There is a bounty of business use cases from which the business can choose in order to monetize their IOT efforts. The best approach is to build out your IOT Business Strategy with one use case at a time. In this manner, not only do you incrementally build out your IOT analytic, data, technology and architecture capabilities, but this enables the organization to build upon the work of previous use cases – to capture, share and refine the IOT data and analytic assets that are key drivers to IOT monetization.
Creating an IoT Monetization roadmap should be the top priority for any IoT initiative. Take the time to identify, validate and prioritize those use cases with the key business stakeholders and constituents to ensure that you are focused on the right use cases in the right order. There is no value in generating and collecting the data if you don’t have a plan for how to monetize that data.
2018 will continue to see the continuing march of economics that drive innovation and market adoption of Big Data, Data Science, Machine Learning and Artificial Intelligence. It’s a great time to be in the data and analytics business, and 2018 will just reinforce that!
What are the characteristics of organizations that will be the ultimate winners in this Great AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others fumble the future? Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. As the world prepares for the impending great AI war, now is not the time for organizations to be shy or to cling to old, outdated business models.
Analytic Modules are pre-built engines that can be assembled to create specific business and operational applications. They produce pre-defined analytic results or outcomes, while providing a layer of abstract that enables the orchestration and optimization of the underlying machine learning and deep learning frameworks. One example of an IoT analytic modules would be Anomaly Detection. Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. A number of different machine learning techniques can be used to help flag and assess the severity of detected anomalies.