Business Transformation Requires Transformational Leaders

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.

Working with blockchain over the past few years has made me realize the broad possibilities of distributed networks. Certainly, one could say that blockchain is “just” a distributed database; however, emerging adoption reinforces the potential underneath. There are myriad ways to adopt this technology, including in transferring funds, managing supply chains, handling tax evasion, and performing targeted analyses of data already recorded on the blockchain.

A capability architecture defines what we are and do at any given time, whereas process, information, platform, and other architectures describe how we accomplish what we are or perform what we do at any given time. As we improve or mature, what we do and/or how we do it changes over time, meaning that both our capabilities and the enabling architectures evolve.