Few challenges being faced in translating the lessons of predicting analytics from other verticals in manufacturing. The objective of this predictive analytics is to get the correct business decisions and it will impact the design and service of the product. The data is being updated continuously through their supply chain. The predictive models are used to connect the real world data to digital twin models of the virtual world. This helps in better understanding and working of their business plus with the on the factory work. Predictive analytics help to find the issues related with the product quality, performance and its features. These helps in better designing the product features and make it to optimum use of it. The predictive model is quite accurate in giving information about the risk failure, improving the machines to put in a better use as well as it gives the best correlation between job characteristics and job failure. Models are being trained through environmental data and IoT data and few factors which affect such data too such as environmental hazards, weather and many more. Its benefit for the business to take predictive analytics into consideration. https://www.ngdata.com/ways-to-improve-customer-experience/

Business analytics and business intelligence are like two sides of a coin. But there is a difference between them. Business analytics is like an umbrella term and intelligence is a part of it together with other aspects of business applications. Once a person runs a business, that person will be able to understand the difference between them, that is, business intelligence is like accessing to all kinds of business related data and software's and put them into the analysis. Business analytics is something using your business intelligence into your data and optimizing the performance of the business. To have a successful business, it is important to follow both. Business intelligence is generally used to look over the previous data, whereas business analytics look to the future needs of the business. It's necessary for a businessman to understand its difference for making any business decisions or predicting for the future. Read more at: http://www.analyticbridge.com/profiles/blogs/business-analytics-and-intelligence-compared

Very often a customer 360 view can be dangerous and distracting as it sets the organization of the track by providing it a false goal to pursue and diverts it from pursuing financially rewarding initiatives. As a consequence, business acquires a constant monitoring stage with their data and analytics investment. Customer 360 view data is not actionable until you don't apply analytics and you can't apply analytics until you know the business problem organization is wanting to address. A more active approach would require focus on identifying the decisions that an organization is trying to make about customers and validate, justify and prioritize those decisions. Read more at : http://www.datasciencecentral.com/profiles/blogs/the-danger-of-pursuing-customer-360-view