Data Exploration – Producing knowledge goes through stages, the first of which begins with the existence and availability of specific data and then a processing process to convert information. The frequent availability of data, in turn, leads to knowledge.

Because of the significant expansion of the world of communications, which is the first feature of the present era, the means of information have flowed in all forms, vessels.

And languages make it a problem for information institutions to control these data and process them to access the required information accurately.

As traditional database systems are no longer sufficient to extract information accurately to suit the needs of their beneficiaries, especially after the era of data repositories had begun, there was an urgent need to draw information from the quantity.

The vast amount of data, in other words, is the process of processing the data. And extracting the information needed at the same time. Which is known as data mining.

The Concept of Data Exploration

Data exploration is the process of integrating traditional methods of data analysis with complex algorithms to extract value. And accurate information from a vast amount of unsafe data to be used later in predicting a future event.

It is also known as a sifting process. It analyzes a large amount of data to extract what is meaningful and discover new knowledge to describe past trends and predict the future.

Data Exploration - Introduction, The Concept and More

Data Types

Raw data without any examination or analysis, known to be Data data.

They analyze and extract from some simple information called information.
Information extracts in more complex ways, particularly taking advantage of the analyst’s experience to gain knowledge.

Data mining emerged in the late 1980s. It has proved to be an effective solution for analyzing large amounts of data by converting it from accumulated and incomprehensible information (data) to valuable information that can then invests and utilized.

The data exploration phase has attracted a lot of attention in research circles over the past decade to develop scalable algorithms. And adapt to increasing amounts of data searching for meaningful cognitive patterns.

The data exploration process is, therefore:

It is an activity that extracts information in large amounts of data stored in databases. And large data repositories to detect meaningful links, patterns. And trends by sorting large amounts of data stored in warehouses. Using pattern recognition techniques as well as statistical and mathematical methods. Also known as knowledge exploration in databases.

One step in discovering knowledge within databases is to use analytical methods such as neural networks, genetic algorithms, decision networks, technology. And hybrid models, to identify patterns and relationships in data sets.

A technical process that uses algorithms to analyze data from multiple perspectives and extract meaningful patterns can predict future user behavior. For example, the market basket analysis system it uses – recommends new products to its customers based on their previous purchases. A living example of how we can use data mining in marketing.

Benefits of Data Exploration

Data exploration and previous definitions are essential for completing tasks related to the creation and extraction of specific information from large amounts of data in general. Still, the most crucial benefits of that process are:

This process enables its beneficiaries to access information that other means could not access.
Decision-makers were able to find an inference pattern by understanding the past to predict the future of the required issue.

This process enables beneficiaries to identify and discover the links, trends, and patterns prevailing in the work of a particular organization.

Stages of data Exploration

The process of data exploration goes through steps:

Understanding the nature of the organization’s work: the first stage is understanding the work of the parent institution. Which determines the type of requirement.

Understanding the data: at this stage, knowing the kind and trajectory of the stored data completon.

Data preparation: at this stage, organizing data is done and ensuring that remains integrated. Free of data redundancy, and without some information.

Data exploration: at this stage, data analysis stays carried out to reach the desired result
evaluation: at this stage. Evaluating the results of the exploration process people carry out.

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