Raw data

Raw Data is a collection of unprocessed data that has not been processed or analyzed for any purpose. The data is commonly retrieved from sources such as sensors, surveys, customer interactions, or databases. It is usually transformed or processed to become useful information.

Raw data is used in a variety of tasks, such as data analysis, machine learning, and artificial intelligence. It is often collected from a broad range of sources, either through manual input or automated scanning methods. Raw data can also be used in data visualization and predictive analytics.

Raw data is generally composed of values that have not been transformed or organized. As such, it usually consists of a series of numbers, letters, or symbols that have not been manipulated. It can be hard to draw meaningful insights from this type of data, and it often requires some form of preprocessing.

Preprocessing raw data involves cleaning, formatting, and organizing it so that it can be more easily used. This typically includes cleaning up and organizing data fields, detecting and removing outliers or missing data points, and manipulating values. It may also involve reshaping raw data to make it easier to interpret.

Once raw data has been preprocessed it can be used to learn more about the field or to gain meaningful insights that can be used to make informed decisions. Common analytics techniques used to analyze raw data include descriptive analytics, exploratory data analysis, predictive analytics, and prescriptive analytics.

Raw data is an essential part of many aspects of computing, programming, and cybersecurity. It is often collected and analyzed to gain insights, identify patterns, and make predictions. It also plays an important role in machine learning and artificial intelligence systems.