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 can involve a variety of different tasks and techniques including data mining, text mining, predictive modeling, statistical analysis, and big data analytics.

1. Identification: Data analytics is the process of examining data sets in order to draw conclusions about the information they contain. This is often done with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions.

2. Description: Data analytics can be broken down into four basic types:

a. Descriptive analytics: This type of data analytics examines data to answer the question of "what happened?". It involves the use of historical data to understand changes that have occurred in a business.

b. Diagnostic analytics: This type of data analytics focuses more on the question of "why did it happen?". It involves more diverse data inputs and a bit of hypothesizing.

c. Predictive analytics: This type of data analytics tries to answer the question "what is likely to happen?". It uses statistical models and forecasts techniques to understand future behavior.

d. Prescriptive analytics: This type of data analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the prediction and showing the decision maker the implications of each decision option.

Data analytics can help organizations optimize their performances, automate complex processes, and improve their strategies by making data-driven decisions.

Question

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 can involve a variety of different tasks and techniques including data mining, text mining, predictive modeling, statistical analysis, and big data analytics.

1. Identification: Data analytics is the process of examining data sets in order to draw conclusions about the information they contain. This is often done with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions.

2. Description: Data analytics can be broken down into four basic types:

a. Descriptive analytics: This type of data analytics examines data to answer the question of "what happened?". It involves the use of historical data to understand changes that have occurred in a business.

b. Diagnostic analytics: This type of data analytics focuses more on the question of "why did it happen?". It involves more diverse data inputs and a bit of hypothesizing.

c. Predictive analytics: This type of data analytics tries to answer the question "what is likely to happen?". It uses statistical models and forecasts techniques to understand future behavior.

d. Prescriptive analytics: This type of data analytics goes beyond predicting future outcomes by also suggesting actions to benefit from the prediction and showing the decision maker the implications of each decision option.

Data analytics can help organizations optimize their performances, automate complex processes, and improve their strategies by making data-driven decisions.

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