Everything we see around us, if not designed by nature, has been designed and manufactured by us. Manufactured items are conceptualized using techniques of sketching and brainstorming. Once fully developed, the concept is converted into CAD models or virtual design model. This CAD models are very good way to validate complex ideas. They capture form, fit, and function of the product and give confidence that the idea is going to work. The information that is stored in the CAD model is eventually converted into engineering drawings, then handed off to manufacturing department. There, the CAD information is used to make a physical product. Manufactured physical products are then used by the customers. A disconnect currently exist between the digital model and the physical production. Most of the information created at the digital CAD model is lost during the physical fabrication phase. Once the physical product is in use, rarely the user's information comes back to make changes to improve the digital design phase. Things are changing. We're in phase when the integration of digital and physical worlds have began. This is a very exciting time. Their connections are being enabled by placing sensors on products and machine and connecting them to Internet and analyzing the resulting flow of data. This convergence of sport machine, machines with sensors, Internet, and data is referred to as Internet of Things or IoTs. Sensors on IoT platform generate data. Expressed in different way, the IoT sensors leave Digital Threads. Analytics is also referred to as advanced analysis, and is the computational process that stitches together the Digital Threads created by Internet of Things to generate actionable intelligence. The anti-braking system in cars give us a good illustration of how data can inform a system to respond in a positive way to improve system performance. Both the front wheels and the back wheels of the cars are equipped with speed sensors. These sensors continuously provide speed data for the wheels. Analytics in this setting would be looking at the continuous stream of data to infer when the back wheel speed is different from the front wheel speed to recognize a slip or skid condition. The recognition of slip condition would prompt the activation of the ABS system to prevent skid. In manufacturing, the data generated from machines, once it is analyzed, can improve the efficiency of making things. Consider this simple analogy. When manufacturing a product, three steps occur: raw materials are collected, processed, and transformed into a physical product. The transformation renders something physical we can use. When advanced analysis is performed, raw data is collected, processed, and transformed into information. The information renders an informed decision or action we can take. Analytics connects the digital and physical worlds and test links the product design phase to manufacturing product use and end of life phases. The exciting benefits of analytics include better designs, better machines, and enhanced manufactured. In closing, a great example of the use of advanced analysis is predictive maintenance. Predictions based on analytics can tell us when a machine needs maintenance. This is known as predictive maintenance, and plays a major role in improved asset utilization.