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
In this on-demand webinar, featuring Cutter Consortium Senior Consultant Philip O'Reilly and 4 of his colleagues, you'll discover how data harmonization — via the marriage of open industry standards and semantic database technologies — results in improved outcomes in data quality, analytics, risk management and governance.
A strategic approach around big data not only includes the multiple analytical, architectural, project, and technical elements in a synergistic manner, but also pays due attention to the financial and people aspects, resulting in business value.
Artificial intelligence (AI) in all its various forms — machine learning (ML), natural language processing (NLP), speech recognition, cognitive systems, intelligent agents, chatbots, and robotics — is generating intense interest across every industry — from consumer electronics, banking, and finance to automotive, insurance, healthcare, government, aerospace, big pharma, retail, and manufacturing.
Even radical approaches to change management, such as business process engineering or Lean, assume there is a desirable endpoint (however temporary) toward which change is directed. However, what if change happens so continuously that no fixed endpoint exists for any initiative, but instead organizations must constantly change, not just to succeed, but even to survive?
Governments are starting to embrace the possibility of creating their own cryptocurrencies.
If electric cars and trucks are the vehicles of the future, then that already suggests a massive change in the industrial infrastructure — new modes of manufacturing, new modes of refueling and repair, and, perhaps, new roads. Given the key role of autos in society, such a transition will be a major story. However, it is really only a small part of the changes facing the automobile industry. As we explore in this Executive Update, other technological changes in the auto industry include: self-driving cars, intelligent auto production processes, and cars as entertainment centers.
There is an ingrained optimistic spirit infusing the enterprise, creating a bias toward action, toward change, toward better things. And, a resulting need for speed: speed to market, shorter cycles, quicker turnaround, and more throughput. Faster, faster, faster! More Agile. But speed kills. Even avid practitioners of Agile — if we pay attention to some of the current conversations — are generally of the view that Agile is not merely about speed, and that breakneck speed can break necks and more, if “Agile” is simply an excuse to hurtle mindlessly into space.
This Advisor discusses the risks and challenges of implementing prescriptive analytics in the context of machine learning.