Business Organization And Management By Mc Shukla Pdf 32
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Because of the capability of machine learning-based decision-making, it provides an immediacy for data scientists to respond to both the business and customer grievances immediately. In fact, [13] also discussed the importance of the immediate responses. Machine learning-based business intelligence systems can seamlessly adapt to the new business environment without re-writing recent analytical programs, and it is much easier to develop new predictive algorithms for data miners.
Secondly, high-quality action can be taken from predictive analytics and prescriptive analytics, as all the knowledge and experience gained from the predictions will be used for an efficient business operation. Machine learning can help educators and trainers provide more personalised learning to learners in education and training. In addition, advanced analytics is achieved by connecting more diverse business systems, such as business process automation, building information systems, and enterprise resource planning. Machine learning based data mining approaches can efficiently manage the business processes by creating a better learning pattern to a specific business environment and provide a selective combination of best practices to be improved rather than a uniform set of best practices to be used across all the businesses.
The final key machine learning technology for analytical agility is data interoperability. Businesses can use heterogeneous and distributed data sources to be localized within an individual business unit or considered as a whole. Typically, data integration and interoperability cannot be achieved without data mining techniques, as machine learning techniques require distributed data sources to be aligned. The main problems in data interoperability are not only transforming the different data schemas into common schemas, but also the techniques that can convert data into stand-alone data sources that can later combine new data with the existing data without being constrained by any constraints and limitations. Incorporating new data into an existing environment is a vital step for further data mining. Currently, however, data interoperability has been mostly achieved by normalization-based techniques, but this is not a good way to combine the new data into the existing data. Machine learning-based data mining approaches can effectively combine new and old data for decision-making across many business units and operate in the same time. d2c66b5586
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