Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
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Customer expectations have never been higher, and they are forcing companies across almost every industry to rethink their approaches to commerce, marketing, sales, and, in particular, customer engagement, service, and support. The result is that companies are taking a great deal of interest in all things related to the experience customers encounter when dealing with their businesses.
Value-stream mapping is a useful tool that forces tech teams to focus on activities that add the most value for the customer, rather than those that are recommended in some textbook, or that employ a hot new technology a senior person happens to want to learn, or that use a development method that will look good when reporting to the CIO. However, too often advocates of this laudable technique have been operating on long-discredited ideas of which economic activities “add value” and which ones do not.
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.
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.