MM1041 Chap.1 Business Data and the DIKW Pyramid
Business Data and the DIKW Pyramid
Where the course begins
The first lecture starts from business itself, described as people working together in an organised way to supply the goods and services others need, usually in return for payment.
Artificial intelligence and data analytics matter because they change how that effort is directed: the lecture says they transform decision making, make practice evidence-based, proactive and efficient, and affect customer engagement, operational efficiency and competitiveness together.
Holding that definition in mind keeps every later technique tied to a business purpose rather than treated as technology for its own sake.
Data, information, knowledge and wisdom
The DIKW pyramid is the first model the course draws. Data sits at the base, in the greatest volume and the least processed form: one recorded value, a measurement or a description, about a thing or an event.
Information is data organised to answer a question, such as a monthly total. Knowledge is a pattern that explains how something behaves, and wisdom is the judgement about what to do.
The lecture's point is that climbing a level means asking a new question of the raw data and making it more valuable, which is why a firm with more data is not automatically a firm that decides better.
Why data has to become variables
The e-learning lesson adds a technical point that is easy to skip.
In analysis, data is represented by variables, because the goal is to compute on it: once a price, a date or a store is held as a variable, mathematical models and programs can find patterns across millions of records.
The lesson also stresses that data is everywhere, from web searches and e-commerce purchases to bank card transactions and social media, and that it already drives science, business management and government policy.
The analytics ladder
A second picture in the first lecture climbs from descriptive analytics, which asks what happened, through diagnostic analytics, which asks why, and predictive analytics, which asks what could happen, to prescriptive analytics, which asks how to make an outcome happen, with the top of the ladder asking what to do, why and how.
Naming the level of a question before answering it is the habit this chapter trains, because the level decides what evidence a good answer needs.
What you will be able to do
By the end of the chapter you should be able to define business, data, variable and data analytics in the course's terms, place any business report or model on the pyramid and the ladder, and explain in two sentences what would move it one level higher.
What this chapter covers
- 01
Business as organised effort to meet needs
- 02
Why AIDA changes business decisions
- 03
The DIKW pyramid from data to wisdom
- 04
Data as variables that can be computed
- 05
Descriptive, diagnostic, predictive and prescriptive questions
- 06
Naming the level before answering
Worked example · free
Place three reports on the pyramid
- 2Place the sales table.
- 2Place the influencer chart.
- 2Place the budget plan.
Key terms
- Information
- Data organised to answer a question, such as a total by branch or by month.
- Knowledge
- A pattern that explains how a business or market behaves, drawn from information over time.
- Wisdom
- The judgement about what action to take and why, built on knowledge.
- Descriptive Analytics
- Analysis that reports what happened, such as last quarter's sales.
- Diagnostic Analytics
- Analysis that explains why something happened by comparing it with a baseline.
- Prescriptive Analytics
- Analysis that recommends which action will produce a chosen outcome.
Business Data and the DIKW Pyramid FAQ
What is the difference between data and information?
Data is a single recorded quality or quantity, such as one transaction, and on its own it answers nothing. Information is data organised so that it answers a question, such as total weekend sales by branch. The shift from one to the other is work done by an analyst, which is why the pyramid treats each level as added value rather than added storage.
Why does the course treat wisdom as the top of the pyramid rather than knowledge?
Because a business is judged on its actions. Knowledge explains how customers or operations behave, but wisdom turns that explanation into a decision and a reason for it. A manager who understands a pattern but does not act on it has gained nothing measurable, so the course places the decision above the understanding.
How do I tell predictive from prescriptive analytics in a question?
Look for the verb. A question that asks you to forecast, estimate or expect is predictive, because it wants a likely future value. A question that asks you to recommend, choose or decide how to reach a target is prescriptive, because it wants an action. Many exam answers lose marks by forecasting when the question asked for a recommendation.
Exam move
Take one business you use every week and write one example of data, information, knowledge and wisdom about it. Then write one question at each level of the analytics ladder. If you can do both in ten minutes without looking back, the vocabulary of the first chapter is secure.
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