Data analytics is the science of analyzing raw data in order to make conclusions about that information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.
Understanding Data Analytics
Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan the workloads so the machines operate closer to peak capacity. Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Continue reading from Investopedia
When you think of big data, you usually think of applications related to banking, healthcare analytics, or manufacturing. After all, these are some pretty massive industries with many examples of big data analytics, and the rise of business intelligence software is answering what data management needs. However, the usage of data analytics isn’t limited to only these fields. While data science is a relatively new field, more and more industries are jumping on the data gold rush. The best examples of big data can be found both in the public and private sectors. From targeted advertising, education, and already mentioned massive industries (healthcare, manufacturing, or banking) to real-life scenarios in guest service or entertainment. Continue reading from DataPine