This article gives us the complete context of what governance means, considering the data lifecycle (create, store, use, etc.) and the cognitive hierarchy of data, information, knowledge, understanding, and wisdom. The authors also look at the elements contained in a formal data model and what these ele­ments tell us about the governance actions that need to be taken when data is accessed, modified, or deleted.

This article gives us the complete context of what governance means, considering the data lifecycle (create, store, use, etc.) and the cognitive hierarchy of data, information, knowledge, understanding, and wisdom. The authors also look at the elements contained in a formal data model and what these ele­ments tell us about the governance actions that need to be taken when data is accessed, modified, or deleted.

This article describes what good governance means for public sector institutions that are embracing open data initiatives. While these organizations make data accessible to increase government transparency and promote economic empowerment, they face addi­tional responsibilities in terms of data quality, privacy compliance, security, and more.

This article describes what good governance means for public sector institutions that are embracing open data initiatives. While these organizations make data accessible to increase government transparency and promote economic empowerment, they face addi­tional responsibilities in terms of data quality, privacy compliance, security, and more.

Michael Atkin depicts the conflicting demands on the CDO, who must cover “operational data management” as well as “data management for analytical insight.” He shows how caring for the quality of the data, understanding its provenance and pedigree, minimizing the transformations, and adding semantic understanding of the data are part of the new responsibilities of the CDO 2.0.

Michael Atkin depicts the conflicting demands on the CDO, who must cover “operational data management” as well as “data management for analytical insight.” He shows how caring for the quality of the data, understanding its provenance and pedigree, minimizing the transformations, and adding semantic understanding of the data are part of the new responsibilities of the CDO 2.0.

Steven Woodward explores in depth the issues of data residency, using the term “geo-jurisdictions” to describe the intersection of geographical and legal boundaries that place constraints on the handling of data. Woodward begins by alerting organizations that falsely believe that data residency is not a concern for them. From this warning, the author moves on to concrete recommendations about policies that should be put in place for various service and deployment models as well as the need for a thorough geo-jurisdiction analysis.

While issues around data and information governance are starting to get the attention they deserve, business and technology leaders still need help finding their way through all the conflicting demands. We invited several authors to present their perspectives and recommendations on this complex web of issues.

While issues around data and information governance are starting to get the attention they deserve, business and technology leaders still need help finding their way through all the conflicting demands. We invited several authors to present their perspectives and recommendations on this complex web of issues.

Generally, business transformation results in designing a target business operating model (BOM), or in other words, a new “business design.” To implement the target BOM is to operationalize the business strategy. With each transformation cycle and new target BOM, there is a shift in decision making depending on changes in the organizational hierarchy and ways of working. To make sure that this shift occurs smoothly and is working as expected, it is imperative to continuously measure the BOM’s effectiveness both in terms of current and predicted performance.

In this Executive Update, we briefly revisit some earlier lifecycle development models and examine the historic move away from waterfall to DevOps via iterative/ incremental and Agile development approaches. In doing so, we push the Agile envelope a little further and consider some trends that affect business analysis and requirements specification and examine the implications of the Agile development framework for business analysts (BAs) when working within a DevOps regime.

Fog computing can create a digital twin of difficult-to-replicate process. Let me explain, through a hypothetical example of a craft brewery. Unlike industrial production, food and beverage manufacturers work with natural ingredients, where the quality of raw materials can vary, making it more difficult to create a uniform product without waste. Producing a consistent quality product is critical to building cus­tomer loyalty. When it comes to products of nature, in particular, manufacturers face unique challenges.

In this Executive Update, we explore a promising breakthrough growth model that we have successfully applied in both B2C and B2B businesses. This model delivers major benefits in terms of speed, cost, and likelihood of success. It involves radical collaboration across the innovation ecosystem and covers the entire innovation process from idea to commercialization, including the strategic, commercial, operational, and technical aspects. We call this the Breakthrough Incubator (BI) model.

This Advisor — one in a series of “Agile Team Tips” — describes the benefits of backlogs and backlog grooming.

When it comes to delivering effective digital transformations, human behavior is often overlooked in favor of a focus on technology. In this series of Advisors, we outline how organizations can truly engage their people by understanding their behaviors and, consequently, ensure that they undergo successful digital change.

The critical role of a business architect is to understand the business needs and design the fundamental business elements that can be configured in many ways to realize what the business wants.

Over the last 12-16 months, we have seen more than a dozen banks and financial institutions worldwide introduce (or announce) virtual banking assistants and bots. Such apps are now available to millions of personal banking customers, and banks are also starting to introduce AI-powered digital assistants for managing corporate customers’ accounts. This Advisor explores some examples of intelligent virtual assistants and bankbots companies have introduced over the past year or so.

In this webinar, Cutter Consortium Senior Consultant Greg Smith reveals how an organization can truly engage its people by understanding their behaviors, and how it can balance empowerment, vision, engaged leadership, tolerance for failure, tangible incentives, and the belief that transformation can actually happen into successful digital change.

Data centers that support today’s cloud systems must implement extensible networks based on technology that allows them to scale in an Agile, cost-effective fashion. Market trends include customer microsegmentation analytics supported by communications service providers that leverage microsegmentation analytics to improve customer retention, revenue generation, and customer experience.

Commercial edge computing products — including hardware solutions featuring embedded analytic and AI technologies — are just now starting to become available or will be soon. Despite these hindrances, already we are seeing examples of companies developing edge computing applications.

End-to-end strategy realization requires many people to work together seamlessly across five stages; this includes teams centered on strategy, customer experience, architecture, product management portfolio management, program and project planning, business analysis, business process, organizational design, and execution. Business architecture is a relatively new addition to the ecosystem of strategy realization, but has a valuable role in all five stages.

Value is the main interest in big data. Extraction of value from big data transcends both analytics and technologies. Value is highly dependent on the “context” in which data is analyzed and used. The Big Data Framework for Agile Business (BDFAB) is my comprehen­sive approach to big data adoption, which includes (among others) a module on business process modeling (BPM) and business analysis (BA). This module considers modeling and optimization of business processes as highly relevant and crucial in deriving value from big data. 

The “one size fits all” era, where RDBMSs were used in nearly any data and processing context, seems to have come to an end.

Agility does not come easily; it is more a question of culture and values than a question of using specific methods and tools. Using a self-assessment tool, this Executive Update describes how organizations can measure their agility.

How does a utility company connect better with its customers? Utility companies must consider strategic investments in all available tools — including soft­ware dedicated to the purpose — to increase customer engagement in and control of their products and ser­vices.