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
“Know your customer” (KYC), a key process for banks today, remains, in most cases, a very costly and long process. Most challenges lie in the efficiency of verifying customer-provided information. With digitally verified claims, verification can be improved, accelerated, and replicated on a large scale. KYC and digitally verified claims open new business opportunities for banks to act as validators for other organizations. This Advisor explores two case studies of KYC implementation.
Here in Part XI of this Executive Update series, we look at how responding organizations view the success of their AI application development efforts to date, including whether they are deriving any benefits from their deployed AI applications and whether such applications are, in fact, actually changing how their organizations operate.
One of the techniques people in the Agile community argue for is retrospectives. A retrospective refers to a meeting held at the end of an iteration where teammates reflect on what they experienced and recommend improvements. It is an important tool because it allows the team to take advantage of their lessons learned. The bottom line is that organizations need to put processes in place to facilitate sharing. Besides being easy to use, developers need to be motivated to use these processes, or else the databases that are provided will remain unused.
Architecture stories are important to articulate so they gain visibility. For example, if you have a feature idea that you think a user would value, you might define a minimum viable product for it and whip up a quick/cheap prototype before making a final implementation decision. If the feedback isn’t positive, then you’d quickly pivot in another direction. The point is that I want the same level of thoughtful planning to occur for architecture as for features.
Is an Agile Firmware Approach Possible?
This Executive Update takes a closer look at being Agile when it comes to firmware. We begin with some tutorial information. Next, we discuss the firmware development process. Finally, we explore the issues typically encountered and identify ways some have taken to resolve them.
To meet increasingly elevated customer expectations, organizations are implementing detailed strategies for distributing and standardizing customer experience (CX) practices and technologies across their various lines of business. In this Advisor, we explore the five most significant challenges organizations face in implementing CX strategies and supporting technologies.
Thoughts on a Project-Volatility Metric, Part V: V6 and V7
Milestones are just an independent, simple, one-level hierarchy that stands side by side with our object list. That said, when milestone commitments (estimated end dates) change — or when teams deliver work ahead of, or lagging behind, the milestone estimated end date — these all become important changes and outcomes that we need to monitor. Monitoring these changes gives us insight into the nature of project scope changes and the interaction between our project management team and IT governance.
Today’s chief data officer not only needs to rethink the relationship between data producer and data consumer but must become intimately familiar with the new requirements for predictive modeling (to unravel scenarios and identify patterns), advanced query (to follow an idea into discovery), and data visualization (to understand interconnections) — the big three for data analytics.