DATA

Data is a collection of facts, such as numbers, words, measurements, observations or even just descriptions of things. They form a set of values of qualitative or quantitative variables. We are surrounded by different kinds of data.  Pieces of data are individual pieces of information. There is so much information today that the problem is do we have to trust this information or most of it is a false one and misleading. Information about political affairs, about the economical state of the country, about how life on planet Earth is going to disappear soon and it is only humans to blame for that sad future ahead. Do we need to believe in all the endless flow of words - too positive if they are political promises and too negative if they come from predictions of the future. Do we need to believe in all these exaggerations? It is a personal choice. There is plenty of information that we produce on personal level as well, because we are in the centre of our own little universe and we mirror what’s going outside. Most of this information exists in the form of rumors and gossip which can cause a lot of troubles and headaches for some of us. How to differentiate between the gossip, the real information?  We have to use our own judgement. But we can say with confidence that we live in a society which is obsessed with the information, data and all these statistical instruments to collect, present and interpret the information. But even the statistics cannot decide between the gossip and information - which is true or false. Some people even are whispering that the statistics are to blame for all these confusions and misunderstandings when it comes to information. 

While the concept of data is commonly associated with scientific research, data is collected by a huge range of organizations and institutions, including businesses (e.g., sales data, revenue, profits, stock price), governments (e.g., crime rates, unemployment rates, literacy rates) and non-governmental organizations (e.g., censuses of the number of homeless people by non-profit organizations). Everything in nature as well could be  classified as data and collected, counted and measured.

Data is measured, collected and reported, and analyzed, whereupon it can be visualized using graphs, images or other analysis tools. Data as a general concept refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing. Raw data ("unprocessed data") is a collection of numbers or characters before it has been "cleaned" and corrected by researchers. Raw data needs to be corrected to remove outliers or obvious instrument or data entry errors (e.g., a thermometer reading from an outdoor Arctic location recording a tropical temperature). Data processing commonly occurs by stages, and the "processed data" from one stage may be considered the "raw data" of the next stage. Field data is raw data that is collected in an uncontrolled "in situ" environment. Experimental data is data that is generated within the context of a scientific investigation by observation and recording. Data has been described as the new oil of the digital economy.

Data, information, knowledge and wisdom are closely related concepts, but each has its own role in relation to the other, and each term has its own meaning. According to the common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion. Knowledge is derived from extensive amounts of experience dealing with information on a subject. For example, the height of Mount Everest is generally considered data. The height can be recorded precisely with an altimeter and entered into a database. This data may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to make a decision about the best method to climb it. Using an understanding based on experience climbing mountains to advise persons on the way to reach Mount Everest's peak may be seen as "knowledge". Some complement the series "data", "information" and "knowledge" with "wisdom", which would mean the status of a person in possession of a certain "knowledge" who also knows under which circumstances is good to use it.

Data is often assumed to be the least abstract concept, information the next least, and knowledge the most abstract. In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guide book containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge". "Information" bears a diversity of meanings that ranges from everyday usage to technical use. This view, however, has also been argued to provide an upside-down model of the relation between data, information, and knowledge. Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. 

Before the development of computing devices and machines, only people could collect data and impose patterns on it. Since the development of computing devices and machines, these devices can also collect data. In the 2010s, computers were widely used in many fields to collect data and sort or process it, in disciplines ranging from marketing, analysis of social services usage by citizens to scientific research. Mechanical computing devices are classified according to the means by which they represent data. 

Gathering data can be accomplished through a primary source (the researcher is the first person to obtain the data) or a secondary source (the researcher obtains the data that has already been collected by other sources, such as data in a scientific journal). Data analysis methodologies vary and include data triangulation and data percolation. 

If we have to summarise: Data can be qualitative or quantitative:

• Qualitative data is descriptive information (it describes something).
     
• Quantitative data is numerical information     (numbers).

Quantitative data can be Discrete or Continuous:

• Discrete data can only take certain values (like whole numbers).
     
• Continuous data can take any value (within a range).

Discrete data is counted, Continuous data is measured. Examples:

Qualitative:

• Your friends' favourite holiday destination.    
     
• The most common given names in your town.    
     
• How people describe the smell of a new perfume.

Quantitative:

• Height (Continuous).
     
• Weight (Continuous).
     
• Petals on a flower (Discrete).
     
• Customers in a shop (Discrete).

Data can be collected in many ways. The simplest way is direct observation and counting, for example counting cars. We collect data by doing a Survey and a questionnaire as well. A Census is when we collect data for every member of the group (the whole "population"). A Sample is when we collect data just for selected members of the group.

There are some different sources of data:

1. Data that are made available by others.

2. Data resulting from an experiment.

3.  Data collected in an observational study.

4. Primary source data (Survey, Questionnaire, Observations)

5. Secondary source data (Reports, books, catalogue, brochure)

And we are going to finish these short observations about data and information with a few activities for everybody.

Here     are some activities for you: Activity 1: Can you make a list with things that could represent discrete data? What kind of data this could be?  Activity 2: Can you make a list with things that could represent continuous data? What kind of data this could be?  Activity 3: Can you compare the discrete and continuous data? Can you make a judgement which data is more popular in life? Activity 4: Can you collect some data about how many days this year you were happy and how many days you were sad? Is the data you collected qualitative or quantitative?    Activity 5: Can you say how many good and kind things you did this year? How many times have you made somebody smile and to feel happy? Can you male a list with data as evidence you have being a good person this year?