Correlation is a statistical relationship between two variables. It shows whether the values tend to move together, move in opposite directions or show no clear pattern.
Correlation
Correlation describes how two variables move in relation to each other.
Quick facts about correlation
Relationship between variables
Correlation helps show whether two values tend to move together, move in opposite directions or show no clear relationship.
Positive correlation
A positive correlation means two variables usually increase or decrease together.
Negative correlation
A negative correlation means one variable tends to increase while the other decreases.
Not causation
Correlation can show a relationship between variables, but it does not prove that one variable causes the other.
How correlation works
Correlation is used to understand patterns between two variables. In analytics, it can help teams see whether changes in one value are associated with changes in another value, such as marketing spend and sales, response time and customer satisfaction, or price and demand. Correlation can usually be understood in three basic ways:
- Positive correlation: two variables tend to move in the same direction.
- Negative correlation: two variables tend to move in opposite directions.
- No clear correlation: the variables do not show a consistent relationship.
- Correlation without causation: two variables may be related without one directly causing the other.
This makes correlation useful for exploring patterns, but it should be interpreted carefully. A strong correlation can help teams ask better questions, but further analysis is often needed before making decisions about cause and effect.
Why correlation matters in BI reporting
Correlation is useful in business intelligence because it helps teams compare behavior across different metrics. A report might show whether higher support response times are linked with lower satisfaction, whether campaign activity moves with sales, or whether product usage increases alongside customer retention. These patterns can make reports more useful because they show relationships, not just isolated numbers.
In Power BI, correlation can be explored through charts, measures and visual comparisons that help users understand how metrics move together. When those reports are shared with customers, partners or internal teams, it is important that the context is clear. A correlation can guide discussion, but it should not be presented as proof of causation unless the analysis supports that conclusion.
Skald BI helps organizations share existing Power BI reports through a secure branded portal. When reports include correlation analysis, Skald BI supports a clearer delivery experience, so each audience can access the insights meant for them with the right structure, context and control.
Use cases
See how different types of organisations use Skald BI to share Power BI securely with employees, customers and partners.
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Frequently asked questions
Positive correlation means two variables usually move in the same direction. For example, if higher marketing spend is often associated with higher sales, the two may be positively correlated.
Negative correlation means two variables usually move in opposite directions. For example, if higher prices are associated with lower demand, the two may be negatively correlated.
Does correlation mean causation?
No. Correlation does not prove that one variable causes another. It only shows that two variables have a relationship or pattern in the data.
How does correlation relate to Skald BI?