Data: Data, the raw material for informationis defined as groups of non-random symbols which represent quantities, actions, objects, etc. Data items in information systems are formed from characters. These may alphabetic, numeric or special symbols such as *, $. Data Items are organized for processing purposed into data structures, file structure and databases.
Data Relevant to Information Processing, and decisions making may also be in form of text, images or voice, e.g. students fill an admission form when they get admission in college. The form consists of raw facts about the students. These raw facts are students name, father name, address etc. The purpose of collecting this data is to maintain the records of the students during their study period in the college.
Information: Information is the data that has been Processed into a form that is meaningful for the recipient and is of real or perceived value in current or prospective actions or decisions. Processing the facts involves the task of comparison, sorting (context). Information has value in the Decision Making Process which will change the probabilities associated with the expected outcomes in a decision situation.
Information is the summarization of data. Technically, data are raw facts and figures that are Processed into information, such as summaries and totals. But since information can also be the raw data for the next job or person, the two terms cannot be precisely defined, and both are used interchangeably e.g. Data collected from census is used to generate different type of information. The government can use it to determine the literacy rate in the country. Government can use the information in important decision to improve literacy rate.
The information can be classified in a number of ways for better understanding. Some classifications are a follows:
Action vs. No Action Information: The information which induces action is called Action Information. The information which communicated only the status of a situation is no-action information.
Recurring vs. Non Recurring Information: The information generated at regular intervals is recurring information. The monthly sales reports, the stock, statements, the trial balance, etc are recurring information. The financial analysis on the report on the market research study is non recurring information.
Internal vs. external Information: The information generated through the internal sources of the organization is termed as an internal information, while the information generated through the government through the government reports, the industry surveys, etc is termed as an external information, as the sources of the data are outside the organization. The action information, the recurring information and the internal information are the prime areas for computerization and they contribute qualitatively for the MIS.
Timeliness: Information must reach the user in a timely manner, just when it is needed not too early, because by the time it is used it would be out of date; not too late because the user will not be able to incorporate it into his/her decision-making.
Appropriateness: Information must be relevant to the person who is using it. It must be within the sphere of his/her activities so that it can be used to reduce uncertainty in his/her decision-making.
Accuracy: Accuracy costs, we don’t always 100% accurate information so long as we know the degree of accuracy it represents (e.g. or 5%).
Conciseness: Information should always contain the minimum amount of detail that appropriate for the user. Too much detail causes information overload.
Frequency: Frequency is related to timeliness. Too often the information presented in linked to the calendar (end of the week, beginning of the month); its frequency should be synchronized with the timing of the decision making of the user.
Understand ability: The format and presentation of information are very important. Some people prefer tubular information, whereas others may need it in a graphical form. Also the use of colors enhances the understand ability of what is presented.
Relevant: It pertains to the particular problem. What data is relevant depends on the decision making model used. E.g. university admissions officials may choose to consider the results of some high high-school test irrelevant, if they believe that it does not improve the chances of some applicant later becoming a successful student.
Complete: All the relevant parts are included. E.g. Marketing data about household incomes may lead to bad decisions, if not accompanied by consumption habits of the targets population.
Communication to the Right Person: It is often common for information to be directed to the wrong level in an organisation, Since each administrator at different levels needs information to perform his duties and responsibilities, information suppliers should ensure that information goes straight to the right person person and not to subordinates who may at times hold on to it for others to see how important he/she is in the organisation.
Current: Decisions are often based on the latest information available.
Economical: The costs of gathering information should be justified by the overall benefits.
Knowledge: Knowledge is the appropriate collection of information, such that it’s intent is to be useful. Knowledge is a deterministic process. When someone “memories” information (as less-aspiring test-bound student often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, integration such as would infer further knowledge.
Let us consider the case of a retail store that is trying to increase sales. Some of the data available includes sales levels for the last 36 months, advertising expenses, and customer comments from surveys. By itself, this data may be be interesting, but it must be organised and analyzed to be useful in making models to forecast patterns and determine relationships among various advertisement expenses and sales.
The resulting information (presented in equations, charts and tables) would clarify relationships among the data and would be used to decide how to proceed it requires knowledge to determine how to analyze data and make decisions.
Education and experience create knowledge in humans. A manager learns which data to collect, the proper models to apply, and ways to analyze results for making better decisions. In some cases, this knowledge can be transferred to specialized computer programs (expert systems).
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