Metrics and Data Analysis

Overview

The Analytics Module provides comprehensive metrics and data analysis capabilities to help you understand and measure your organization's performance across inspections, tasks, issues, and resources. This guide covers all available metrics, their calculations, and how to effectively analyze your data.

Understanding Metrics

What are Metrics?

Metrics are quantitative measurements that help you track, analyze, and understand your organization's performance. They provide objective data points that can be used to make informed decisions and identify areas for improvement.

Metric Categories

Based on the system's metric definitions, metrics are organized into four main categories:

Inspection Metrics

• Count-based metrics: Total numbers and frequencies
• Performance metrics: Scores, rates, and quality measures
• Time-based metrics: Duration and scheduling analysis

Task Metrics

• Completion metrics: Task completion rates and status
• Efficiency metrics: Time and resource utilization

Issue Metrics

• Volume metrics: Issue counts and distribution
• Resolution metrics: Time to resolution and status tracking

Resource Metrics

• Utilization metrics: Resource usage and performance
• Reading metrics: Resource measurements and trends

Available Metrics by Data Type

Inspection Metrics

Count-Based Metrics

• Inspections Total number of inspections conducted
  • Use Case: Track inspection volume and workload
  • Analysis: Compare inspection counts across sites, time periods, or assignees
• Scheduled Inspection Count Number of inspections that were scheduled
  • Use Case: Monitor planning effectiveness
  • Analysis: Compare scheduled vs. actual inspections
• Missed Inspection Count Number of inspections that were not completed on schedule
  • Use Case: Identify compliance issues
  • Analysis: Track missed inspection trends and causes

Performance Metrics

• Average Score Mean score across all inspections
  • Use Case: Measure overall inspection quality
  • Analysis: Track score trends and identify improvement areas
• Inspection Completion Rate Percentage of inspections completed successfully
  • Use Case: Monitor completion efficiency
  • Analysis: Compare completion rates across different factors
• Scheduled Inspection Rate Percentage of scheduled inspections that were completed
  • Use Case: Measure planning accuracy
  • Analysis: Identify scheduling optimization opportunities

Quality Metrics

• Flagged Responses Number of responses that were flagged for review
  • Use Case: Monitor quality control
  • Analysis: Track flagged response patterns and root causes
• Flagged Rate Percentage of responses that were flagged
  • Use Case: Measure response quality
  • Analysis: Compare flagged rates across different templates or questions

Time-Based Metrics

• Average Duration Mean time to complete inspections
  • Use Case: Optimize inspection efficiency
  • Analysis: Identify factors affecting inspection duration

Response Analysis

• Responses Specific response data for detailed analysis
  • Use Case: Deep dive into response patterns
  • Analysis: Analyze response distributions and trends

Task Metrics

Volume Metrics

• Tasks Total number of tasks created
  • Use Case: Track task volume and workload
  • Analysis: Compare task counts across assignees, priorities, or time periods

Performance Metrics

• Task Completion Rate Percentage of tasks completed successfully
  • Use Case: Measure task management effectiveness
  • Analysis: Identify factors affecting completion rates

Issue Metrics

Volume Metrics

• Issues Total number of issues reported
  • Use Case: Track issue volume and trends
  • Analysis: Compare issue counts across categories, priorities, or sites

Resource Metrics

Utilization Metrics

• Resource Sum Sum of resource readings or measurements
  • Use Case: Track total resource usage
  • Analysis: Compare resource consumption across different factors
• Resource Average Average of resource readings
  • Use Case: Measure typical resource performance
  • Analysis: Identify resource performance trends and patterns

Metric Calculations and Formulas

Basic Calculations

Count Metrics

Average Metrics

Rate Metrics

Duration Metrics

Advanced Calculations

Weighted Averages

For metrics that need to account for different weights:

Moving Averages

For trend analysis over time:

Percentage Change

For comparing periods:

Metric Aggregation

Time-Based Aggregation

• Daily: Metrics calculated per day
• Weekly: Metrics aggregated by week
• Monthly: Metrics aggregated by month
• Quarterly: Metrics aggregated by quarter
• Yearly: Metrics aggregated by year

