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.
Recently Published
Vince Kellen sheds light on the significant impact of big data in his domain of expertise: education. He then examines how universities can use big data to improve teaching and learning and describes the challenges involved. He concludes by offering suggestions for strategy development as universities incrementally apply big data to their core enterprise, education.
Santhosh Ravindran and Fiona Nah explore utilizing prescriptive analytics to enhance business processes, focusing on ML algorithms. While clarifying how “the foresight offered by prescriptive analytics enables organizations to make major decisions in a short time frame with greater accuracy,” Ravindran and Nah rightfully direct our attention on the importance of embedding such analytics carefully and iteratively within business processes.
The authors offer some insightful thoughts on how machine learning can help extract value from big data. The authors begin their discussion by “illustrating the limitations of current methods and human intellect across the 4 Vs (volume, velocity, variety, and veracity)” and the barriers that can block the extraction of the 5th V (value). Their article further highlights some excellent ML use cases at cutting-edge companies.
This issue of Cutter Business Technology Journal explores various angles to big data with a focus on the trends in predictive analytics, ML, IoT, and the cloud.
The adoption of business architecture has continued to increase globally — and at a faster pace than ever — demonstrating that the discipline is here to stay. This Advisor provides a brief reflection on the state of business architecture to date and a glimpse into the future.
Governments worldwide are seeking to apply blockchain to a range of applications and domains to streamline and secure information systems and to better serve their citizens.
In the heat of practicing architecture within organizations, it is easy to lose sight of the fact that “architecture” is not a real thing. It is a paradigm: a metaphor transplanted from other fields in which the idea of architecture was firmly and usefully established.
As with other data-intensive technologies, collecting and analyzing data from Internet of Things (IoT) deployments requires sound data management strategies and tactics. Collecting and analyzing this data can be generalized to include three distinct processes: (1) data collection and storage, (2) data integration, and (3) data analysis (see Figure 1). In this Advisor, we examine each of these processes.