Business Transformation Requires Transformational Leaders
Leadership and teaming skills are front and center in times of rapid change. Meet today’s constant disruption head on with expert guidance in leadership, business strategy, transformation, and innovation. Whether the disruption du jour is a digitally-driven upending of traditional business models, the pandemic-driven end to business as usual, or the change-driven challenge of staffing that meets your transformation plans—you’ll be prepared with cutting edge techniques and expert knowledge that enable strategic leadership.
Recently Published
How should project teams corral the natural but emotional forces exerting pressure on project decision making and processes?
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.
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 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.
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.
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.
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.