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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When designing products and services for the Internet of Things (IoT), organizations should make defining procedures for ensuring customer privacy an initial priority to avoid embarrassing and potentially costly surprises down the road.
This Executive Update explores the impact of the IoT on traditional business and technology architectures and the role of EA as an effective methodology for developing and implementing IoT strategies. We examine business architecture and how it integrates IoT-driven processes with traditional processes.
Architects are moving from single frameworks to instead work with a set of frameworks -- each finely tuned to serve a particular purpose. This new report by Roger Evernden explores a simple and pragmatic approach to developing a set of architectural frameworks that can evolve and be used as a constant guide to direct and manage EA. (This report is complimentary for Cutter Members. To purchase, please click the button below.)
Successful IoT use cases can emerge only by moving away from a product-centric approach that focuses on one-time sales and toward enabling an ecosystem of collaborating devices and services working in concert to build a longer-term, value-based customer relationship.
Traditional Agile does not consider enterprise architecture as a key part of the process but assumes that architecture guidance is being provided in the background. Traditional enterprise architecture (EA), however, has also failed to evolve and the majority of EA teams are under pressure due to the increased adoption of Agile within enterprises. Thus, the traditional role of EA has been under attack by the emergence of Agile within enterprises and its adoption beyond the IT domain.
In this Executive Update, Part I of a two-part series, we examine cloud-based data warehousing and platform as a service (PaaS) offerings designed to support data management for analytical applications. Part II will cover the trends, developments, and considerations pertaining to the adoption of self-service business intelligence (BI) environments.
In practice and training, we should use EA methodologies as a way to assist human thinking, not as a mechanical, technology-driven form of “box-checking.” Above all, we should understand that we create EA models so that we can use them as a basis for human analysis. The models are the beginning, not the goal.
Advisor
Lessons in IoT Data Management
All that has been said and written about the challenges associated with the Internet of Things (IoT) does not quite prepare you for the practical difficulties that crop up as you start implementing and deploying IoT solutions.