Documentation

Analysis Stages

Understanding Fluxjiva's Analysis Pipeline

Fluxjiva uses a structured, 9-stage analysis pipeline to transform your raw data into actionable insights. Each stage builds upon the previous ones, creating a comprehensive analytical narrative.

The 9 Analysis Stages

Stage 1: Data Quality Assessment

In this initial stage, Fluxjiva evaluates the quality of your data by:

  • Identifying missing values
  • Detecting outliers and anomalies
  • Checking for inconsistencies in data formats
  • Assessing column data types
  • Evaluating data distribution

The system generates a data quality report that highlights potential issues and recommends remediation steps.

Stage 2: Automated Data Cleaning

Based on the quality assessment, Fluxjiva automatically:

  • Handles missing values through imputation or removal
  • Standardizes data formats
  • Corrects data type inconsistencies
  • Removes or flags outliers
  • Normalizes text fields (case, whitespace, etc.)

You can review and adjust the automated cleaning steps before proceeding to the next stage.

Stage 3: Exploratory Data Analysis

In this stage, Fluxjiva explores your data to uncover patterns and relationships:

  • Generates descriptive statistics
  • Creates distribution visualizations
  • Identifies correlations between variables
  • Detects trends and seasonality in time series data
  • Segments data into meaningful groups

The insights from this stage inform the subsequent analysis steps.

Stage 4: Feature Engineering

Fluxjiva automatically creates new features that enhance the analytical value of your data:

  • Derives new variables from existing ones
  • Creates interaction terms
  • Performs dimensionality reduction
  • Generates time-based features
  • Develops domain-specific indicators

These engineered features often reveal insights that aren't apparent in the raw data.

Stage 5: Feature Selection

To optimize analysis performance, Fluxjiva selects the most relevant features:

  • Evaluates feature importance
  • Removes redundant variables
  • Identifies the most predictive features
  • Balances model complexity and performance
  • Considers domain-specific relevance

This focused approach improves both the accuracy and interpretability of the analysis.

Stage 6: Model Selection and Training

Fluxjiva automatically selects and trains appropriate models based on your data and analysis goals:

  • Evaluates multiple model types
  • Performs cross-validation
  • Tunes hyperparameters
  • Compares model performance
  • Selects the optimal model(s)

The system prioritizes both performance and interpretability in its model selection.

Stage 7: Model Evaluation and Validation

Fluxjiva rigorously evaluates the selected models:

  • Assesses performance on holdout data
  • Calculates relevant metrics
  • Performs sensitivity analysis
  • Checks for overfitting
  • Validates against business objectives

This ensures that the insights generated are reliable and actionable.

Stage 8: Insight Generation

In this stage, Fluxjiva transforms analytical results into clear, actionable insights:

  • Identifies key drivers and influences
  • Quantifies relationships and effects
  • Generates predictions with confidence intervals
  • Creates scenario analyses
  • Develops domain-specific recommendations

These insights are presented in business-friendly language, not technical jargon.

Stage 9: Visualization and Storytelling

Finally, Fluxjiva presents the insights in a compelling, visual narrative:

  • Creates appropriate visualizations for each insight
  • Develops an interactive dashboard
  • Generates a narrative summary
  • Highlights key findings and recommendations
  • Provides context and explanations

This makes the insights accessible and actionable for all stakeholders.

Human Touchpoints

While Fluxjiva's analysis pipeline is automated, you can intervene at key points:

  • Review and adjust data cleaning steps
  • Provide domain knowledge for feature engineering
  • Guide model selection based on business requirements
  • Validate insights against real-world experience
  • Customize visualizations and dashboards

These touchpoints allow you to combine the efficiency of AI with human expertise.

Next Steps

To learn more about how to interact with each analysis stage, check out our Tutorials section.