Data, Information and Knowledge

1. Data, Information and Knowledge WJEC ICT GCSE

2. Starter Activity • Open the PowerPoint: • L Drive  Miss Pickles  Year 10  Data, Information and Knowledge • Complete Task 1: • Find a definition for these terms: • Data • Information • Knowledge

3. Lesson Objectives • Understand the difference between data and information. • Be able to identify data, information and knowledge. • Suggest why data, information and knowledge is used.

4. Data • Data are raw facts and figures that on their own have no meaning. (e.g. readings from sensors, survey facts, etc) • Data can be numbers, words, letters, images, sound etc.

5. Information • Information is data that has been processed by a computer system to give it meaning. • Processed can mean: • having calculations performed on it • converted to give it meaning • organized in some way 21041926 Date of Birth - 21st April 1926 A,A,A,A,B,B,C,C,C,D,D A student’s predicted GCSE grades 2010, 93, digital TV In 2010, 93% of UK homes had digital TV

6. Knowledge • Knowledge is derived from information by applying rules to it. • Draw conclusions, make judgements or opinions. Make predictions and decisions. Example Knowledge If we apply knowledge that the speed limit on the motorway is 70 mph we now know that the driver is breaking the speed limit. Information A vehicle is travelling at 100 mph on the M4. Data 100, M4

7. Collecting Data Data can be collected ...

8. Turning Data into Information To turn data into information it needs to be processed. Data Processing Information Data Processing Information

9. Turning Information into Knowledge Understand the information and learn from it. We apply rules to it to learn. We can then draw conclusions, make judgements or opinions, make predictions and decisions.

10. Summary Data Raw facts and figures Information Data Context Meaning Processing Information Knowledge Rules

11. Quality of Data • GIGO (Garbage In Garbage Out) • If data input is poor the resulting information output will be poor i.e. corrupt, inaccurate etc. Garbage In Garbage Out

12. Measuring the Quality of Data • Is the data fit for purpose? • Is the data suitable for doing the job for which it was collected? • Is there enough data? • Has the correct data been collected? • Is the data accurate? • Has the data been made up? • Is some data missing? • Are their any mistakes in the data? • Is the data bias? • Are questions worded to prompt a certain answer? • Has a broad sample of data been collected?

13. Encoding Data • Data is often coded when it is collected on input into an ICT system. e.g. • M or F • Mo, Tu, We, Th, Fr, Sa, Su • S, M, L, XL, XXL

14. Advantages of coding Data • Coded data is quicker to type in • It takes up less storage space • It is easier to check a code using validation checks • It is faster to access data that is coded

15. Disadvantages ofcoding Data • Precision of data can be lost • In the example all shades of blue are coded as “blue” • The user needs to know the codes used Data in Pink Blue Black Blue Stored data Pink Blue Black Blue

16. Let’s Recap • What is data? • What is Information? • What is knowledge? • What does GIGO stand for? • What does encoding mean?