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Data Analysis

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Data mining, which is also called knowledge discovery, is a term used to describe how information is gathered from large sets of data and organized into useful groups to discover patterns or relationships between the sets. Data analysis can uncover predictive information that isn't readily apparent to business owners or employees; patterns are often too intricate to discover merely through observation. The relationships or trends between data groups are uncovered via complex computer algorithms.

Types of Data and Data Analysis

Data can include numbers, facts, or text files. In most cases, it will contain a combination of the three. A simple example of a data set would be a list of a store's monthly retail sales. Data is often operational, such as payroll and expenses. Sometimes metadata--data about the data--is collected in order to study the database's design or definitions.

Data files have become so enormous that it's impossible to analyze them without special tools like data analysis software. The volume of data grows daily for most organizations. Human analysis is a difficult process because of the size of the files as well as the presence of multiple factors affecting the data.

Organizing Data: The First Step

Material is collected from databases and grouped according to class, cluster, association, or patterns. Raw data in itself is useless; it must be understood and grouped according to one of several methods. For example, retail sales might be broken down by item as well as by sales on a given day of the week, or even sub-categorized into sales at a certain hour of the day. Sales could be further grouped by characteristics of purchasers: male or female, young or old.

After creating, organizing, and studying the database, a hypothesis is created about the sample. A predictive model is constructed based on the sample data, then the model is tested repeatedly to try to prove or disprove the hypothesis. After testing many times, the results are tested on real customers to see if the model can be verified.

Analysts are interested in maximizing all possible correlations while eliminating any preconceived assumptions they may have. In this way, they can discover the relevance of their questions and all possible outcomes. A good analyst also ranks the factors in order according to their significance.

Benefits of Data Analysis

Data analysis enables companies to investigate the effect of various factors on their consumers and employees. It can be used to detect fraud and increase profit margins. Businesses use it to learn about markets, customers, communication, and competitors. Sales people use it to develop sales strategies to appeal to high-end customers or certain target markets. Manufacturers use it to improve processes and production. In the medical field, data analysis can help determine the effectiveness of medications or procedures.

The goal of data mining is to give the user new information that he can rely on to predict future patterns and relationships. With all the new technology that exists today, gathering data is easy, and storage is fairly simple. The method of analysis and interpretation of information is the key to its usefulness; the information is converted into knowledge about business trends, such as which products are selling or which ones would benefit most from promotional advertising.


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