Data Literacy for Business Decisions
Judge whether a business claim from a table, KPI, chart or dashboard really holds up. No coding: you explore realistic business data with clicks, check claims and write honest versions, on the data of Raket Parts, a fictional maker of rocket parts.
You will be able to
- explain what data is and what it can tell you
- describe any table and its traps
- find the base behind every percentage
- spot averages and mix effects that mislead
- rewrite a misleading claim honestly
What data is
Where business records come from, how they become figures, and why they are not the whole truth.
Why we use data
Good questions, evidence for decisions, checking results, and the limits of data.
Reading data tables
What one row means, types, units, codes and the traps in a raw extract.
Percentages, rates and ratios
The base behind every percentage, rates against counts, points against per cent.
Averages, weights and mix effects
Mean or median, weighted averages, and how an average can fall while every group rises.
Reading business metrics
Numerator, denominator, population, period and exclusions behind every KPI.
Comparing periods consistently
Trading days, fiscal weeks and partial periods.
Trends and seasonality
Seasonal peaks, year-on-year views and breaks in a series.
Correlation without cause
Confounders, reverse causation and the evidence a causal claim needs.
Reading charts and dashboards
Encodings, scales, filters left on and the as-of date.
Recognising misleading claims
Base effects, cherry-picked periods and survivorship.
Personal data and privacy duties
What counts as personal data and the duties that follow.
Classification and handling
Labels, storage, sharing and retention.
Security threats in data work
Common threats and what to do about them.
Escalating with evidence
Evidence, impact, options and a named owner.
Policy scope and authority
Inside, outside or unclear: who decides.
Ethical conflicts in data work
Principles at stake and how to respond.
Is this change real?
Signal or noise: run charts and margins.
Does this evidence apply here?
Population, conditions and measures before you borrow a benchmark.
Claim-checking clinics
Check twelve claims in three passes and rewrite each one honestly.
Mid-Path project: review a pack
Write a reviewer note on a Raket Parts business review pack.
Final assessment
A claim dossier, an escalation and a decision record.
Data Literacy / Module 1
What data is
Every order, invoice and sick day at Raket Parts leaves a trace in a computer system. This module shows what those traces are, where they come from, how they become useful figures, and why they never tell the whole story.
Sections
Data Literacy / Module 2
Why we use data
Data does not make decisions. People do. This module shows how good decisions start with a clear question, use data as evidence, and stay honest about what the data cannot tell you.
This module builds on module 1. If you are new to this, start with module 1.
Sections
Data Literacy / Module 3
Reading data tables
Every business figure starts as rows in a table. Learn what one row stands for, what each column holds, which units and codes it uses, and the traps to check before you trust a number.
This module builds on module 2. If you are new to this, start with module 2.
Sections
Data Literacy / Module 4
Percentages, rates and ratios
Most claims in a business review are percentages. Learn to find the base behind each one, tell a share from a rate, and describe a change in points or per cent without misleading anyone.
This module builds on module 3. If you are new to this, start with module 3.
Sections
Data Literacy / Module 5
Averages, weights and mix effects
An average can fall while every group in it rises. Learn when the mean misleads, why averaging averages goes wrong, and how a change in mix explains a figure that looks impossible.
This module builds on module 4. If you are new to this, start with module 4.
Sections
Data Literacy / Module 1 / 1.1
1.1 What data is
Section 1.1 complete
Next: 1.2 Where data comes from.
Data Literacy / Module 1 / 1.2
1.2 Where data comes from
Section 1.2 complete
Next: 1.3 From data to information.
Data Literacy / Module 1 / 1.3
1.3 From data to information
Section 1.3 complete
Next: 1.4 Data is not the whole truth.
Data Literacy / Module 1 / 1.4
1.4 Data is not the whole truth
Section 1.4 complete
Next: the module check: 4 tasks with no hints.
Continue to the module check Recap this section Back to module 1
Data Literacy / Module 1 / Check
Module 1 check
Module 1 complete
Next: Module 2, Why we use data.
