Chapter 1 IT -Data processing and information

• Data


  Raw data, can take the form of characters, symbols, images, audio clips, video clips
• Information


  Data that has been given meaning, often through processing
• The difference between data and information is that data has no meaning, whereas information is data which has been given meaning
• Examples of data


  • 110053, 641609, 160012, 390072, 382397, 141186
  • 01432 01223 01955 01384 01253 01284 01905 01227 01832 01902 01981 01926 01597
  • σωρFma
• Data is stored as a sequence of binary digits (bits) on a computer
• Data is usually processed for a particular purpose, often so that it can be analysed
• Direct data


  Data collected for a specific purpose or task and used only for that purpose
• Indirect data


  Data obtained from a third party and used for a different purpose to that which it was originally collected for
• Sources of direct data


  • Questionnaires
  • Interviews
  • Observation
  • Data logging
• Questionnaires


  • Consist of a set of questions, can be closed or open-ended, can be on paper or computer
• Interviews


  • Formal meeting between interviewer and interviewee, can be structured or unstructured
• Observation


  • Data collectors watch what happens in a given situation
• Data logging


  • Using a computer and sensors to collect data over time, can be continuous or at regular intervals
• Uses of direct data

  • Planning alteration of a bus route
• Data needed to plan bus route alteration


  • Walking time from new development to existing bus routes
  • Number of passengers using existing route
  • Number of passengers who would use new route
  • Effect on villagers' daily lives
• Electoral register


  List of adults entitled to vote in an election, can be open or restricted version
• Data brokers


  Companies that collect and analyse individuals' personal information and sell it to other organisations
• Uses of indirect data


  • Identity checks, fundraising, debt collection, mailing lists
• Advantages of direct data

  • We know how reliable it is
  • Can gather specific data
  • Only need to collect as much as required
• Disadvantages of direct data


  • Sample/group size may be small
  • May not know origin of data or if it's representative
• Advantages of direct data


  • We know how reliable direct data is since we know where it originated
  • We can ensure that a representative cross-section of the group is sampled
  • The data collector can use methods to gather specific data even if it is obscure
  • The data collector only needs to collect as much or as little data as necessary
  • The data may be useful to other organisations and there may be opportunities to sell it
• Disadvantages of direct data

  • The sample or group size may be small
  • The person collecting the data may not be able to gain physical access to particular groups of people
  • It may not be possible to gather original data due to the time of year
  • The collection of data may be more expensive than using an indirect data source
• Because of time and cash restraints, the sample or group size may be small whereas indirect data sources tend to provide larger sets of data that would use up less time and money than using direct data collection with a larger sample size
• With indirect data sources we may not know where the data originated and it could be that the source is only a small section of that group, rather than a cross-section of the whole group. This is often referred to as sampling bias
• With indirect data sources, the original purpose for which data was collected may be quite different to the purpose for which it is needed now. Irrelevant data may need to be removed
• Using a direct data source could be problematic if the people being interviewed are not available thus reducing sample size, whereas using indirect data sources allows the sample size to be greater resulting in increased confidence in the results produced
• To gather data from a specific sample would take a lot longer than it would with indirect data. In addition, by the time all the required data has been collected it may possibly be out of date so an indirect data source could have been used
• Indirect data may be of a higher quality as it might have already been collated and grouped into meaningful categories whereas with direct data sources, questionnaire answers can sometimes be difficult to read and the transcripts of interviews take time to read in order to create the data source
• Compared to indirect data sources, the collection of data may be more expensive than using an indirect data source as people may have to be paid to collect it. Extra cost may be incurred as special equipment has to be bought, such as data-loggers and computers with sensors, or purchasing the paper for questionnaires, whereas this would not be needed using an indirect source
• Observation


  A direct data source
• Differences between indirect data and direct data


  • Not provided
• Quality of information


  Judgement regarding the quality of information is fairly subjective, it depends on the user and such judgements can vary between users
• Poor quality data can lead to serious consequences. Poor data may give a distorted view of business dealings, which can then lead to a business making poor decisions. Customers can be put off dealing with businesses that give poor service due to inaccurate data, causing the business to get a poor reputation
• With poor quality data it can be difficult for companies to have accurate knowledge of their current performance and sales trends, which makes it hard for them to identify worthwhile future opportunities
• One example can be seen in the data provided by a hospital in the UK, which resulted in it being temporarily closed down, until it was realised that the death rate data provided had been incorrect and it was actually significantly lower
• Incorrectly addressed mail costs the postal service in the USA a substantial amount of money and time to process correctly
• Accuracy


  Information should be free from errors and mistakes
• If the original data is inaccurate then the resulting information will also be inaccurate. In order to make sure the information is accurate, a lot of time needs to be given to the collection and checking of the data
• Mistakes can easily occur, for example a quantity of 62 could easily be copied down as 26 if the digits were accidentally transposed
• Relevance


  Data captured should be relevant to the purposes for which it is to be used. It must meet the requirements of the user