13  Introduction to Diagnostic Analytics

Diagnostic Analytics

Diagnostic analytics is the second stage in the analytics maturity model, following descriptive analytics and preceding predictive and prescriptive analytics. While descriptive analytics answers “What happened?”, diagnostic analytics focuses on “Why did it happen?” by identifying relationships, patterns, and root causes in data.

By using data mining, correlations, drill-down analysis, and statistical tests, diagnostic analytics helps businesses and researchers uncover causal relationships and hidden insights.

13.1 Importance of Diagnostic Analytics

  • Identifies the root causes of business outcomes.
  • Helps optimize operations by understanding key influencers.
  • Supports better decision-making through data-driven insights.
  • Provides a bridge between descriptive analytics (what happened?) and predictive analytics (what will happen?).

13.2 Techniques Used in Diagnostic Analytics

Drill-Down Analysis
  • Breaks down aggregated data into smaller subcategories to find specific causes.
  • Example: If total sales drop, drill-down analysis can reveal whether a specific product category, region, or customer segment is responsible.
Data Mining
  • Identifies hidden patterns in data using machine learning and AI.
  • Example: A company might discover that high customer churn is linked to late customer service response times.
Correlation and Regression Analysis
  • Correlation Analysis measures the strength of relationships between two variables.
  • Regression Analysis identifies how one or more independent variables influence a dependent variable.
  • Example: Analyzing how marketing spend impacts sales revenue.
Hypothesis Testing
  • Uses statistical tests like the t-test, chi-square test, and ANOVA to validate assumptions.
  • Example: Testing whether customer complaints are significantly higher in one region compared to others.
Time Series Analysis
  • Examines data over time to detect trends and anomalies.
  • Example: Website traffic drops after a website redesign, revealing UX issues.

13.3 Example Use Cases

Business Intelligence

  • Retailers analyze customer purchase history to find out why certain products perform better in specific seasons.
  • Banks detect fraud patterns by analyzing transaction data.

Healthcare

  • Hospitals use diagnostic analytics to find why patient readmission rates are high by identifying risk factors.
  • Drug companies analyze clinical trial data to identify side effects and treatment effectiveness.

Human Resources (HR)

  • HR departments use diagnostic analytics to understand why employee turnover is increasing.
  • Surveys and performance data are analyzed to correlate engagement levels with attrition rates.