Strategic advice to leverage new technologies

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.

Applying the principles of “loosely coupled” to master data and containing fragmentation within a framework that governs the collaboration process will lead to the design patterns of solutions that fit the collaboration process. This is designed fragmentation, or “connected architecture.” This framework — a thought process more than a recipe — is described in this Advisor.

Today, analytics and computerized decision support can help managers make better choices in semistructured and even unstructured situations. Managers should be curious and seek evidence and answers from data for previously unasked and/or unanswered questions. Indeed, as this Advisor explains, they should become data explorers and sophisticated decision support users.

In this Advisor, the author describes the conflicting demands on today’s CDO, who must cover “operational data management” as well as “data management for analytical insight.” He shows the importance of caring for the quality of the data, understanding its provenance and pedigree, minimizing the transformations, and adding semantic understanding of the data.

Software architecture requires balance. Often, you can focus too much on it, creating robust products that miss customer needs or over-engineer solutions. Conversely, especially in Agile contexts, you can under-engineer things and your product efforts can succumb to relent­less refactoring rework. So there’s a balance to strike in architecture, no matter what methodology you use to create your software. In Agile contexts, that balance is often lost. And it usually leans to less over more. In this Advisor, I describe a rule that has helped me successfully strike the right balance between Agile and architecture: chaos is constant, so continuously refactor.

Artificial intelligence has many applications in supply chain and logistics. These range from automating the analysis and reporting on global supply chain activities and optimizing supply chain planning and execution to predictive applications for scheduling, strategic sourcing, and remote monitoring and predictive maintenance. This Advisor describes the major drivers for applying AI in the supply chain, including demand for greater visibility and transparency into supply chain data, processes, and execution, and a need to reduce risk and satisfy customer demands.

Line-of-business (LOB) applications are at the center of most large enterprises. These are the applications we don’t think about and we don’t appreciate, but they’re the glue that keeps an organization functioning. They are the boring applications that drive internal processes, but the more LOB apps an enterprise has, the more technology it employs and the more productive it is.

Being hybrid is not new. Not at all. If we look around, we can see the entrenched old and the emerging new sitting side by side in our enterprises. And, in important instances, we have actually thought through, at some level, how the old and the new are tied together, thought of together, and managed together. If we do it right, we can build hybrids that allow us to have one foot in the present and one foot in the future, as we make our way across the skybridge to the new buildings of the future.