Key Features

Elldy BI offers a comprehensive suite of features to empower businesses with faster, smarter, and more reliable insights.

• Basket System: Organize your data by business and department for structured analysis.
• Smart Dataset Import: Upload Excel/CSV files with automatic cleaning, validation, and transformation.
• Master Screens: Generate KPIs and dashboards instantly with no manual configuration.
• Custom Screens: Build interactive analysis screens with charts, tables, and advanced visualizations.
• Advanced Operations: Perform quick stats, grouping, trend analysis, correlation, and AI-driven insights effortlessly.
• Automation & Alerts: Automate recurring data processes and get actionable alerts.
• Collaboration & Sharing: Securely share dashboards with teams or publicly for wider access.
• Cloud-Ready: Access your data and analytics anywhere, anytime, without infrastructure overhead.

Workflow Overview

Elldy BI follows an intuitive workflow to help users organize, analyze, and visualize data with minimal effort.

1. Create Basket: Set up business units and departments to organize datasets efficiently.
2. Import Dataset: Upload Excel/CSV files; Elldy BI automatically cleans, validates, and transforms data.
3. View Dataset: Preview and explore data, detect anomalies, and prepare it for analysis.
4. Master Screens: Automatically generate KPIs and dashboards for key metrics and performance tracking.
5. Create Screens: Build custom analysis screens with interactive charts, tables, and visual components.
6. Apply Operations: Perform advanced operations like trend analysis, comparative studies, correlation checks, and AI insights.
7. Share & Collaborate: Distribute dashboards securely across teams or publicly to drive data-driven decisions.

Elldy Variant 1 – Launch Announcement

🚀 Overview

Elldy proudly announces the launch of Variant 1, our first and most advanced release. Designed to seamlessly combine Business Intelligence (BI) and Data Intelligence (DI), this variant empowers organizations to transform raw data into actionable insights faster than ever before.

✨ Key Highlights

• Unified BI & DI – A single platform to manage, analyze, and visualize data.
• AI-Powered Intelligence – Automated insights, anomaly detection, and forecasting.
• Real-Time Dashboards – Instant visualization of KPIs and trends.
• Simplified Data Journey – From raw datasets to meaningful intelligence without coding.

🔮 Future Vision

Variant 1 is just the beginning. Our R&D division is actively working on future variants, exploring next-generation technologies that will further expand Elldy’s capabilities in AI, automation, and decision intelligence.

💡 Why It Matters

• Track performance in real-time.
• Forecast trends with AI-driven accuracy.
• Simplify complex data analysis into clear decision-making.

Basket & Data Management

In Elldy BI, Baskets are like smart folders that hold your datasets. You can upload, organize, and manage multiple datasets within each basket, making it easier to analyze and create dashboards.

📝 What is a Basket?

A Basket is a container for grouping datasets. Think of it as a project folder that keeps related CSV/Excel files together.

Example use cases:

• Sales Basket → Upload monthly sales data across different regions.
• HR Basket → Manage employee, attendance, and performance data.
• Finance Basket → Keep balance sheets, transactions, and forecasts.

⚡ Steps to Use Baskets

1. Create a Basket
   • Navigate to the Basket Management page.
   • Click Create Basket.
   • Enter a Basket Name (e.g., "Sales 2025").
   • Add an optional description.
   ✅ Your basket is now ready to hold datasets.
2. Add Data to a Basket
   • Inside the selected basket, click Add Data.
   • Upload your CSV or Excel file.
   • Elldy BI automatically validates headers, detects data types, and prepares it for analysis.
   • You can upload multiple datasets into the same basket.
3. Actions on Baskets
   • View Details → See all datasets inside the basket.
   • Upload More Data → Add additional files anytime.
   • Delete Basket → Remove the entire basket and its datasets.

🔎 Example Workflow

Create Basket → "E-commerce Sales"
Upload Files → sales_jan.csv, sales_feb.csv, sales_march.csv
View Basket → Check uploaded files.
Run Analysis → Apply operations (Group By, Trend, AI Insights) on selected datasets.

