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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Many technologies exist today that have the potential to change the manner in which we get work done. Currently, the software developer job is heavily labor-intensive. Yes, we use software tools to perform many of the repetitive tasks; however, for the most part, the programming job is performed by highly talented individuals who specify, design, code, and test complex pieces of code and make them work. We have attempted to automate such tasks, but we can best characterize current efforts as assistance (helping workers by providing guidance and information) rather than automation (replacing humans with machines). In this Executive Update, we identify 20 technologies that have the potential to alter this picture in both the near and long term.
This Advisor considers the development of a continuous deployment process for software by combining Agile methods and DevOps. After first looking at the salient characteristics of both techniques, we discuss why bringing together Agile and DevOps — and the resultant continuous delivery and deployment chain — is truly worthwhile.
The increasing importance of a coherent organizational strategy to maximize the exploitation of data for growing a digital business is clear. There is a need to locate, consolidate, classify, and access the necessary data — often data of many different formats stored in different areas of the enterprise as well as external data — to drive many different aspects of a successful digital business. To achieve these types of capabilities in an efficient and scalable manner, the digital business needs to be able to identify, extract, transform, contextualize, and distribute the necessary data. The data warehouse or data lake is a critical part of this data ecosystem, and many different aspects of the digital business can exploit this resource in a consistent way by means of a shared metadata framework.
Among the most important developments concerning AI and blockchain are the new, emerging business models, enabled by these technologies, that are beginning to appear in various industries. This is especially true when it comes to fintech, and the combining of blockchain and AI to create new financial platforms that can provide virtual banking services to people who currently are not served or who are underserved by more traditional banking services offers a good example.
As part of an ongoing series on artificial intelligence (AI) in the enterprise, in this Executive Update, we turn specifically to AI development platform preferences, including open source and commercial providers’ development tools and AI-as-a-service platforms.
Based on our client experience, a significant opportunity to improve the business value of both enterprise architecture (EA) and Agile lies in combining their practices. Several frameworks could help in this sense, and with the right vision in mind, companies can increase their ROI for both EA and Agile. In practice, we have seen several ways in which organizations combine EA with Agile thinking and methods to break through the anti-patterns and improve results. This Advisor highlights a useful example from a Netherlands central government client and comes down to the adoption of Agile architectural approaches described in SAFe 4.5.
About a decade ago, Cutter Consortium published my Executive Update series on the project-volatility metric, which examined the notion of project volatility and set forth the assumptions that underlie my own project-planning approach. The framework has grown tremendously. Thus, we are returning to this Update series with a revised Part II and a plethora of Updates to come.
Some of the more interesting developments with artificial intelligence (AI) involve its application in media and entertainment. There is a great deal of innovation underway to utilize AI in practically all aspects of media and entertainment — from content creation, procurement, categorization, and distribution to display, intellectual property (IP) protection, marketing, audience measurement, and customer service.