Chapter 2 — What data are
Lesson map
Lesson 02 published. Map, example, EX004.
flowchart TD
N0["Learning outcomes"]
N1["2.1 Data, information, knowledge"]
N2["2.2 Types of data"]
N3["2.3 Context and meaning"]
N4["2.4 Structured and unstructured"]
N0 --> N1 --> N2 --> N3 --> N4
N4 --> EX["Worked example"]
EX --> Q["Exercise EX004"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["2.1 Data, information, knowledge"]
N2["2.2 Types of data"]
N3["2.3 Context and meaning"]
N4["2.4 Structured and unstructured"]
N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX004"]
[Learning outcomes]
|
v
[2.1 Data, information, knowledge]
|
v
[2.2 Types of data]
|
v
[2.3 Context and meaning]
|
v
[2.4 Structured and unstructured]
|
v
[Worked example] --> [Exercise EX004]
Learning outcomes
You will (1) distinguish data, information and knowledge; (2) describe data types; (3) explain how context shapes meaning.
2.1 Data, information, knowledge
Dr. Lindqvist explains that data are recorded symbols, such as a number in a till record. Information is data given meaning in context, such as knowing that the number is the quantity of milk sold at store twelve on a Tuesday. Knowledge is understanding that lets people act, such as knowing that milk sales rise before holidays and ordering more stock. Amara notes that governance mostly works with data and information, but its purpose is to support good knowledge and decisions. Amara agrees.
2.2 Types of data
Amara learns common types: numbers for quantities and prices, text for names and descriptions, dates and times for transactions, codes for categories such as store or product, and true or false values for flags such as consent to marketing. Each type has rules: a price cannot be negative, a date must exist on the calendar and a code must appear in an agreed list. Knowing the type of each field helps people check whether a value makes sense at all. Types are documented.
2.3 Context and meaning
The same value can mean different things. At Harbourline, sales in the finance system include tax, while sales in the store system exclude it. Both are labelled sales. When managers compare reports, the numbers disagree by the tax amount, and each side assumes the other is wrong. Amara sees that missing context, not bad arithmetic, causes the disagreement. Recording definitions alongside data is therefore one of the simplest and most useful governance actions an organisation can take. Harbourline later adds definitions to reports.
2.4 Structured and unstructured
Till records and stock levels are structured data, stored in tables with defined fields. Customer emails, supplier contracts and store photographs are unstructured, holding information that is harder to search and check. Harbourline has far more unstructured data than it realised, much of it in shared folders without owners. Dr. Lindqvist explains that governance must cover both, though methods differ: structured data suit automatic rules, while unstructured data rely more on classification, access control and retention. Amara counts thousands of unowned files in one folder alone.
Worked example
Amara distinguishes data, information and knowledge, learns data types and rules, finds that a tax difference explains disagreeing sales reports and notes unowned unstructured data in shared folders.
Synthetic case. Examples are synthetic.
Errors and limits
Assuming identical labels mean identical definitions causes disputes.
Exercises
- EX004 — Knowledge example. Give the course's example of knowledge. Rubric: 3 = milk sales rise before holidays, so order more;; 2 = partly correct;; 1 = vague;; 0 = none.
- EX005 — Sales disagreement. Why did sales reports disagree? Rubric: 3 = finance includes tax, stores exclude it;; 2 = partly correct;; 1 = vague;; 0 = none.
- EX006 — Unstructured example. Name one unstructured data example at Harbourline. Rubric: 3 = customer emails, contracts or photographs;; 2 = partly correct;; 1 = vague;; 0 = none.
Continue studying — lesson 02 (chapter 02 of 40)
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