White Paper on Concept Of Data Processing
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Wikipedia
A white paper is a report or guide that informs readers concisely about a complex issue and presents the issuing body’s philosophy on the matter. It is meant to help readers understand an issue, solve a problem, or make a decision. A white paper is the first document researchers should read to better understand a core concept or idea.
The term originated in the 1920s to mean a type of position paper or industry report published by some department of the UK government.
Since the 1990s, this type of document has proliferated in business. Today, a business-to-business (B2B) white paper is closer to a marketing presentation, a form of content meant to persuade customers and partners and promote a certain product or viewpoint. That makes B2B white papers a type of grey literature.
The term white paper originated with the British government and many point to the Churchill White Paper of 1922 as the earliest well-known example under this name. Gertrude Bell, the British explorer and diplomat, was possibly the first woman to write a white paper. Her 149-page report was entitled “Review of the Civil Administration of Mesopotamia” and was presented to Parliament in 1920. In the British government, a white paper is usually the less extensive version of the so-called blue book, both terms being derived from the colour of the document’s cover.
White papers are a “tool of participatory democracy … not [an] unalterable policy commitment”. “White papers have tried to perform the dual role of presenting firm government policies while at the same time inviting opinions upon them.
In Canada, a white paper is “a policy document, approved by Cabinet, tabled in the House of Commons and made available to the general public”. The “provision of policy information through the use of white and green papers can help to create an awareness of policy issues among parliamentarians and the public and to encourage an exchange of information and analysis. They can also serve as educational techniques.”
White papers are a way the government can present policy preferences before it introduces legislation. Publishing a white paper tests public opinion on controversial policy issues and helps the government gauge its probable impact.
By contrast, green papers, which are issued much more frequently, are more open-ended. Also known as consultation documents, green papers may merely propose a strategy to implement in the details of other legislation, or they may set out proposals on which the government wishes to obtain public views and opinion.
Data processing is the collection and manipulation of digital data to produce meaningful information. Data processing is a form of information processing, which is the modification (processing) of information in any manner detectable by an observer.
The term “Data Processing”, or “DP” has also been used to refer to a department within an organization responsible for the operation of data processing programs.
Data processing may involve various processes, including:
- Validation – Ensuring that supplied data is correct and relevant.
- Sorting – “arranging items in some sequence and/or in different sets.”
- Summarization (statistical) or (automatic) – reducing detailed data to its main points.
- Aggregation – combining multiple pieces of data.
- Analysis – the “collection, organization, analysis, interpretation and presentation of data.”
- Reporting – list detail or summary data or computed information.
- Classification – separation of data into various categories.
The United States Census Bureau history illustrates the evolution of data processing from manual through electronic procedures.
Although widespread use of the term data processing dates only from the 1950’s, data processing functions have been performed manually for millennia. For example, bookkeeping involves functions such as posting transactions and producing reports like the balance sheet and the cash flow statement. Completely manual methods were augmented by the application of mechanical or electronic calculators. A person whose job was to perform calculations manually or using a calculator was called a “computer.”
The 1890 United States Census schedule was the first to gather data by individual rather than household. A number of questions could be answered by making a check in the appropriate box on the form. From 1850 to 1880 the Census Bureau employed “a system of tallying, which, by reason of the increasing number of combinations of classifications required, became increasingly complex. Only a limited number of combinations could be recorded in one tally, so it was necessary to handle the schedules 5 or 6 times, for as many independent tallies.” “It took over 7 years to publish the results of the 1880 census” using manual processing methods.
The term automatic data processing was applied to operations performed by means of unit record equipment, such as Herman Hollerith’s application of punched card equipment for the 1890 United States Census. “Using Hollerith’s punch card equipment, the Census Office was able to complete tabulating most of the 1890 census data in 2 to 3 years, compared with 7 to 8 years for the 1880 census. It is estimated that using Hollerith’s system saved some $5 million in processing costs” in 1890 dollars even though there were twice as many questions as in 1880.
Computerized data processing, or Electronic data processing represents a later development, with a computer used instead of several independent pieces of equipment. The Census Bureau first made limited use of electronic computers for the 1950 United States Census, using a UNIVAC I system, delivered in 1952.
The term data processing has mostly been subsumed by the more general term information technology (IT). The older term “data processing” is suggestive of older technologies. For example, in 1996 the Data Processing Management Association (DPMA) changed its name to the Association of Information Technology Professionals.” Nevertheless, the terms are approximately synonymous.
Commercial data processing involves a large volume of input data, relatively few computational operations, and a large volume of output. For example, an insurance company needs to keep records on tens or hundreds of thousands of policies, print and mail bills, and receive and post payments.