92526 Chap.1 Defining Data and the DIKW Pyramid
Defining Data and the DIKW Pyramid
The first module sets the vocabulary the rest of the subject depends on. It starts from the Australian Bureau of Statistics view of data as measurements or observations collected to inform, and breaks any table into three parts: the data unit being studied, the data items measured about it, and the individual observations recorded.
With those words in place, counting and restructuring a sports dataset becomes mechanical rather than guesswork.
The chapter then climbs the DIKW pyramid, in which raw data is processed into information, connected into knowledge and distilled into wisdom, and explains why the base of that pyramid is messy, incomplete and socially constructed.
It closes with the three reasons sport collects data at all, summarised as explore, trust and predict, each pointing towards better decisions.
What this chapter covers
- 01
The ABS definition of data
- 02
Data units, data items and observations
- 03
Counting observations in a sports table
- 04
Data, information, knowledge and wisdom
- 05
Why data is messy, incomplete and socially constructed
- 06
Explore, trust and predict as reasons to collect
Worked example · free
Name the unit, items and observations in a four-rower test table
- 1Data unit: each row describes one rower, so the unit is a rower and the table has 4 units.
- 1Data items: the columns are 2 km time, height and squad, so there are 3 items, two numerical and one categorical.
- 1Observations: every unit has a value for every item, so 4 times 3 gives 12 observations.
- 1The 181.5 cell is one observation: the value of the item height recorded for the unit Rower B.
Key terms
- Data item
- A characteristic of a data unit that is measured or counted, such as height or country of birth.
- Information
- Data that has been processed and grouped so it means something to a particular person, team or goal.
- Knowledge
- The patterns that connect separate clusters of information, one level above information in the DIKW pyramid.
- Wisdom
- Shortcuts for decisions learnt from clusters of knowledge, at the top of the DIKW pyramid.
Defining Data and the DIKW Pyramid FAQ
What is the difference between a data unit and a data item?
A data unit is the entity you are studying, such as a player, team or business, and in a standard table it occupies a row. A data item is a characteristic measured or counted about that unit, such as sprint time or height, and it occupies a column. The value where a row meets a column is one observation.
How do you count observations in a sports dataset?
Multiply the number of rows by the number of measured items, provided every unit has a value for every item. A squad of 30 players tested on 6 items gives 180 observations. If players are tested on several occasions and each occasion is a row, multiply players by occasions first to get the rows.
What does the DIKW pyramid show?
It shows how raw data becomes useful for decisions. Data are raw records of discrete events; information is that data processed into meaningful clusters; knowledge is the pattern connecting clusters of information; and wisdom is the set of shortcuts learnt from knowledge. Each level up compresses the one below it.
Why is data described as socially constructed?
Because people decide what to collect, how to collect it and for what purpose. The way data is gathered influences what participants provide, researchers only record what they notice, and data are built to serve a goal, so what is never asked about never becomes data. Bias can enter at each of those points.
Why does sport collect data in the first place?
The Week 1 slides give three reasons: data lets you see what you cannot see yourself, it places evidence under beliefs that shape a worldview, and it lets you estimate what might happen next. These are summarised as explore, trust and predict, and all three aim at improved decision making.
Exam move
Draw any small sports table you know, from a fantasy league sheet to a gym log, and label one data unit, one data item and one observation on it. Then count the observations two ways, once as rows times items and once by counting cells, until the arithmetic is automatic.
Write the four DIKW levels from memory with a sport example for each, making sure each example really is a pattern or a shortcut at the upper levels rather than another summary. Finally, take one dataset you have used and list how its collection method might have shaped what it contains, which is the habit the later weeks on quality and bias build on.
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