Continue to module 2 Try it on your own question Recap module 1 Back to module 1
Data Literacy / Module 2 / 2.1
2.1 Decisions need evidence
Section 2.1 complete
Next: 2.2 Start with a good question.
Data Literacy / Module 2 / 2.2
2.2 Start with a good question
Section 2.2 complete
Next: 2.3 From question to decision.
Data Literacy / Module 2 / 2.3
2.3 From question to decision
Section 2.3 complete
Next: 2.4 What data cannot tell you.
Data Literacy / Module 2 / 2.4
2.4 What data cannot tell you
Section 2.4 complete
Next: the module check: 4 tasks with no hints.
Continue to the module check Recap this section Back to module 2
Data Literacy / Module 2 / Check
Module 2 check
Module 2 complete
Next: Module 3, Reading data tables.
Continue to module 3 Try it on your own question Recap module 2 Back to module 2
Data Literacy / Module 3 / 3.1
3.1 What one row stands for
Section 3.1 complete
Next: 3.2 Columns, types and codes.
Data Literacy / Module 3 / 3.2
3.2 Columns, types and codes
Section 3.2 complete
Next: 3.3 Traps in a raw extract.
Data Literacy / Module 3 / 3.4
3.4 Describe a table
Section 3.4 complete
Next: the module check: 4 tasks with no hints.
Continue to the module check Recap this section Back to module 3
Data Literacy / Module 3 / Check
Module 3 check
Module 3 complete
Next: Module 4, Percentages, rates and ratios.
Continue to module 4 Try it on your own question Recap module 3 Back to module 3
Data Literacy / Module 4 / 4.1
4.1 Every percentage has a base
Section 4.1 complete
Next: 4.2 Shares, rates and ratios.
Data Literacy / Module 4 / 4.2
4.2 Shares, rates and ratios
Section 4.2 complete
Next: 4.3 Points or per cent.
Data Literacy / Module 4 / 4.3
4.3 Points or per cent
Section 4.3 complete
Next: the module check: 4 tasks with no hints.
Continue to the module check Recap this section Back to module 4
Data Literacy / Module 4 / Check
Module 4 check
Module 4 complete
Next: Module 5, Averages, weights and mix effects.
Continue to module 5 Try it on your own question Recap module 4 Back to module 4
Data Literacy / Module 5 / 5.2
5.2 Weighted averages
Section 5.2 complete
Next: 5.3 When the mix moves.
Data Literacy / Module 5 / 5.3
5.3 When the mix moves
Section 5.3 complete
Next: the module check: 4 tasks with no hints.
Continue to the module check Recap this section Back to module 5
Data Literacy / Module 5 / Check
Module 5 check
Module 5 complete
Next: module 6, Reading business metrics, coming soon.
Back to the Path Try it on your own question Recap module 5 Back to module 5
Data Literacy / Module 1 / Recap
Recap card
1.1 What data is
What data is
- Each order, sick day or quality check leaves a trace in a system. That trace is data.
- A good record says who, what, when and how.
- Data can be numbers, dates, codes, text or images.
Common mistake
Treating an opinion as data. "Customers love the webshop" is a claim to test.
Data Literacy / Module 1 / Recap
Recap card
1.2 Where data comes from
Where data comes from
- Each part of the business records data in its own system.
- Systems pass data to each other, often in batches.
Common mistake
Not knowing the source. A figure from the wrong system answers the wrong question.
Data Literacy / Module 1 / Recap
Recap card
1.3 From data to information
From data to information
- Data → information → insight → decision.
- The question decides which summary you need.
Common mistake
Stopping at information. "150 failed" only helps once you compare it.
Data Literacy / Module 1 / Recap
Recap card
1.4 Data is not the whole truth
Data is not the whole truth
- Records can arrive late, go missing or disagree.
- Say "as of" a date.
- When systems disagree, check the evidence.
Common mistake
"It is in the system, so it is true." 18 deliveries have two different dates.
Data Literacy / Module 1 / Recap
Recap card
Module 1: What data is
What data is
- Each order, sick day or quality check leaves a trace in a system. That trace is data.
