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Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
Validation
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Validation

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Validation

Validation

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  • 1. Objectives of ControlThe objectives of control are: To ensure that all data are processed To preserve the integrity of maintained data To detect, correct and re-process all errors To prevent and detect fraud
  • 2. Types of ControlsThe different controls can be grouped under fiveheadings:Manual controlsData protection controlsValidation checksBatch controlsOther controls
  • 3. Types of ErrorSystem designers must guard against the followingtypes of error:Missing source documentsSource documents on which entries are omitted,illegible or dubiousTranscription errorsData preparation errorsProgram faultsMachine Hardware faults
  • 4. Manual ChecksEven in advanced systems checking of sourcedocuments is necessary. Checks may include:Scrutiny to detect:  Missing entries  Illegible entries  Illogical or unlikely entriesReference of the document to stored data to verifyentriesRe-calculating to check calculations made on thedocument
  • 5. Data Collection Controls The collection of data for processing involves transcribing it into a form suitable for machine processing. There is a real possibility of error at this stage. Controls must be imposed to prevent or detect errors at this stage. The type of control depends on the method of data collection used:
  • 6. Data Collection Controls 2 On-line systems. These depend on the data displayed on a VDU or printed being checked by the operator before being processed. Character recognition. With these techniques, accuracy depends on the character reader detecting any doubtful character or mark.
  • 7. Validation Checks 1 A computer can’t notice errors in data being processed in the same way that a human operator can. Validation checks are an attempt to build into computer programs the ability to detect and report incorrect data items Checks can be made at two stages:  Input – when data is entered  Updating – after processing
  • 8. Validation Checks 2The main types of validation check used are: Presence. Data are checked to ensure that all necessary fields are present e.g. A payroll program must have complete employee and national insurance fields. Size. Fields are checked to ensure they contain the correct number of characters e.g. Student_Id should contain 6 characters i.e. G01234 Range. Numbers or codes are checked to ensure that fall within a permissible range e.g. DOB in a school database should fall between 1982 and 1989.
  • 9. Validation Checks 3 Character. Fields are checked to ensure that they contain only characters of the correct type e.g. there are no letters in a number field. Format. Also called a picture check. Fields are checked to ensure that the format is correct e.g. that a code contains the correct number of letters and numbers AND they are in the correct sequence. Reasonableness. Quantities are checked to ensure that they are not abnormally high or low. Check Digits. See next slide.
  • 10. Check Digits Use of a check digit allows a number to be self- checking. It is calculated using a mathematical formula and then becomes part of the number itself. When the number is input into the computer the validation program uses the same mathematical formula to check the number and ensure that the number is correct i.e no digits have been transposed. The modulus 11 algorithm is used to create check digits for ISBN numbers.
  • 11. Modulus 11 Assign each digit a weight. The right hand digit (the least significant) is given a weight of 2, the next digit to the left 3 and so on Multiply each digit by its weight and add the products together Number 2 5 4 6 Weight 5 4 3 2 Products 10 20 12 12 Total 54 Divide the total by 11 and find the remainder
  • 12. Modulus 11 - 2 54 divided by 11 = 4 remainder 10. Subtract the remainder from 11 to find the check digit. 11 – 10 =1 The new number is 25461 Now try this. The ISBN number of Pat Heathcote’s book is 095324900. What is the check digit? Why is it presented in this format?
  • 13. Batch Processing 1Batch controls are fundamental to most computerbased accounting systems. The main stages ofbatch control are:Batching. Documents are arranged in batches bybeing placed in a wallet or clipped together. A batchcover note (see next slide) is attached to the batch.Numbering. Each batch is allocated a uniquenumber, which is entered on the batch cover note.Batch Registers. Each department responsible forprocessing the batch records its receipt anddispatch in a register. It is then possible to checkthat all batches have been dealt with and trace anybatch that gets lost or delayed.
  • 14. Batch Processing 2 Batch Totals. Control totals are obtained for each batch. The control totals comprise:  The total number of documents in the batch  Totals of the fields that are required to control e.g. total value of invoices Hash Totals. A hash total is a sum of values calculated solely for validation purposes e.g. adding all the employee numbers together.The computer should be able to perform the same calculation and report any discrepancy. All of the above is recorded on the batch cover note so that the results of processing can be checked against manually calculated values.
  • 15. Verification Verification is the process of entering data twice. The second entry is compared to the first to ensure that it is accurate. It is commonly used in batch processing where a second data entry clerk will key in each batch to verify it. You will have come across this technique when changing a password and you are asked to re- confirm your new password.
  • 16. Validity vs. Accuracy It is possible to to ensure that data is valid. It can never be guaranteed to give us correct information as the following examples show: “A student’s DOB is 02/01/87”. The data falls within a valid range and is entered in the correct format. However, her actual DOB is 02/10/87! “The market research questionnaire data we collected shows that 95% of the population eats soup twice a day”. However, this research was carried out outside a soup kitchen! Remember valid data can lead to inaccurate information.
  • 17. Further Reading Paper  Heathcote – pages 127 – 131  De Watteville – page 164  Mott – pages 66 – 68 Web  www.creditron.com/checkdig.htm (Bar codes and Mod10 explained)  www.lcweb.loc.gov/issn/check.html (Modulus 11 explained)

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