Data and Information

• Data 


  Data is raw facts and figures with no meaning, for example 532 is meaningless
• Information 


  Information is data that has been processed and given a context e.g £532 in the bank
• Knowledge 


  Knowledge is an understanding of rules. It’s the result of applying rules to information to allow decisions to be made or to allow you to interpret the information. For example a shop had a target sale of 1000 but only reached 532 in actual sales. Therefore they need to advertise more
• Knowledge Worker 


  someone who is employed because they understand rules related to a particular task
• Factors affecting quality of information

  • Up to date
  • Complete
  • Relevance
  • Accuracy / reliable
  • presented effectively
• Up to date 


  If information is out of date, then it can reduce quality of information. Real time processing needs data inputted to be processed immediately, otherwise it’s not up to date. Is the information collected in a timely manner. Is it recent and regularly updated
• Complete 


  Refer to situations whereby information is missing. Is all the information required there. Is there anything missing or something that hasn’t been filled out.
• Relevance 


  Having information that isn’t relevant can be a disadvantage as it adds volume which can increase the time taken to find relevant information. Information that is essential in one situation may have no use in another
• Accurate / reliable 


  Information has been verified and deemed trustworthy. If information comes from a direct source its more likely to be reliable than information from an indirect source
• Presented effectively 


  If information is presented in such a way that’s difficult to understand the it loses its value. Is the information clear, easy to understand and read.
• Data Validation methods

  • range
  • type
  • length
  • format
  • presence
  • Modulus 11
  • Lookup
• A common problem with computer systems is that it is very easy to put incorrect data into them e.g

  • could mistype a name or address in a database resulting in letters sent to wrong people
  • an EFTPOS till could misread a barcode and charge wrong price
  • A MICR reader could misread account number on cheque and take money out of wrong bank account
• GIGO - garbage in garbage out 


  If you put incorrect data into a computer system, you will get incorrect results out of it. Processing incorrect inputs will produce incorrect outputs

  • Sometimes incorrect data can cause a computer system to stop working temporarily. Problem in batch processing systems. Stops a batch processing system, the whole nights processing time may be lost
• Techniques to make it difficult for incorrect data to enter:

  • Data verification
  • Data validation
• Data Validation 


  ensures data entered is sensible and accurate. Mistakes if data is copied manually:

  • person may misread original data
  • long codes which have no human meaning are vulnerable
  • type of typing error where two characters are accidentally swapped
  • Omission error is when data is omitted or excluded from data entry. Data could be lost, damaged or overlooked prior to entry into computerised system
• Types of Transcription Error

  1. Omission errors - when info is left out
  2. Substitution error - when info is complete but incorrect
  3. Transposition errors - slightly different form substitution errors in that the correct info is entered but reversed or mixed up in some way
• Data Validation 


  A check performed by computer software on data as it enters a computer system at the input stage for processing.

  Purpose: trap any data that doesn’t conform to certain rules

  It cannot prove data is the actual value the user intended

  It allows computer to use a number of techniques to ensure data entered will be

  • sensible, reasonable, within acceptable boundaries and complete

  Data is not processed until validation check has been successful
• Range Check 


  will check an input value against an upper limit boundary and a lower limit boundary. If the value falls outside the limits then it is invalid
• Type Check 


  This ensures that the data item is of a particular data type
• Length Check 


  used to check the data entered contains a certain number of characters
• Format Check 


  used to ensure data item matches a previously determined pattern and that particular characters have particular values such as letters or digits.
• Presence Check 


  when entering data into a database some fields may be optional. This check will not allow certain fields to remain blank. e.g a mobile telephone number may be optional as the person may not own one
• Check Digit 


  involves using an extra digit which is added to the numeric data item. Used on bar codes on products.

  Calculated using the digits from the number and then added to end of the number.

  When number is inputted, same calculation takes place and result is compared to check digit. If same results, processing continues. If not, error has occurred and number needs to be re-entered
• Lookup Check 


  This method uses a lookup table. The data value entered is compared against a stored list of data values looking for a match. If a match is found the data value is valid, otherwise it’s invalid. For example, when a product code is looked up in a stock file to confirm such an item exists
• Data Verification 


  used to confirm the integrity of data entered into the system. It ensures the data is consistent and that it has not been corrupted. Verification checks that the data which has been entered is as intended and matches the source data.

  A human operator can perform a visual check or proofreads what has been entered. The data subject may be asked to confirm the details. Verification reduces the possibility of transcription errors
• Methods of Data Verification

  • Double entry
  • Proof reading
• Double Entry 


  This involves entering the data twice into the computer system and the computer will check both copies to ensure there are no differences. Any differences will be manually corrected
• Proof reading 


  This is also known as visual checking. This involves the user checking data entered into the computer system perfectly matches the source of the data. If the user sees that data is correct then they confirm this to the computer
• Some information systems process data that’s entered into them in batches from documents. These systems typically use batch processing. Standard validation checks such as range and format checks are used to identify typing errors during data entry, but when data is entered in batches there are two extra types of errors that may occur:

  • a document may be accidentally missed out
  • the data on a document is entered twice
• Control and Hash totals

  Two special validation checks that are used when data is entered in batches to identify if one of these types of errors occurs
• Control Totals 


  Involve establishing record counts or totals of the values in selected fields within each record, and checking these totals during data validation. They are meaningful in the sense that they may have a use that is additional to their function within the system
• Hash totals 


  These are meaningless totals. A hash total is a number produced by adding together corresponding fields over all the records of a file, when such a total doesn’t have any external meaning but is used solely to verify the records in the file.

  The hash total is amended whenever a change occurs to the relevant field; the file is verified by recomputing the hash total and any corruption to the values in the field will be shown by a discrepancy between the stored total and the recomputed total
• Limitations of Data Validation and Verification

  Data validation and verification don’t guarantee that all data entered will be correct and error free. There are limitations in using either of these methods. When using a range check for the day of a typical month, it’s possible that the day is valid but incorrect such as the day was entered as 13 and should be 31. referred to as transposition error.
• Data validation limitations 


  When using a presence check if data exists in the given field it will be valid but the actual data could be incorrect such as forename rather than surname.

  In using a lookup table, a data value may be valid but isn’t stored in the lookup table and therefore it will be classified as invalid by the computer. This will require the lookup values stored in the table being updated regularly to ensure the data validation check isn’t limited
• Data verification limitations 


  When using data verification, a limitation could be a data source. If the data source is unreliable and the data entered is checked against this source it will cause a problem.

  The user may proofread and seeing a match with what is on the source document with what is displayed on the screen and then they will confirm this to the computer as being correct when the actual source is incorrect.