DATA

Data is a collection of facts, such as numbers, words, measurements, observations or simply descriptions of things. It forms a set of values of qualitative or quantitative variables. We are surrounded by different kinds of data. Individual pieces of data are separate pieces of information. Today, there is so much information available that the real question is whether we should trust it. How much of it is true, and how much is false or misleading? Information about political affairs, the economic state of a country, or predictions that life on planet Earth will soon disappear and that humans alone are responsible for this future. Should we believe the endless flow of words—overly positive when they are political promises and overly negative when they are predictions about the future? Should we believe all these exaggerations? It is a personal choice.

We also produce a great deal of information on a personal level because each of us stands at the centre of our own little universe, reflecting what happens around us. Much of this information exists in the form of rumours and gossip, which can cause a great deal of trouble and anxiety. How can we distinguish gossip from genuine information? We have to rely on our own judgement. One thing, however, is certain: we live in a society obsessed with information, data and all the statistical tools used to collect, present and interpret it. Yet even statistics cannot decide whether information is true or false. Some people even whisper that statistics themselves are responsible for many of these confusions and misunderstandings.

While the concept of data is commonly associated with scientific research, data is collected by a huge variety of organisations and institutions, including businesses (for example, sales data, revenue, profits and share prices), governments (crime rates, unemployment rates and literacy rates) and non-governmental organisations (such as censuses of homeless people carried out by charities). Everything in nature can also be classified as data and can be collected, counted and measured.

Data is measured, collected, reported and analysed, after which it can be presented using graphs, charts, images or other analytical tools. As a general concept, data refers to existing information or knowledge represented or coded in a form suitable for processing and use. Raw data is a collection of numbers or characters before it has been cleaned or corrected. Raw data often needs to be corrected to remove outliers or obvious measurement and recording errors, such as a thermometer placed in the Arctic recording a tropical temperature. Data processing usually takes place in stages, and the processed data from one stage often becomes the raw data for the next. Field data is raw data collected in an uncontrolled natural environment. Experimental data is generated within a scientific investigation through observation and recording. Today, data has often 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 and meaning. According to the traditional view, data is collected and analysed, becoming useful information only after it has been interpreted. Knowledge develops from extensive experience working with information on a particular subject. For example, the height of Mount Everest is considered data. That height can be measured precisely, recorded and stored in a database. The data may then be included in a book together with other facts about Mount Everest to help readers decide on the best method of climbing it. A mountain guide using years of climbing experience to advise others on the safest route to the summit represents knowledge. Some people extend this sequence by adding wisdom—the ability not only to possess knowledge but also to understand when and how to use it appropriately.

Data is often regarded as the least abstract concept, information as the next level, and knowledge as the most abstract. In this view, data becomes information through interpretation. For example, the height of Mount Everest is data; a book describing its geological characteristics is information; and a guidebook explaining the best way to reach its summit represents knowledge. However, some researchers argue that this hierarchy oversimplifies the relationship between data, information and knowledge. In general, information is closely connected with ideas such as communication, control, meaning, perception, representation and understanding.

Before the invention of computers, only people could collect data and identify patterns within it. Since the development of computers and digital technologies, machines can also collect, process and analyse enormous amounts of data. During the 2010s, computers became essential in fields ranging from marketing and public services to medicine, engineering and scientific research. Mechanical and digital computing devices differ mainly in the ways they represent and process data.

Data can be gathered through primary or secondary sources. A primary source means that the researcher collects the data directly for the first time. A secondary source means that the researcher uses data already collected by someone else, such as published research or official statistics. Data analysis methods vary and may include techniques such as data triangulation and data percolation.

If we summarise everything discussed so far, data can be either qualitative or quantitative.

Qualitative data is descriptive information—it describes qualities or characteristics.

Quantitative data is numerical information expressed in numbers.

Quantitative data can be either discrete or continuous.

Discrete data can take only certain values, usually whole numbers.

Continuous data can take any value within a given range.

Discrete data is counted, whereas continuous data is measured.

Examples of qualitative data include:

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

Examples of quantitative data include:

• Height (continuous).
• Weight (continuous).
• The number of petals on a flower (discrete).
• The number of customers in a shop (discrete).

Data can be collected in many different ways. The simplest method is direct observation and counting, such as counting the number of cars passing a particular point. Data can also be collected through surveys and questionnaires. A census collects information from every member of a population, while a sample collects information from only a selected part of the population.

There are several main sources of data:

1. Data made available by others.
2. Data resulting from an experiment.
3. Data collected through an observational study.
4. Primary source data (surveys, questionnaires and observations).
5. Secondary source data (reports, books, catalogues and brochures).

We will finish these short observations about data and information with a few activities for everyone.

Activity 1: Make a list of things that represent discrete data. What type of data are they?

Activity 2: Make a list of things that represent continuous data. What type of data are they?

Activity 3: Compare discrete and continuous data. Which type do you think is more common in everyday life? Explain your answer.

Activity 4: Collect 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: Think about how many kind and helpful things you have done this year. How many times have you made someone smile or feel happier? Can you make a list of evidence showing that you have been a kind and caring person this year?