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
Nonfunctional requirements is an overly simplistic view of hyperliminal coupling that arises from transposing the idea of functional requirements and contract certainty to the hyperliminal reality of a system’s behavior in its environment. If we let go of the idea of nonfunctional requirements and focus instead on hyperliminal coupling, we have a greater chance of actually building something that can respond to its environment, as is explored in this Advisor.
The application of advanced technologies, including artificial intelligence (AI), are increasingly being used to detect wildfires before they grow out of control, predict a wildfire's path, and assess the risk and potential damage caused by wildfires. This Advisor explores the application of AI and machine learning technologies to mitigate the impact of wildfires.
As the pandemic unwinds, what lasting lessons can companies carry from the moments of “What now?” to successfully respond to “What’s next?” What makes the journey toward AI ethics so difficult? Get answers to these questions and more in this issue of The Cutter Edge.
Take a minute and write an answer to the question, “What is technical debt?” Then read this Advisor and reread your answer — and see if it still makes sense.
When contemplating transformation, you should test your assumptions in a pilot project before betting the farm on them. Such a project should be chosen to have minimal impact on the company in case of failure, should be realistic to build with a small team, and should have high potential for growth.
A common denominator behind failed ML projects is the anthropomorphism of technology. This Advisor explores how, through our tendency toward anthropomorphism, we fail to make a critical distinction in the way humans and machines interpret the world.
Today’s decision support tools are useful but limited — and running algorithms can be computationally expensive. We need statistical shortcuts that simplify incredibly complex equations so that they can run on today’s hardware. This Executive Update highlights the “quantum leap” and how it will end estimating and solve equations in their full complexity. Instead of waiting 24 hours, the solution might only take a second. Within the next 10-20 years, the quantum computing era can lead us to a fully intelligent enterprise.
Myles Suer and John Wills address how to ready data for analysis and competitive advantage. They compare and contrast how legacy companies differ from startup enterprises in terms of data history, governance processes, and management experience. They explore in great depth the importance of properly cataloging data for eventual strategic use. An important lasting lesson of their article is the importance of people in maximizing the business results from any data strategy and technology investment.