- A good record says who, what, when and how.
- Data can be numbers, dates, codes, text or images.
Common mistake
Treating an opinion as data. "Customers love the webshop" is a claim to test.
Where data comes from
- Each part of the business records data in its own system.
- Systems pass data to each other, often in batches.
Common mistake
Not knowing the source. A figure from the wrong system answers the wrong question.
From data to information
- Data → information → insight → decision.
- The question decides which summary you need.
Common mistake
Stopping at information. "150 failed" only helps once you compare it.
Data is not the whole truth
- Records can arrive late, go missing or disagree.
- Say "as of" a date.
- When systems disagree, check the evidence.
Common mistake
"It is in the system, so it is true." 18 deliveries have two different dates.
Data Literacy / Module 2 / Recap
Recap card
2.1 Decisions need evidence
Decisions need evidence
- Data tests a belief; it does not decide.
- Counts and rates can tell different stories.
- A decision needs a named owner.
Common mistake
Using data to decorate a decision. Most failures were final checks; the highest rate was elsewhere.
Data Literacy / Module 2 / Recap
Recap card
2.2 Start with a good question
Start with a good question
- Measure, population, period, comparison.
- Vague questions cannot be answered.
Common mistake
"How are sales?" Ask "invoiced sales in Q3 2025 against Q2" instead.
Data Literacy / Module 2 / Recap
Recap card
2.3 From question to decision
From question to decision
- Question, decision, data, check.
- Check the result honestly, including what else changed.
Common mistake
Claiming a cause. Webshop orders rose 53%, but so did all orders.
Data Literacy / Module 2 / Recap
Recap card
2.4 What data cannot tell you
What data cannot tell you
- Only what was recorded is in the data.
- Small numbers swing.
- Split questions into "data" and "ask".
Common mistake
Expecting the data to explain why. Reasons usually need a conversation.
Data Literacy / Module 2 / Recap
Recap card
Module 2: Why we use data
Decisions need evidence
- Data tests a belief; it does not decide.
- Counts and rates can tell different stories.
- A decision needs a named owner.
Common mistake
Using data to decorate a decision. Most failures were final checks; the highest rate was elsewhere.
Start with a good question
- Measure, population, period, comparison.
- Vague questions cannot be answered.
Common mistake
"How are sales?" Ask "invoiced sales in Q3 2025 against Q2" instead.
From question to decision
- Question, decision, data, check.
- Check the result honestly, including what else changed.
Common mistake
Claiming a cause. Webshop orders rose 53%, but so did all orders.
What data cannot tell you
- Only what was recorded is in the data.
- Small numbers swing.
- Split questions into "data" and "ask".
Common mistake
Expecting the data to explain why. Reasons usually need a conversation.
Data Literacy / Module 3 / Recap
Recap card
3.1 What one row stands for
What one row stands for
- Know what one row stands for: "one row is one …".
- Count rows only when one row is the thing you are counting.
Common mistake
Counting rows as people. 1,805 absence rows are 421 employees.
Data Literacy / Module 3 / Recap
Recap card
3.2 Columns, types and codes
Columns, types and codes
- Add numbers, order dates, count text, codes and IDs.
- Read the data dictionary for any column you do not know.
Common mistake
Treating an ID or a code as an amount. Averaging employee numbers means nothing.
Data Literacy / Module 3 / Recap
Recap card
3.3 Traps in a raw extract
Traps in a raw extract
- Check units: 24.00 hours is 3 days.
- Blank means not recorded, not zero.
- Use the latest version of each record.
- Numbers can arrive as text.
Common mistake
Counting both versions. 1,820 raw rows are 1,805 absences.
Data Literacy / Module 3 / Recap
Recap card
3.4 Describe a table
Describe a table
- One row, key, types, units, traps.
- Describe the table before you quote a number.
Common mistake
Mixing codes. One price-review credit note nearly doubles "returns".
Data Literacy / Module 3 / Recap
Recap card
Module 3: Reading data tables
What one row stands for
- Know what one row stands for: "one row is one …".
