Strategic advice & alerts to leverage data analytics & new technologies
Leverage data and the technologies that generate it, from IoT to AI/machine learning, wearables, blockchain, and more, to improve decision-making, enrich collaboration and enable new services.
Value is the main interest in big data. Extraction of value from big data transcends both analytics and technologies. Value is highly dependent on the “context” in which data is analyzed and used. The Big Data Framework for Agile Business (BDFAB) is my comprehensive approach to big data adoption, which includes (among others) a module on business process modeling (BPM) and business analysis (BA). This module considers modeling and optimization of business processes as highly relevant and crucial in deriving value from big data.
The “one size fits all” era, where RDBMSs were used in nearly any data and processing context, seems to have come to an end.
Banking and financial services companies were among the first to apply artificial intelligence (AI) in strategic applications. Initially, this took place in the mid- to late 1980s in the form of expert and knowledge-based systems for credit and loan approval and mortgage processing, and so on, and then in the early to mid-1990s, when neural network-based applications for credit and bank card fraud detection, and profitability management, began to be deployed.
Cutter Consortium is conducting a series of surveys on how organizations are adopting, or planning to adopt, artificial intelligence (AI) technologies. We also seek to identify important issues and other considerations they are encountering or foresee encountering in their efforts. Here in Part V, we look at findings pertaining to the industries and domains where organizations see AI having its most significant impact.
In this on-demand webinar, Cutter Consortium Senior Consultant Frank Contrepois shares advice, forged from his experiences with AWS, on how you can avoid wasting money on cloud services by keeping an eye on — and acting upon — three things.
Analytics can be performed at various points in the deployment of a solution. Certainly, there are situations where “localized” analytics may be more appropriate than analytics performed in the cloud, and still others where analytics on the organization’s network might be more appropriate. The location of analytics can also determine where and when to integrate data into the analytical solution.
In this Advisor, I describe some important AI developments we are seeing with the IoT.
In this Advisor, we examine how fog architecture addresses cybersecurity for next-generation networks.