To bridge the divide between the general understanding of the concepts that drive the business model and the departments and teams into which the employees have been divided, the organization has to make the leap from a model-driven data architecture to a model-driven organization; it has to derive its own structure from the logical design.

The early emergence of a new, potentially viral term — a “lakehouse” — suggests another wave of fuzzy thinking is about to infect the data architecture space.

In the webinar, “Overcoming the Industry 4.0 Skills Shortage,” Barry M. O'Reilly discussed the skills shortage that is both inevitable and predictable when businesses try to solve problems with Industry 4.0, which is less about automating old processes and more about inventing a new world in which computing drives business rather than mirrors it. It is apparent that we cannot simply continue as we have in the past. Educating engineers faster, matching them to jobs more easily, and simply doing “the same old thing” has not solved the earlier skills crises — and Industry 4.0 presents even tougher challenges than what we have experienced thus far. In this Advisor, Barry shares some responses to questions following the webinar.

I often describe the strategic risk management of emerging technology and disruptive business models as a combination of continuity and change. Striking the balance is often difficult in a high-stakes, rapidly changing environment. However, one should find comfort and guidance in the fact that while the tech­nical components change, the principles of risk manage­ment remain the same. The trick is to understand the technology enough and apply the appropriate mitigation strategy to de-risk the solution while striking a balance between business value and the assumption of too much organizational risk.

Data architecture as currently practiced is beset with a range of problems, many of which are described in a recent issue of Cutter Business Technology Journal — as are some solutions to some of the problems. However, I contend that we need to return to basics in decision making and ask how decisions are actually made by people and in organizations.

From the need for more tech­nology regulation to what technologies will be most transformative, from guidelines for keeping our data safe to minimizing the profusion of misinformation — we are sure you’ll find value in the observations in this issue of Cutter Business Technology Journal. We trust that they’ll give you the foresight to proceed with optimism, yet vigilance, into this new decade.

With a systematic way to assess, design, measure, implement, and effec­tively manage data, leaders increase the chances of maximizing the true potential of AI/ML, while meeting the needs of regulators.

Agile budgeting in the value streams is about products, rather than projects, forming the value stream portfolio. A product can already exist and generate value or can be an innovative idea that may turn into a component of the value stream portfolio.

The combination of a blockchain data structure and a set of consensus rules allows something magical to happen: stateless, bankless, digital money (aka cryptocurrency). More precisely, what is loosely described as a cryptocurrency is a fungible “token” that is “native” to a blockchain. The major native tokens can be exchanged for the government-backed fiat currencies we are more used to, but smart contracts can create and maintain special-purpose tokens.

In the webinar, “Envisioning the Organization of the Future: The Cognitive Enterprise,” Cutter Consortium Fellow William Ulrich described the formalisms that underpin the cognitive enterprise vision, the benefits to be accrued under that vision, and the steps your organization can take to begin its journey toward a cognitive enterprise vision while achieving incremental value at each step along the way. This Advisor shares with you some of the questions Bill fielded at the end of the webinar and his responses.  

Transformer-based natural language processing (NLP) models have serious implications for the entire field of NLP — from speech recognition systems to natural language generation (NLG), natural language understanding (NLU), machine translation, and text analysis applications. Consequently, tools for developing transformer-based models have become popular among researchers and developers implementing NLP applications.

To increase the chance for blockchain/distributed ledger technology (DLT) to gain traction and for improved market awareness and adoption of the technology, what should the reference model of the regulatory sandbox dedi­cated for blockchain/DLT technology look like?

To create an organization that can effectively respond to heightened customer centricity and product customization, you need effective and coordinated product development teams. To respond to customer needs quickly, teams need to be autonomous, empowered, self-organizing, and cross-functional. They must take high-level direction from management and then, through intense collaboration, complete work more quickly than their predecessors. In this Advisor, we share our thoughts on putting up guardrails for your teams.

Digital architecture involves internal stakeholders shifting paradigms from thinking of solutions from within to fully incorporating external stakeholders (customers) in the solution design process. Different organizations leverage various approaches to achieve this paradigm shift, but one of the main drivers of digital architecture is customer centricity. This Advisor highlights this driver as well as some of the other most critical ones.

This issue of Cutter Business Technology Journal (CBTJ) continues the conversation we began in our last CBTJ and focuses on blockchain technology adoption beyond cryptocurrencies and financial services. The authors explore areas such as energy and utilities and government and present real examples of successful DLT implementations. They share their practical experiences in overcoming and addressing some of the known issues with blockchain projects.

In the era of open source projects, which enable deep analysis of code, it is doubtful that we can maintain privacy through the ages. Publicly accessible code facilitates consistent improvements and debugging through the regular scrutiny of accessible data. At the same time, it also increases the possibility of finalizing an attack on the network, causing losses and a reduction in trust. As a result, several companies now focus on analyzing blockchains.

Digital competitors have raised the bar and are forcing incumbents from virtually every industry into huge transformation programs. To win this race for digitalization, incumbents are investing vast amounts of resources into multi-year programs that require them to change the engine with the train still rolling. This requires executive teams to manage many challenges and produce multiple results that are either different than expected or diluted.

How should project teams corral the natural but emotional forces exerting pressure on project decision making and processes?

Mike Rosen’s webinar “Architecture for Digital Business” explored how to support your business transformation by taking an architectural approach to strategy. Here are four questions we asked Mike at the end of the webinar that you may also be considering.

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.

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.

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.

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.