share a few characteristics that encapsulate how successful business architecture practitioners think and act.

Internet of Things (IoT) applications — including connected products and services in general, and remote condition-based monitoring and predictive maintenance in particular — offer considerable opportunities for companies to provide a better customer experience (CX) via real-time customer service and support.

Artificial intelligence (AI) strategy, at its core, must closely align with a company’s business objectives, ensuring synergy between the corporate strategy and the AI strategy.

Artificial intelligence (AI) and machine learning (ML) are technologies that can solve problems and do a world of good. But we must not forget that they are fallible and require human oversight. AI/ML developers must adhere to a strong code of ethics and employ ethicists to audit new applications. They must also ensure valid and reliable data testing and remain vigilant to the possibility that the technology may not work as intended.

Based on recent findings from a Cutter Consortium survey on customer experience (CX) management in the enterprise, most organizations appear to have somewhat guarded expectations of artifical intelligence’s potential to influence CX.

A business architecture initiative such as business capability modeling can be a challenging endeavor in many organizations. Typically, little clarity exists as to what a business capability is, what business capabilities are used for, and why a business needs them. The challenge is to address these concerns while succinctly articulating why adopting business capability modeling is a good idea.

Examples of unintended consequences abound in government and the social sciences, but also occur in engineering, business, management, IT governance, and other areas. In this Advisor, we explore some examples.

Design thinking helps companies identify real issues they never knew existed and leads to innovative digital and non-digital solutions never before imagined.

Digital transformation is not an end point; it is just a beginning. It is imperative to continuously evolve your digital solutions to keep meeting the needs of your users and your market.

This Advisor looks at two fundamental project metrics that everyone uses at all levels in the IT organization: size and flow.

While standard security practices still apply for automation initiatives, there are specific security practices to keep in mind for automation design.

Two major challenges hindering organizations from successfully implementing their customer experience (CX) management initiatives include difficulty defining and mapping just what the customer journey/experience should be and an inability to provide a seamless CX across their different channels. To tackle these issues, organizations are adopting customer journey mapping tools and service blueprints — two different but complementary practices.

APIs have moved beyond system-to-system integration and utility. They have now expanded to focus on solving problems for customers, the workforce, and suppliers/partners. So how do enterprises begin to evolve their API platform?

We are increasingly hearing about the role of the CCO — the chief customer officer — who has the authority to ensure that the organization provides a unified and seamless customer journey/experience across all customer channels and guides the adoption and dissemination of CX practices and technologies. But just how standard is the role of the CCO among organizations?

No matter where you are in your journey or what your stance is on digital architecture (is it real or just story?), this issue of CBTJ will challenge your worldview and challenge each of us “to work as the scientist rather than the consumer of the scientist’s work,” building and testing our own theories in the laboratory of our own experience.

Complex technology projects require a pushback against biases, oversimplifications, and the need for certainty that will inform many proposed solu­tions. The role of dissent is to harden and strengthen these proposals and to identify the right course of action among them.

What is the new new thing in artificial intelligence (AI)? What new methods or approaches have been invented? Based on my research, it appears that the real news in AI is processing: advances in computer speed, more solid-state technology, small embedded neural networks, and multilayer neural networks. So what does it all mean?

As artificial intelligence (AI) becomes more visible as a corporate strategic tool, organizations will have to incorporate issues surrounding AI as part of corporate strategy. In this Advisor, the authors examine some best practices for the successful implementation of AI initiatives.

In this Advisor, the authors examine prevailing concepts of trust in the context of AI applications and human-computer interaction. They emphasize that trust building is a dynamic proc­ess and outline how to build initial trust in AI systems.

Data democratization means providing equal access to everyone — level­ing the playing field between parts of the organization so that all parties can get access to the data. Data democratization is also a recognition that today’s economy is truly an information one, filled with information workers — and information workers need information.

This Advisor is a call to pause — to ponder where architecture has been and where it is and to provide a framework within which to do that.

Emotion recognition is an evolving technology that is still in the very earliest stages of enterprise adoption. However, commercial providers now offer platforms and services utilizing emotion recognition, and we are seeing end-user organizations express growing interest in using the technology.

This Advisor describes what an organization needs to develop and implement the organizational skills and capabilities associated with big data.

To incorporate a software delivery capability, your team should thrive on adopting new technology, practices, and processes. There are three primary ways companies can do this effectively, as we explore in this Advisor.