Machine Learning Use cases in Data Management
Today, there are an incredible number of challenges around the world across the industry domain that can be solved by providing the right training data – sample — to the right machine learning algorithms. Great thanks to the latest developments in Machine Learning algorithms. If we look at the basics of machine learning, the perspective of handling data is a way different to computers when compared to humans. The process is fast, accurate and flexible with computers. Data management is not a separate industry sector; it’s an integral part of each and every organization. And need to be handled with high priority. Machine learning is continuously evolving over a period of time which enables it to handle the data to get the best use of it across the industries. Sometimes data management becomes more important than algorithms to drive the solutions. It is said in Forbes publication, enterprise investments in machine learning will nearly double over the next three years, reaching 64% adoption by 2020. We can picture the use of ML in various fields of data management, a resource that enhances the business benefits in several industries. Sorting through dark data: To sort and handle different types of emails, documents and images stored on different servers, machine learning, and its combined algorithm power will be helpful. Deciding which data to cutoff: AI, machine learning, and analytics can systematically identify the seldom used data and indicates that data is obsolete. Which can take the maximum time of employers. Aggregation of data: Sometimes there is a need for aggregation of data for queries, and it needs integration to access the data from different sources. But using machine learning, it makes the process so efficient by automatic mapping between the sources and data repository application. Organized data storage system for best access: There are different kinds of data such as most used, seldom and never used data. IT departments use “smart” storage engines which use machine learning algorithms to classify different types of data. This eliminates the concept of manual address storage optimization.