Monday, November 17, 2008
What Is Data Entry
A data entry clerk is a member of staff who reads hand-written or printed records and types them into a computer. They are sometimes employed on a temporary basis, but most large companies which have large amounts of data will hire on a near permanent basis. For a mailing company, data entry clerks might be required to type in reference numbers for items of mail which had failed to reach their destination, so that the relevant addresses could be deleted from the database used to send the mail out. If the company was compiling a database from addresses handwritten on a questionnaire, the person typing those in would be a data entry clerk. In a cash office, a data entry clerk might be required to type expenses into a database using numerical codes.
The number of data entry clerks working with physical hand-written documents has declined in modern times, as it has become easier for people in other positions within a company to enter their own data as it emerges rather than have a different employee do this task full time. Data processing is any computer process that converts data into information or knowledge.[1] The processing is usually assumed to be automated and running on a computer. Because data are most useful when well-presented and actually informative, data-processing systems are often referred to as information systems to emphasize their practicality. Nevertheless, both terms are roughly synonymous, performing similar conversions; data-processing systems typically manipulate raw data into information, and likewise information systems typically take raw data as input to produce information as output.
When the domain from which the data are harvested is a science or an engineering, data processing and information systems are considered too broad of terms and the more specialized term data analysis is typically used, focusing on the highly-specialized and highly-accurate algorithmic derivations and statistical calculations that are less often observed in the typical general business environment. In these contexts data analysis packages like DAP, gretl or PSPP are often used. This divergence of culture is exhibited in the typical numerical representations used in data processing versus numerical; data processing's measurements are typically represented by integers or by fixed-point or binary-coded decimal representations of numbers whereas the majority of data analysis's measurements are often represented by floating-point representation of rational numbers.
More generally, the term data processing can apply to any process that converts data from one format to another, although data conversion would be the more logical and correct term. From this perspective, data processing becomes the process of converting information into data and also the converting of data back into information. The distinction is that conversion doesn't require a question (query) to be answered. For example, information in the form of a string of characters forming a sentence in English is converted or encoded meaningless hardware-oriented data to evermore-meaningful information as the processing proceeds toward the human being.
Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods. It has been described as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data and the science of extracting useful information from large data sets or databases.] Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making.
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The number of data entry clerks working with physical hand-written documents has declined in modern times, as it has become easier for people in other positions within a company to enter their own data as it emerges rather than have a different employee do this task full time. Data processing is any computer process that converts data into information or knowledge.[1] The processing is usually assumed to be automated and running on a computer. Because data are most useful when well-presented and actually informative, data-processing systems are often referred to as information systems to emphasize their practicality. Nevertheless, both terms are roughly synonymous, performing similar conversions; data-processing systems typically manipulate raw data into information, and likewise information systems typically take raw data as input to produce information as output.
When the domain from which the data are harvested is a science or an engineering, data processing and information systems are considered too broad of terms and the more specialized term data analysis is typically used, focusing on the highly-specialized and highly-accurate algorithmic derivations and statistical calculations that are less often observed in the typical general business environment. In these contexts data analysis packages like DAP, gretl or PSPP are often used. This divergence of culture is exhibited in the typical numerical representations used in data processing versus numerical; data processing's measurements are typically represented by integers or by fixed-point or binary-coded decimal representations of numbers whereas the majority of data analysis's measurements are often represented by floating-point representation of rational numbers.
More generally, the term data processing can apply to any process that converts data from one format to another, although data conversion would be the more logical and correct term. From this perspective, data processing becomes the process of converting information into data and also the converting of data back into information. The distinction is that conversion doesn't require a question (query) to be answered. For example, information in the form of a string of characters forming a sentence in English is converted or encoded meaningless hardware-oriented data to evermore-meaningful information as the processing proceeds toward the human being.
Data mining is the process of sorting through large amounts of data and picking out relevant information. It is usually used by business intelligence organizations, and financial analysts, but is increasingly being used in the sciences to extract information from the enormous data sets generated by modern experimental and observational methods. It has been described as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data and the science of extracting useful information from large data sets or databases.] Data mining in relation to enterprise resource planning is the statistical and logical analysis of large sets of transaction data, looking for patterns that can aid decision making.
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# posted by David @ 4:31 PM 
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