DATA RECORDS & FILES By Sinkala

Data -These are raw facts and figures. For example, data can be, recorded hours worked by an employee, invoices, receipts, orders numbers, or prices, goods quantities Data itself is meaningless unless manipulated and transformed (processed) into meaningful forms (information) By Sinkala

Data processing stages Data processing stages refers to the process of collecting data, at the point of activity up to the time data is transformed into information. Raw data is assumed to pass through 5 stages as shown below.   OUTPUT INPUT PROCESS, storage By Sinkala

By Sinkala

Sources of Data (origination) Data can originate from the following sources: -Business transactions(invoices, receipts, customer orders etc) -Census( counting of people and distribution of wealth) -Observation(observing some happenings and recorded details) -Experiments (observations and recordings about the progress of an experiment) - Market survey(sampling to test the reaction of a product or service being introduced) By Sinkala

Verification(Preparation) Data verification is a process of checking for errors in input data before it is carried forward for processing. Verification is normally carried out by more than one person or machine. The same data is compared and checked character by character against one another

Types of Errors detected by verification -Transcription errors -Transposition -Omission

Six (6) Steps required to process a transaction and they include( refer to slide 4 ) data entry/ capture validation DATA PROCESSING storage OUTPUT generation query support

Data entry/ capture This is the collection of raw data from the outside world into an information system. It can be manually or by using automatic data capture devices, such as: ???

MICR, OCR, OMR, Bar Code Reader  Keyboard, mouse, flatbed scanners, joysticks, digital data tablets (for graphic drawing), electronic cash registers etc

Validation Validation is ensuring that the input data is of the right type and within reasonable limits. Type of validation checks. (concept of a field)

E. g name, a date, an amount, sex etc NAME: SEX: COURSE: A field is any item of data within a record. It is made up of a number of characters. E. g name, a date, an amount, sex etc NAME: SEX: COURSE: NAME SEX COURSE

Type of validation checks. Size checks. - Fields are checked to ensure that they contain the right number of characters e.g. customer No. Specified as 6 numerical characters. This means that the program will validate for 6 numerical characters. Presence check Range checks also called limit checks Character check Format check. Consistency

PROCESSING This is a procedure of transforming raw data into useful information. Therefore, at this stage DATA is converted into INFORMATION. (Information is the processed data)

PROCESSING This is a procedure of transforming raw data into useful information. Therefore, at this stage DATA is converted into INFORMATION. (Information is the processed data) STORAGE After data has been processed, it is stored either internally or externally. Typical storage devices (magnetic and optical) include: Magnetic: hard disk (fast access, big capacity) -floppy disk (slow access, low capacity) -Flash disk, Tapes Optical: CDs, DVDs With storage the main features include speed, reliability and capacity of storage.

OUTPUT Is the final generation of information from an information system to the outside world. Output generation is facilitated by output devices which include: -CRT (cathode ray tube) monitors and LCD/LED displays -printers -speakers -plotters QUERY SUPPORT At this stage information is queried for further verification or for conformity (agreeing with certain accepted standards or norms).

General Qualities of good information: For information to be useful, it must possess several characteristics attributes. These attributes add value or increase the potential of information. Accurate Meaningful Reliable/ reliability Easy to use User targeted Relevant Timely Complete Error free verifiable  

General Qualities of good information: Accurate- For information to be useful, it must be exactly and precise. This can help the decision makers to make accurate decisions. Meaningful- Good information must be meaningful and easy to understand, not to be misleading. Reliable- Good information must accurately represent the events or activities of an organization. In addition, it must be easy to follow and retrievable.  

General Qualities of good information: Easy to Use-(Understandable) - Good information must be user friendly. Information must not be complicated because it may not be too useful as people may not really understand it. User Targeted- information must be specific, brief and user targeted. Timely- Good information should provided in good time and received at the right time  

General Qualities of good information: Relevant –good information should make a difference to the decision maker by reducing uncertainty or by adding increased knowledge or value to the decision maker. Complete- must include all relevant data or aspects of the data that are required. Error free- data sets must contain a minimum number of errors. Verifiable- generated information of the same process must be the same or within reasonable variance.

Data organization c c c c c c c c Refers to the way we store and arrange data, in order to make it easy for storage, manipulation and retrieval of information. Information is organized in unit form according to the data hierarchy structure shown below FILE RECORDS RECORDS RECORDS F F F FIELD F F FIELD FIELD FIELD c c c c c c c c

A FILE:

Exercise(max 10min) By Sinkala