Group-Based Aggregation

• By Site: Metrics grouped by location
• By Category: Metrics grouped by classification
• By Status: Metrics grouped by current state
• By Assignee: Metrics grouped by responsible person

Data Analysis Techniques

Descriptive Analysis

Central Tendency

• Mean: Average value of a metric
• Median: Middle value when data is ordered
• Mode: Most frequently occurring value

Variability

• Range: Difference between maximum and minimum values
• Standard Deviation: Measure of data spread
• Variance: Average squared deviation from mean

Distribution Analysis

• Histograms: Visual representation of data distribution
• Percentiles: Values that divide data into equal parts
• Quartiles: 25th, 50th, and 75th percentiles

Comparative Analysis

Cross-Sectional Comparison

• Site Comparison: Compare metrics across different locations
• Category Comparison: Compare metrics across different categories
• Time Period Comparison: Compare metrics across different time periods

Longitudinal Analysis

• Trend Analysis: Track metric changes over time
• Seasonal Analysis: Identify recurring patterns
• Growth Analysis: Measure rate of change

Correlation Analysis

Metric Relationships

• Positive Correlation: When one metric increases, another increases
• Negative Correlation: When one metric increases, another decreases
• No Correlation: No relationship between metrics

Correlation Strength

• Strong: Correlation coefficient > 0.7
• Moderate: Correlation coefficient 0.3-0.7
• Weak: Correlation coefficient < 0.3

Filtering and Segmentation

Date-Based Filtering

Predefined Periods

• Today: Current day's data
• Yesterday: Previous day's data
• Last 7 Days: Past week
• Last 30 Days: Past month
• Last 90 Days: Past quarter
• Last 4 Weeks: Past 4 weeks
• This Month: Current month
• Last Month: Previous month
• Custom Range: User-defined date range

Time-Based Analysis

• Hourly: Data segmented by hour
• Daily: Data segmented by day
• Weekly: Data segmented by week
• Monthly: Data segmented by month

Categorical Filtering

Site-Based Filtering

• All Sites: Include all locations
• Specific Sites: Filter by individual sites
• Site Groups: Filter by site categories

Status-Based Filtering

• Inspection Status: Completed, In Progress, Pending
• Task Status: To Do, In Progress, Completed, Cancelled
• Issue Status: Open, In Progress, Resolved, Closed

Template-Based Filtering

• Inspection Templates: Filter by specific inspection types
• Task Templates: Filter by specific task types
• Resource Templates: Filter by specific resource types

Performance Indicators (KPIs)

KPI Definition

Key Performance Indicators (KPIs) are specific, measurable metrics that help organizations track progress toward goals and objectives.

KPI Configuration

Setting KPI Targets

• Target Value: Define the desired metric value
• Target Type: Absolute value or percentage
• Time Frame: Period for achieving the target
• Frequency: How often to measure progress

KPI Visualization

• Target Lines: Horizontal lines on charts showing target values
• Progress Indicators: Visual representation of current vs. target
• Alert Thresholds: Visual indicators when metrics fall below targets

Common KPI Examples

Inspection KPIs

• Completion Rate Target: 95% of inspections completed on time
• Quality Score Target: Average inspection score of 85% or higher
• Response Time Target: Average inspection duration of 30 minutes or less

Task KPIs

• Task Completion Rate: 90% of tasks completed within deadline
• Task Efficiency: Average task completion time of 2 days or less

Issue KPIs

• Issue Resolution Time: Average resolution time of 48 hours or less
• Issue Volume: Maximum of 10 open issues per site

Resource KPIs

• Resource Utilization: 80% resource utilization rate
• Resource Performance: Average resource reading within normal range

Trend Analysis

Trend Identification

Upward Trends

• Positive Growth: Metrics consistently increasing over time
• Accelerating Growth: Rate of increase is getting faster
• Sustained Growth: Consistent positive movement

Downward Trends

• Declining Performance: Metrics consistently decreasing over time
• Accelerating Decline: Rate of decrease is getting faster
• Sustained Decline: Consistent negative movement

Stable Trends

• Consistent Performance: Metrics remaining relatively constant
• Cyclical Patterns: Regular ups and downs over time
• Seasonal Variations: Recurring patterns based on seasons

