DATA ANALYTICS

What is Data Analytics?

Analytics is the process of discovering, interpreting, and communicating significant patterns in data. Quite simply, analytics helps us see insights and meaningful data that we might not otherwise detect.

Therefore, Data analytics is the science of analyzing raw data 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.

Why Is Data Analytics Important?

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. 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.

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. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click. Thus, Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data. A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new—and better—products and services.

Who Is Using Data Analytics? 

Data analytics has been adopted by several sectors, such as the travel and hospitality industry, where turnarounds can be quick. This industry can collect customer data and figure out where the problems, if any, lie and how to fix them. Healthcare is another sector that combines the use of high volumes of structured and unstructured data and data analytics can help in making quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers.

What are the types of Data Analytics? 

Data analytics is broken down into four basic types. 

1. Descriptive analytics: This describes what has happened over a given period of time 

2. Diagnostic analytics: This focuses more on why something happened. This involves more diverse        data inputs and a bit of hypothesizing. 

3. Predictive analytics: This moves to what is likely going to happen in the near term. 

4. Prescriptive analytics: This suggests a course of action. 

What is the process involved in Data Analysis?

References : https://www.mastersindatascience.org/learning/what-is-data-analytics/, https://www.g2.com/articles/what-is-data-analytics
Images : Google (www.g2.com, http://stepedge.in/must-have-features-for-big-data-analytics-tools-5442)

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