📊 Visualization Ready

Once data is added to a basket:

• You can immediately start charting (bar, line, pie, scatter, etc.).
• Apply operations like Sum, Mean, Trend, Forecast, or AI Insights.
• Use the data in Dashboards & Reports.

✅ Pro Tip: Organize baskets by department or project. For example:

• Marketing Basket → Campaign results
• Finance Basket → Expense reports
• Supply Chain Basket → Inventory & logistics

View Basket

A Basket helps you save and organize your uploaded datasets in one place. You can create separate baskets like “Q1-Sales Basket” or “Marketing Insights” to manage your data more efficiently. This makes it easier to find and work with specific datasets later.

📂 How to View Baskets

1. Click View Basket from the sidebar menu.
2. Browse the list of all created baskets.
3. Select a basket to see its details such as name, business info, and assigned departments.
4. You can add new datasets to the basket, view files inside it, or delete the basket if not needed.

Add Business

Adding a Business allows you to organize your datasets under different companies or departments. For example, you can create separate businesses like “Global Tech Pvt Ltd” or “Marketing Insights Hub” to manage data more efficiently.

🏢 How to Add a Business

1. Go to Settings → Business Setup from the sidebar.
2. Click Add Business.
3. Enter business details such as:
   • Business Name – e.g., FinSmart Analytics, EduWave Solutions, GreenMart Retail
   • Business Contact – e.g., 9876543210, 9123456789
   • Business Address – e.g., Bangalore, India, New York, USA
4. Enter Admin Info:
   • Admin Name – e.g., Ravi Kumar, Anita Sharma, John Smith
   • Admin Contact – e.g., 9812345678, 9500012345
   • Admin Email (optional) – e.g., ravi.kumar@finsmart.com, anita@eduwave.org
5. Click Save to complete setup.

Add Department

Adding a Department helps you organize datasets within a specific business into smaller units or branches. For example, you can create departments like “Sales”, “Finance”, or “Research & Development” under a business for better management.

🏢 How to Add a Department

1. Go to Settings → Add Department from the sidebar.
2. Click Add Department.
3. Select the Business to which the department belongs.
4. Enter department details such as:
   • Department Name – e.g., Marketing, Human Resources, IT Support
   • Status – Active / Inactive
5. Click Save to complete setup.

Import Dataset

Once your dataset is uploaded, the next step is to import it into Elldy for analysis. Importing prepares the data for dashboards, reports, and insights.

📥 How to Import a Dataset

1. Go to Data → Import Dataset from the sidebar.
2. Click Import Dataset.
3. Select the Department where the dataset belongs.
4. Provide the required details:
   • Date – e.g., 17-08-2025
   • Title – e.g., Q2 Sales Report, Customer Feedback 2025
   • Dataset File – choose your CSV or Excel file
5. Click Import to process the dataset.

View Datasets

Once a dataset is imported, it will appear in the View Datasets section. From here, you can manage, analyze, and organize your datasets.

📂 Actions Available

• Summary – View all transformation steps applied (duplicates removed, missing values filled, etc.).
• View – See the final cleaned dataset after processing.
• Use – Confirm the dataset for analysis and move it to Onboarded Datasets.
• Delete – Remove unwanted datasets from the system.
• Merge – Combine multiple datasets into one for consolidated analysis.

📂 Your Datasets

All uploaded datasets are listed here with their details such as:

• Basket – The business dataset group (e.g., Air Quality, Amazon Sales).
• Department – The department under which the dataset was uploaded (e.g., Analysis, Marketing).
• Title – The descriptive title of the dataset (e.g., Air Quality Index, Sales Data).
• Status – Current state (e.g., Completed, Pending).
• Actions – Available operations (Summary, View, Use, Delete, Merge).

