Business Transformation Requires Transformational Leaders

This Advisor reflects on the technology evolution of data warehouses and data lakes as well as the evolution of computer-assisted decision making, including data exploration, analytics, and decision support. Arguably, the data warehouse still serves an important purpose and will continue to have a significant place in the IT infrastructure. It is important, how­ever, to consider new technologies to support the need for real-time data, dynamic data warehouses, self-service business analytics, and algorithmic decision making.

During this on-demand webinar, you'll explore how to overcome the barriers to innovation using a Breakthrough Incubator model. You'll learn how to use a breakthrough incubator—an external partner that manages networks of collaborators—to accelerate the creation of new business propositions, and to overcome the barriers to innovating radical, non-core products and/or services at your organization. (Not a member? For a limited time, guests can watch the recorded webinar here.) 

Gone are the days that an organization could plan for sustainable competitive advantage and build a five-year (or even three-year) strategic plan. The business environment has become ever-more chaotic, dynamic, and disruptive. Enter agility, as the new capability to develop transient competitive advantage with shorter planning and execution cycles. Welcome to the age of “agilification.” In this Advisor, the authors touch on the important interplay among leadership, culture, business architecture, and digital architecture.

The simple reason we cannot see (or perhaps refuse to see) a paradigm shift upon us is because we tend to look at the world through the old paradigm. So perhaps all we need to do to meet tomorrow’s problems is to stop using yesterday’s thinking. This Executive Update is a call for the acceptance of complexity and the introduction of interdisciplinary thinking to all aspects of life, starting with software engineering as the guinea pig. By seeking to understand complexity instead of hiding it, we can build better-quality software with less stress.

As companies adopt Agile as their standard for software development, they usually encounter resistance from several directions — from other parts of IT as well as from the business. We often see organizations struggling with cultural change, insufficient business involvement, and other aspects of scaling. To overcome these challenges, some organizations use ways that worked for them in the past, but in an Agile context this results in counterproductive outcomes. These anti-patterns are hard to root out and tend to reappear. In this Advisor, we highlight two of the most common Agile anti-patterns.

Data governance, a broad and very expansive subject, potentially applies every time there is a transition between data states (i.e., contextual, temporal, or geographic) or when data is accessed. Currently, adoption of data governance policies tends to be passive and non-active rather than overt or proactive. In other words, data governance is the default, out-of-the-box governance that requires no action. But today we have the opportunity to create more individualized overt and proactive data governance policies that meet the specific needs and requirements of a corporation. Indeed, the issues of responsibility and liability for keeping data while it is at rest or in motion are beginning to dominate many conversations about data.

Here, in Part VII, we continue examining industries and domains where organizations see AI having its most significant impact.

Participate in our study on how organizations are adopting or planning to adopt CX management practices and technologies and what they see as the possible impacts on their businesses. In particular, we want to determine the actual current status of CX efforts within organizations and what their plans are for utilizing CX in the future.