Tools and methods can work in some contexts but not others. If you have your own principles and mindset, then you can adapt or create your own methods and tools to fit your context. Once we realized this, we made a mental leap from a focus on methods and tools to a focus on principles and mindset.

In a recent survey, Cutter Consortium asked organizations whether their customer experience (CX) practices and technologies are meeting or beating company expectations. We also sought to answer the following question: can organizations that have deployed these CX practices and technologies deliver a better customer experience and journey?

Business architecture is a critical — and typically missing — bridge between strategy and execution. Organizations should leverage it to translate strategies and other business direction and collectively architect, prioritize, and plan actions to be taken from a business-driven, enterprise-wide perspective. Indeed, business architecture and business architects contribute unique value across the strategy execution lifecycle, as well as connect other teams and help them be more effective.

Transforming all the data we generate into insights requires many steps. For someone to have the confidence level to use the resulting information and insights, these data manipulations must be trusted. In this Advisor, we review these steps.

As we explore the idea of “making a digital shift,” it’s important to examine the ways to keep up the momentum and stay on track in managerial, not technical, terms. The premise is that, as with a paint job, meticulous preparation is essential to success. From the earliest days, partial successes and outright failures litter the history of digital shifts, with write-offs running into 10 figures on some government projects.

The myth surrounding Agile projects goes something like this: a small team of developers who can handle any coding task (database, business logic, user interface, middleware, etc.) works hand-in-hand with the end user who talks with the development team about the details of the work requirements. The small-team-filled-with-generalists model may work for some small projects, but it doesn’t scale. The problem has been with confusing two parts of the traditional development problem: collaboration and specialized skills.

In this Advisor, we take a closer look at another type of important COVID-19 data: secondary data, which can help with future pandemic predictions.

The volume of data available within an enterprise — and externally to it — is phenomenal. As a consequence, the role of information architecture has evolved, from the passive structuring and managing of data to a smarter, more active role of information effectiveness.

Researchers at hospitals, universities, and technical institutes are teaming up to apply artificial intelligence, machine learning, and analytics to help determine and predict COVID-19 patients’ hospitalization paths and medical needs.

The Industrial Agile Framework is a framework for applying Agile to physical product delivery. It pulls together everything that’s needed to design and mass produce a product, beginning with an idea and including design, components, supplier considerations, manufacturing, and everything in between. With Industrial Agile, you can change directions while working on product development and you don’t have to go back to square one. And, as with Agile for software, inspecting early and often means finding and fixing errors before they become excessively costly. At the end of their recent webinar on the Industrial Agile Framework, Cutter Consortium Senior Consultants Hubert Smits and Peter Borsella responded to some questions that you may be wondering about as well.

Established risk management methodologies and approaches tend to be static in nature and lead to models that are backward-looking. During the COVID-19 crisis, many companies have found their decision-making tools and dashboards for crisis management and business continuity to be inadequate given the geographic scale of the disruption. New risk models look ahead by utilizing AI and ML and can be continually updated as more data becomes available. In the first in a series of webinars, Tom Teixeira, Carl Bate, and Craig Wylie answered some questions about what risk management looks like in this changing business landscape.

This month's issue of Cutter Business Technology Journal (CBTJ) examines the new face of automation and explores novel ways to address the various issues and challenges encountered.

In my coaching, it is incredible how much pushback I receive on the idea of demoing your architecture. It seems Agile teams are comfortable demoing end-user functionality, but incredibly uncomfortable when you ask them to demo architectural elements.

Although many organizations’ IT departments have begun to focus on quickly adopting emerging technol­ogies, many of those organizations struggle to balance the need to deploy new technology with speed and agility while maintaining compliance with their organization’s traditional risk management frameworks. This struggle is why organizations adopting BaaS must shift from letter of the law (“box-checking compliance”) to spirit of the law (principles-based compliance) risk management (i.e., to risk-based methodologies whose foundational risk mitigation principles align with desired business outcomes).

Increased communication among team members leads to a culture of transparency. Transparency is about making shared assumptions explicit. The more transparent teams and stakeholders are with one another, the more the organization understands priorities, and the sooner critical feedback loops get closed.

In this Advisor, I adapt the term “directional selection” to a business use to indicate the natural shift an organization goes through when one way of achieving a business goal has a better economic outcome, or directional selection, than any other alternative. Indeed, directional selection in complex, large organizations is the force behind the many changes required to increase an organization’s fitness. 

There are two types of data involving COVID-19: primary data and secondary data. As we explore in this Advisor, primary data relates directly to the pandemic and measures outcomes.

As data becomes an increasingly large factor in every¬day living — overcoming organizations with a tsunami of new information — how can we cope with all this data alongside our legacy transactional data and make sense of it all?

In response to the coronavirus pandemic, facial recognition vendors are developing new algorithms and employing multiple biometric technologies to enable their products to identify people wearing masks.

Showing the Agile Manifesto to teams, managers, and stakeholders and discussing it in a two-hour Agile introduction session serves no practical purpose. We need instead to center mindset-based actions on the human aspect in order to achieve sustainability and generate better, high-quality value with a positive economic outcome for customers and the business. That healthy balance comes naturally from directional selection, which merges mindset, the human aspect, collaboration frameworks, and methods together.

A digital company uses digitalization to increase its value. Achieving this requires a holistic approach, which covers the business model, processes and organization, digital enablers (e.g., data and technology), and employee skills and company culture. Finding out which initiatives add value and contribute to company goals is key to finding the digital equilibrium.

As we fight through current issues and eventually come out on the other side to whatever will be the “new normal,” a word has increasingly made its way into the daily discourse of business and technology leaders, as well as of politicians (at least those who know how to spell it): trustworthiness. Some things are noticed only when we miss them; trustworthiness is one of those. It is interesting to delve into how we got to this situation; the role that information technology has been playing in the erosion of trustworthiness; and how it, like many double-edged tools, might help solve the very problems it has helped create.

In this Advisor, we consider two scenarios — investment planning and digital transformation — where using a capability model as a foundation for delivering information can be valuable.

In this last article of the data lakehouse series, the author polishes his crystal ball and shares his opinions on the likely direction of the data architecture considerations around warehouses, lakes, and any other forthcoming popular memes.

Although organizations at the forefront of digital transformation have successfully deployed the combi­nation of design thinking, Lean Startup, and Agile methods and immensely benefitted from them, most technology organizations and their leaders still do not appreciate how the three complement each other.