Introduction
How do the most innovative companies ensure that they are developing the right capabilities for success? Many organizations set ambitious innovation goals, but without the necessary skills, processes, and leadership styles, these goals remain unachievable.
This is where Correlation Analysis in My360’s Capability Analyzer comes in. By identifying the capabilities most strongly linked to innovation success, organizations can align their strengths with their strategy—or adjust their strategy to better fit their existing strengths.
In this article, we’ll explore:
✅ What Correlation Analysis is and why it matters.
✅ How My360 calculates correlation and why benchmark data is essential.
✅ How to interpret correlation values and apply insights to real-world strategy.
🚀 Let’s dive in!
What is Correlation Analysis in My360?
Correlation Analysis measures the statistical relationship between two factors—in this case, an organization’s innovation capabilities and its strategic objectives.
It helps answer critical questions like:
Do the organization’s existing capabilities support its innovation strategy?
If not, which capabilities should be strengthened?
Are there alternative strategies that better fit the company’s strengths?
By analyzing these relationships, My360 helps leaders make data-driven decisions instead of relying on guesswork.
How Correlation is Calculated: The Benchmark Filter vs. Primary Filter
🚨 One of the most important concepts in Correlation Analysis is understanding which data is used for the calculation.
Primary Filter vs. Benchmark Filter (Secondary Filter) – What’s the Difference?
Filter Type | Purpose |
Primary Filter | Defines the organization(s) being analyzed. It provides the mean, min, and max scores of innovation capabilities for the selected company. |
Benchmark Filter (Secondary Filter) | Defines the dataset used for correlation calculations. It determines how capabilities correlate across a broader dataset (e.g., industry, region, or top innovators). |
📌 Key Insight: Correlation is NOT calculated from the Primary Filter. It is calculated exclusively from the Benchmark Filter (Secondary Filter) to find patterns across many companies.
If we calculated correlation using only one company, the analysis would be statistically meaningless. Instead, we need a large dataset (at least 500+ innovation assessments) to identify real patterns.
The Mathematics Behind Correlation: Pearson’s Formula
In My360, correlation is calculated using Pearson’s correlation coefficient (r):
r=∑(Xi−Xˉ)(Yi−Yˉ)∑(Xi−Xˉ)2∑(Yi−Yˉ)2r=∑(Xi−Xˉ)2∑(Yi−Yˉ)2∑(Xi−Xˉ)(Yi−Yˉ)
Where:
XiXi = Individual values of capability scores.
XˉXˉ = Mean of capability scores in the benchmark filter.
YiYi = Individual values of strategy, leadership style, or other analyzed factors.
YˉYˉ = Mean of strategy scores in the benchmark filter.
This formula calculates how strongly a given innovation capability is associated with a particular strategy or leadership style.
Why Correlation Requires a Large Dataset?
✅ A small dataset produces unstable correlation values because there isn’t enough variation to find meaningful patterns.
✅ A benchmark filter of 500+ assessments ensures reliable correlation results.
✅ If the dataset is too small, random fluctuations will appear as false correlations.
Interpreting Correlation Values in My360
Correlation (r) Value | Interpretation |
+1.0 | Perfect positive correlation – Two variables always increase together. |
+0.5 to +0.9 | Strong positive correlation – High alignment between the variables. |
+0.2 to +0.4 | Moderate correlation – Some relationship, but not absolute. |
0 | No correlation – The two variables are not related. |
-0.2 to -0.4 | Moderate negative correlation – When one increases, the other decreases slightly. |
-0.5 to -0.9 | Strong negative correlation – When one increases, the other decreases significantly. |
📌 Why is 0.4 Considered High in the Social Sciences?
In physics, correlations can be close to 1.0 because relationships follow strict laws (e.g., gravity).
In business and innovation research, many factors influence success, so correlations rarely exceed 0.7.
A correlation of 0.4 or higher is considered meaningful in organizational studies.
Why You Should Exclude Non-Innovators
Including non-innovators in correlation analysis weakens statistical accuracy.
Metaphor: Studying Bicycling Behavior
Imagine you are researching what factors make people faster cyclists.
You collect data on experienced cyclists and analyze their training, bike type, and fitness levels.
❌ Now imagine including people who don’t ride bicycles.
Their data would distort the results because their speed is always 0.
This makes it harder to identify meaningful patterns among actual cyclists.
📌 In the same way, including non-innovators makes it harder to determine which capabilities drive innovation success.
How to Exclude Non-Innovators in My360?
✅ In the benchmark filter, tick the box "Exclude non-innovators" to remove companies that score too low in innovation capability.
Real-World Example: Correlation Analysis for Innovation Strategy
Scenario
A tech startup wants to determine whether to focus on Incremental Innovation or Radical Innovation.
Correlation Results from My360:
Capabilities highly correlated with Radical Innovation:
✅ Open Innovation (r = 0.45)
✅ Risk-Taking Culture (r = 0.41)Capabilities highly correlated with Incremental Innovation:
✅ Process Optimization (r = 0.44)
✅ Cost Efficiency (r = 0.38)
Interpretation
The startup has strong Process Optimization scores but low Risk-Taking scores.
This suggests Incremental Innovation is a better fit for their current strengths.
If they want to pursue Radical Innovation, they must build Risk-Taking and Open Innovation capabilities.
📌 Decision: Adjust the innovation strategy OR develop new capabilities to support a different strategy.
How to Apply Correlation Analysis to Strategy
✅ Align leadership styles with innovation goals – If Agile Leadership is strongly correlated with success, but your organization scores low, you may need leadership training.
✅ Identify capability gaps – If a capability has high correlation but low internal scores, it should be a priority for development.
✅ Compare different innovation strategies – If your current approach doesn’t align with your strengths, consider an alternative strategy.
Conclusion
Correlation Analysis in My360 provides deep insights into how capabilities align with innovation success. By understanding correlation values, using the right benchmark filters, and excluding non-innovators, organizations can:
✅ Ensure their strategy is supported by strong capabilities.
✅ Identify and fix gaps in leadership, processes, and culture.
✅ Make data-driven decisions that lead to sustainable innovation success.