Transformation of a company and managing through change are inherently difficult. The inclusion of a digital component in this transformation effort makes the work that much more challenging, even for the sharpest and most visionary of executives. Companies undertaking digital transformation, in our opinion and based on our experience, face four major challenges that we believe design thinking tools can help overcome. In this Advisor, we explore two of these challenges and explain how design thinking can provide solutions.

Transformation of a company and managing through change are inherently difficult. The inclusion of a digital component in this transformation effort makes the work that much more challenging, even for the sharpest and most visionary of executives. Companies undertaking digital transformation, in our opinion and based on our experience, face four major challenges that we believe design thinking tools can help overcome. In this Advisor, we explore two of these challenges and explain how design thinking can provide solutions.

This issue of The Cutter Edge explores how the next two to three years will be important for blockchain in terms of implementing proper regulations and standards to enable wider adoption; why architecture must refocus on three core principles to play a pivotal role in business transformation, and more!

Organizations should view customer analytics as a way to help align the enterprise and make it function from the same set of metrics to provide the much-talked-about but often difficult-to-achieve “single view” of the customer. This view serves as the basis for making decisions about how best to interact with your customers; in effect driving all aspects of the customer lifecycle — from acquisition, retention, and growth to maintaining loyalty and measuring ROI across the organization.

Here in Part XI, we discuss how completion time estimates are determined and the biases that affect those estimates.

AI strategy, at its core, must address vital questions, such as the following: How can AI deliver better value to customers? How can it help companies increase revenues, enhance efficiency, and reduce human errors? How can AI capabilities be integrated into the existing organizational processes to develop a distinct competitive advantage? To address those questions, AI strategy must closely align with a company’s business objectives, ensuring synergy between the corporate strategy and the AI strategy.

AI strategy, at its core, must address vital questions, such as the following: How can AI deliver better value to customers? How can it help companies increase revenues, enhance efficiency, and reduce human errors? How can AI capabilities be integrated into the existing organizational processes to develop a distinct competitive advantage? To address those questions, AI strategy must closely align with a company’s business objectives, ensuring synergy between the corporate strategy and the AI strategy.

In a recent on-demand webinar, “Using AI to Improve Agile Teams,” Cutter Consortium Senior Consultant Jon Ward described a team that was able to cut time-to-market in half and reduce the cost to deliver by 60% by using Agile with artificial intelligence (AI). He addressed how AI could be used to further enhance a team’s productivity, where AI might inhibit it, and outlined where AI can be used to improve your productivity. This Advisor shares some of the questions Jon responded to at the end of the webinar, which you may also be considering.

In Part III of this Executive Update series on design thinking and digital transformation, we examine the top challenges that arise from pursuing a digital transformation strategy and how design thinking tools and the design thinking process can help address these challenges.

How does an architect see things that may be dark otherwise? There are many examples where seeing things differently make things different, sometimes with extraordinary, decades-long impact.

This issue of CBTJ will help you understand that a data architecture should be much more than merely a technology roadmap. To be of any value to people in an organization, the architecture should guide the people in an organization to an understanding of how to organize for ever-changing information requirements.

The data scientist role is perhaps the most important of all roles in the adoption of big data–based decision making.

In this article, Sagar Gole and Vidyasagar Uddagiri help you understand which fundamental concepts — specifically, the six elements of an enterprise-wide data architecture — you should address today in order to “overcome challenges and leverage the opportunities and benefits of digital transformation.” They describe the “secret sauce” that prepares your organization to thrive during a digital transformation journey.

How do you put context-setting information at the center of your data architecture? Adrian Jones illustrates how to do this by putting Cutter Consortium Senior Consultant Barry Devlin’s architecture into practice. He stresses the importance of context-setting infor­mation by pointing out the increased vulnerability to which we are exposing ourselves. We produce and use more and more data. In the backs of our minds, we know that data governance is of growing importance, but we don’t act in the right way on this knowledge. The problem with data governance is that it is never part of a data architecture but rather addressed as a separate process. If our data architectures are not aware of the vulner­ability being introduced, accidents are just waiting to happen. Jones hands you the recipe for avoiding these accidents.

This issue of CBTJ will help you understand that a data architecture should be much more than merely a technology roadmap. To be of any value to people in an organization, the architecture should guide the people in an organization to an understanding of how to organize for ever-changing information requirements.

This issue of CBTJ will help you understand that a data architecture should be much more than merely a technology roadmap. To be of any value to people in an organization, the architecture should guide the people in an organization to an understanding of how to organize for ever-changing information requirements.

Christian Kaul and Lars Rönnbäck explore what it means to adopt a data-centric paradigm. It certainly isn’t enough to have a data-centric data architecture; the implications are much more fundamental. The ultimate consequence is that you need to create a model-driven organization. By doing so, data architecture determines the shape of the organization, not the other way around.

Christian Kaul and Lars Rönnbäck explore what it means to adopt a data-centric paradigm. It certainly isn’t enough to have a data-centric data architecture; the implications are much more fundamental. The ultimate consequence is that you need to create a model-driven organization. By doing so, data architecture determines the shape of the organization, not the other way around.

Barry Devlin takes us on a journey to help us understand how context plays a big role in using data. Known for creating the first data warehouse architecture, he proposed a new standard for data architecture for today’s world in 2013. Devlin puts context-setting information at the heart of all data architectures, and for good reason. In the drive to digitize more business processes, the intricacies of how all stakeholders inter­act with data have been underexposed. Though it is understandable that getting a grip on technology and reorganizing your business is hard enough, it is precisely this interaction that will determine your success. If you turn your perspective around, as he argues, your data architecture will be of more value.

Clearly, blockchain will have an impact on technology and the ways we conduct business and how governments and not-for-profits provide services. But until we alleviate several clear barriers, progress will be slow. We discuss a handful of those barriers in this Advisor, including lack of interoperability, lack of mature toolsets, and lack of qualified personnel.

In this Advisor, we consider the four types of blockchain and explore some financial services where blockchain can be used effectively to enhance customer experience and increase the efficiency of those services.

With Industry 4.0, there is constant change in everything from business models to technology platforms to hype and social trends. Keeping up, let alone getting ahead, requires experimentation and constant reinvention. To support that, organizations need a steady supply of engineers in an ever-growing field of products, protocols, and platforms — and there simply aren’t enough to keep up.

Architecture should play a key role in creating the strategy for a digital business. But strategy alone is not enough, so architecture must go deeper by focusing on three pillars: (1) accelerated change, (2) decentralized decisions, and (3) public self-governance.

According to a recent Cutter Consortium survey, approximately a quarter of organizations are looking into using smartbots and intelligent assistants for their customer experience initiatives. Intelligent agents and smartbots enable customers to conduct common interactions in a conversational manner via speech and natural language speech or text-powered interfaces.

This issue of The Cutter Edge explores how to keep the digital train moving with minimal friction, cost, and effort; the characteristics that encapsulate how successful business architecture practitioners think and act, and more!