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.
Steven Woodward explores in depth the issues of data residency, using the term “geo-jurisdictions” to describe the intersection of geographical and legal boundaries that place constraints on the handling of data. Woodward begins by alerting organizations that falsely believe that data residency is not a concern for them. From this warning, the author moves on to concrete recommendations about policies that should be put in place for various service and deployment models as well as the need for a thorough geo-jurisdiction analysis.
While issues around data and information governance are starting to get the attention they deserve, business and technology leaders still need help finding their way through all the conflicting demands. We invited several authors to present their perspectives and recommendations on this complex web of issues.
Fog computing can create a digital twin of difficult-to-replicate process. Let me explain, through a hypothetical example of a craft brewery. Unlike industrial production, food and beverage manufacturers work with natural ingredients, where the quality of raw materials can vary, making it more difficult to create a uniform product without waste. Producing a consistent quality product is critical to building customer loyalty. When it comes to products of nature, in particular, manufacturers face unique challenges.
Over the last 12-16 months, we have seen more than a dozen banks and financial institutions worldwide introduce (or announce) virtual banking assistants and bots. Such apps are now available to millions of personal banking customers, and banks are also starting to introduce AI-powered digital assistants for managing corporate customers’ accounts. This Advisor explores some examples of intelligent virtual assistants and bankbots companies have introduced over the past year or so.
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.