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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If we’re going to discuss disruptive forces in enterprise architecture (EA), we ought to ask if EA itself is at risk of disruption. Is there a customer base that currently does not value EA but could benefit from a simpler version of it? A truly effective enterprise architecture should be something that senior executives in an enterprise use to manage their business on a day-to-day basis, that guides the implementation of strategy, and that helps them in communicating and implementing change.
Whether it is data warehouses or marts, data lakes, or reservoirs, the IT industry has a penchant for metaphor. The subliminal images conjured in the human mind by the above terms are, in my opinion, of critical importance in guiding thinking about the fundamental meanings and architectures of these constructs. Thus, a data warehouse is a large, cavernous, but well-organized location for gathering and storing data prior to its final use and a place where consumers are less than welcome for fear of being knocked down by a forklift truck. A data mart, on the other hand, creates an image of something between your friendly corner store and Walmart.
The current market for IoT implementation platforms is still very much developing. Consequently, IoT cloud products and services are evolving steadily and will continue to do so for the foreseeable future as the market for connected platforms and services accelerates over the next few years in response to the demand from end-user organizations for practical IoT implementation and data management and analysis tools. That said, a considerable number of products are available from a range of vendors, and here, Curt Hall surveys them.
The Internet of Things (IoT) is driving demand for cloud-based platforms designed for building and managing connected solutions and for storing and analyzing the data they generate. This Executive Report examines the available products for implementing IoT applications and services, including cloud-based IoT infrastructure platforms, IoT infrastructure services, cloud-based IoT data management and analysis platforms and services, and industry/domain-specific IoT solutions and commercial applications.
This Executive Update discusses the strategies and objectives of automating the SDLC processes through IT tools, the execution methodology and framework, key execution challenges, and the benefits derived. We use a real-life case study at a large stock exchange based in the Asia-Pacific region to describe a pragmatic and holistic approach for automation that resulted in substantial ROI.
The enterprise players' IoT platforms are, for the most part, comprehensive IoT implementation environments providing platform-as-a-service (PaaS) capabilities. However, in addition to supporting the infrastructure requirements necessary for building, connecting, and managing IoT connected products and applications, they are also designed to integrate with, and take advantage of, the back-end, infrastructure, communications, process management/workflow, and analytics capabilities provided by the various business components of their respective vendors' ERP, CRM, BI, cloud, industrial control, and other enterprise software offerings.
Social business analytics is the most complex form of social media analysis because it involves analyzing unstructured social data in combination with structured data and other content maintained in enterprise sources. This requires an infrastructure for sourcing, managing, and analyzing social and enterprise data, and for integrating the findings back into the organization's enterprise data analysis and decision-support processes.
This case study explores how Cutter's team, headed by Lynn Winterboer, helped an $18b organization deliver reliable and timely reporting with Agile Analytics.