Trend Analysis Techniques

Moving Averages

• Simple Moving Average: Average of last N periods
• Weighted Moving Average: Recent periods weighted more heavily
• Exponential Moving Average: Smoothing technique for trend analysis

Trend Lines

• Linear Trend: Straight line showing overall direction
• Polynomial Trend: Curved line for complex patterns
• Seasonal Decomposition: Separate trend from seasonal patterns

Statistical Analysis

• Regression Analysis: Quantify relationship between variables
• Correlation Analysis: Measure strength of relationships
• Significance Testing: Determine if trends are statistically significant

Comparative Analysis

Benchmarking

Internal Benchmarking

• Historical Comparison: Compare current performance to past periods
• Site Comparison: Compare performance across different locations
• Category Comparison: Compare performance across different categories

External Benchmarking

• Industry Standards: Compare to industry averages
• Best Practices: Compare to recognized best practices
• Competitive Analysis: Compare to competitor performance

Comparative Metrics

Relative Performance

• Percentage Difference: How much better/worse than benchmark
• Ranking: Position relative to other entities
• Percentile: Percentage of entities performing better

Performance Ratios

• Efficiency Ratio: Output per unit of input
• Quality Ratio: Good outcomes per total outcomes
• Productivity Ratio: Output per time period

Data Quality and Validation

Data Quality Assessment

Completeness

• Missing Data: Identify and handle missing values
• Data Coverage: Ensure adequate data for analysis
• Time Gaps: Identify periods with insufficient data

Accuracy

• Data Validation: Verify data correctness
• Outlier Detection: Identify and handle unusual values
• Consistency Checks: Ensure data consistency across sources

Timeliness

• Data Freshness: Ensure data is current
• Update Frequency: Verify data update schedules
• Real-time vs. Batch: Understand data update methods

Data Validation Techniques

Automated Validation

• Range Checks: Verify values are within expected ranges
• Format Validation: Ensure data follows expected formats
• Cross-field Validation: Check relationships between fields

Manual Validation

• Sample Review: Manually review sample data
• Expert Review: Have subject matter experts review data
• User Feedback: Collect feedback from data users

Data Cleaning

Handling Missing Data

• Imputation: Fill missing values with estimates
• Exclusion: Remove records with missing data
• Flagging: Mark missing data for special handling

Outlier Treatment

• Investigation: Understand why outliers occur
• Adjustment: Modify outliers based on context
• Exclusion: Remove outliers from analysis

Best Practices for Data Analysis

Analysis Planning

Define Objectives

• Clear Goals: Define what you want to achieve
• Key Questions: Identify specific questions to answer
• Success Criteria: Define how you'll measure success

Data Requirements

• Data Sources: Identify required data sources
• Data Quality: Ensure data meets quality standards
• Data Access: Verify access to required data

Analysis Execution

Systematic Approach

• Start Simple: Begin with basic analysis
• Iterate: Refine analysis based on findings
• Document: Record analysis steps and findings

Validation

• Cross-check: Verify results with multiple methods
• Peer Review: Have others review your analysis
• Sensitivity Analysis: Test how results change with different assumptions

Results Interpretation

Context Consideration

• Business Context: Understand business implications
• External Factors: Consider external influences
• Historical Context: Compare to historical performance

Actionable Insights

• Clear Recommendations: Provide specific action items
• Priority Setting: Rank recommendations by impact
• Implementation Plan: Outline how to implement changes

Communication

Audience Adaptation

• Technical vs. Non-technical: Adjust detail level for audience
• Visual Aids: Use charts and graphs effectively
• Storytelling: Present findings as a narrative

Regular Reporting

• Frequency: Establish regular reporting schedule
• Consistency: Use consistent formats and metrics
• Evolution: Update reports based on changing needs

Continuous Improvement

Performance Monitoring

• Track Changes: Monitor impact of implemented changes
• Feedback Loop: Collect feedback on analysis quality
• Process Improvement: Continuously improve analysis processes

Skill Development

• Training: Invest in analytical skills development
• Tool Mastery: Become proficient with analysis tools
• Best Practice Sharing: Share learnings across the organization