- Count rows only when one row is the thing you are counting.
Common mistake
Counting rows as people. 1,805 absence rows are 421 employees.
Columns, types and codes
- Add numbers, order dates, count text, codes and IDs.
- Read the data dictionary for any column you do not know.
Common mistake
Treating an ID or a code as an amount. Averaging employee numbers means nothing.
Traps in a raw extract
- Check units: 24.00 hours is 3 days.
- Blank means not recorded, not zero.
- Use the latest version of each record.
- Numbers can arrive as text.
Common mistake
Counting both versions. 1,820 raw rows are 1,805 absences.
Describe a table
- One row, key, types, units, traps.
- Describe the table before you quote a number.
Common mistake
Mixing codes. One price-review credit note nearly doubles "returns".
Data Literacy / Module 4 / Recap
Recap card
4.1 Every percentage has a base
Every percentage has a base
- Percentage = part ÷ base × 100.
- Change the base and the percentage changes.
- Always say "x% of what".
Common mistake
Hiding the base. 65.1% of assessed enquiries reads like 65.1% of all enquiries.
Data Literacy / Module 4 / Recap
Recap card
4.2 Shares, rates and ratios
Shares, rates and ratios
- Share: part of a whole. Rate: events per base. Ratio: one amount per another.
- Compare groups of different sizes with rates.
Common mistake
Comparing counts. Final checks have most fails, but re-inspections fail most often.
Data Literacy / Module 4 / Recap
Recap card
4.3 Points or per cent
Points or per cent
- Points: new % minus old %.
- Per cent change: divide by the old value.
- Give both values.
Common mistake
"Fell by 0.8%". 27.5% to 26.7% is 0.8 points, or 2.9 per cent.
Data Literacy / Module 4 / Recap
Recap card
Module 4: Percentages, rates and ratios
Every percentage has a base
- Percentage = part ÷ base × 100.
- Change the base and the percentage changes.
- Always say "x% of what".
Common mistake
Hiding the base. 65.1% of assessed enquiries reads like 65.1% of all enquiries.
Shares, rates and ratios
- Share: part of a whole. Rate: events per base. Ratio: one amount per another.
- Compare groups of different sizes with rates.
Common mistake
Comparing counts. Final checks have most fails, but re-inspections fail most often.
Points or per cent
- Points: new % minus old %.
- Per cent change: divide by the old value.
- Give both values.
Common mistake
"Fell by 0.8%". 27.5% to 26.7% is 0.8 points, or 2.9 per cent.
Data Literacy / Module 5 / Recap
Recap card
5.1 Mean or median
Mean or median
- Mean: total ÷ count. Median: the middle value.
- A few large values pull the mean up.
- Typical: median. Totals and budgets: mean.
Common mistake
Calling the mean "typical". Mean invoice €5,115, median €1,271.
Data Literacy / Module 5 / Recap
Recap card
5.2 Weighted averages
Weighted averages
- Averaging averages treats every group the same.
- Weight by size, or divide the total by the count.
Common mistake
Averaging averages. €5,320 instead of the real €5,115.
Data Literacy / Module 5 / Recap
Recap card
5.3 When the mix moves
When the mix moves
- An overall average can fall while every group rises.
- Check each group and each group’s share.
- Say what the change does NOT mean.
Common mistake
"Customers spent less". Every segment rose; the mix changed.
Data Literacy / Module 5 / Recap
Recap card
Module 5: Averages, weights and mix effects
Mean or median
- Mean: total ÷ count. Median: the middle value.
- A few large values pull the mean up.
- Typical: median. Totals and budgets: mean.
Common mistake
Calling the mean "typical". Mean invoice €5,115, median €1,271.
Weighted averages
- Averaging averages treats every group the same.
- Weight by size, or divide the total by the count.
Common mistake
Averaging averages. €5,320 instead of the real €5,115.
When the mix moves
- An overall average can fall while every group rises.
- Check each group and each group’s share.
- Say what the change does NOT mean.
Common mistake
"Customers spent less". Every segment rose; the mix changed.