We’d be better served by focusing on human’s irrational thought patterns than fearing some AI-enabled bogeyman. Doing so would shed light on our illogical biases and thought patterns that can intrude into AI algorithms. It would also help understand how AI can overcome our inherent intellectual handicaps and boost productivity
With Big Data increasing and the time-consuming task of data preparation, the industry is looking for better ways to improve efficiency and speed up the change of data into meaningful information. With that said, when analyzing the data journey and the constituents data serves, metadata is the common recurring theme that enables an organization that replaces “data wrangling” with data discovery. Metadata management must be a core competency, a place of innovation and of strategic importance. Data regulations and sensitive data will need to be managed, and it all starts at a metadata level in order leverage, yet protect those it intends to serve.
The field service component, distribution, channel, aftermarket service/repair, and integrator are the glue to the entire IoT value chain. The opportunity service organizations have to leverage IoT to transform their business is incredible. It is a tool that will allow field workers to spot issues continuously as an expanded “remote monitoring” opportunity, maybe as a SaaS model. This could be a new revenue stream, but really a chance to see more issues with continual data streaming in instead of quarterly route-based maintenance. The value lies in domain specific expertise and vertical specialization, but delivery is the key.
The IIoT is the digitization of industrial assets and processes that connects products, machines, services, locations/sites to workers, managers, suppliers, and partners. The IIoT creates a universe of sensors that enables an accelerated deep learning of existing operations. These data tools allow for rapid contextualization, automatic pattern, and trend detection. Furthering this for manufacturing operations will finally allow for true quantitative capture of formerly “expert” qualitative operations. The question is whether or not we can leverage analysis on a continual basis to have continuous machine health monitoring and preempt catastrophic failure. This is what is known in IIoT as predictive analytics.
Data clustering is the classification of data objects into different groups (clusters) such that data objects in one group are similar together and dissimilar from another group. Many of the real world data clustering problems arising in data mining applications are pair-wise heterogeneous in nature. Clustering problems of these kinds have two data types that need to be clustered together. In an industrial setting, despite collecting data from tens of thousands of sensors, less than 1% is actually utilized. We can move rapidly into Industry 4.0 by combining subject matter expertise, data collection methods and next-generation data science tools, beyond many of the "me too" products.
People often see the Industrial IoT (IIoT) in a narrow view, namely the ability to increase efficiency, productivity, and cost savings. While that’s true, it’s a limited list of benefits based on one’s understanding of connectivity, data, and information to influence behavior. There are several ways to lead organizational change, and it all depends on one’s role and how they view their role in the organization. With IIoT sensors and analytics, continuous condition monitoring is available in a cost-effective manner to have information flowing in real-time to further enhance productivity, efficiency, health, and safety.