Data Cleaning Summary

Elldy provides a detailed Cleaning Summary. This helps you understand the preprocessing applied before analysis.

📊 Dataset Stats Overview

• Initial Rows – Total records before cleaning.
• Removed Duplicates – Duplicate rows eliminated.
• Removed Null Rows – Empty rows dropped.
• Date Conversions – Number of date values standardized.
• Missing Value Handling – Filled using Mean, Median, or Mode.
• Precision Adjustments – Standardized decimal values for consistency.

🗓️ Date Column Transformations

Detected date columns can be transformed for time-based analysis:

• Remove – Exclude column from analysis.
• Split – Break into Year, Month, Day.
• Rename – Assign a custom column name.

🔢 Datatype Enforcements

Elldy automatically infers column data types and enforces consistency:

• Numeric – Ensure valid integers or floats.
• Date – Detect and convert time-based columns.
• Categorical – Encode or rename categories.

🧹 Additional Cleaning Checks

• Removed Invalid Numeric Values – Non-numeric entries skipped for clean calculations.
• Replaced Invalid Strings – Special characters or errors auto-corrected.
• Invalid Emails Cleaned – Wrong email formats blanked or replaced with placeholders.

🛠️ Data Cleaning Activity Log

Every transformation applied to your dataset is recorded, such as:

• Duplicates and nulls removed.
• Date conversions applied.
• Null values filled with median/mode.
• Numeric and categorical type corrections.
• Precision and format adjustments.

📋 Data Transformation Activity

A detailed log of all preprocessing steps is available, giving you full visibility into changes:

• Invalid values removed or replaced.
• Data types standardized.
• String and numeric formats cleaned.
• Columns renamed, split, or encoded as needed.

Master Screen

A Master Screen is your centralized place to save and organize key charts, KPIs, and insights in one view. Think of it as your personalized dashboard where all visual analysis related to a dataset lives.

🎯 Why Use a Master Screen?

• Quickly revisit insights without rebuilding charts.
• Compare performance across periods or departments.
• Track changes over time using saved KPIs.
• Organize multiple datasets into different master dashboards.

🛠️ How to Create a Master Screen

1. Go to Dashboard → Master Screen from the sidebar.
2. Click Create Master Screen.
3. Enter a Master Screen Name – e.g., Q3 Sales Master, Customer Insights 2025.
4. Select the Onboarded Dataset you want to link with this screen.
5. Click Save to create your Master Screen.

📌 Example

If you are analyzing Q3 performance, you can create a Master Screen named "Q3-Sales Master". All charts, KPIs, and insights related to Q3 sales will be stored in one place, making it easy to monitor and compare results.

Auto KPIs in Elldy BI + DI

Once you create a Master Screen in Elldy, the platform automatically analyzes your data and generates Key Performance Indicators (KPIs) tailored to your business needs.

These Auto KPIs save time, reduce manual effort, and provide immediate clarity on what truly matters. Instead of digging into raw numbers, you instantly see ready-to-use metrics aligned with your datasets and business context.

🔑 How Auto KPIs Work

• Data Understanding: Elldy’s intelligence engine scans your dataset structure (sales, profit, quantity, region, time period, etc.).
• KPI Identification: Relevant KPIs are generated automatically — e.g., Total Sales, Average Profit Margin, Monthly Growth, Conversion Rate, Top 5 Products/Regions.
• Visualization: The KPIs are presented in easy-to-read cards and charts inside the Master Screen.
• Dynamic Updates: As data changes or new datasets are imported, KPIs refresh automatically, always keeping you updated.

🌟 Benefits of Auto KPIs

• Instant Insights: No manual calculation or setup.
• Business-Focused Metrics: KPIs are intelligently chosen to match your industry and dataset.
• Time-Saving: Get to decisions faster without spending hours defining measures.
• Consistency: Ensure every team looks at the same trusted metrics.
• Action-Oriented: Combine KPIs with advanced analytics (forecast, anomaly detection, correlation) for decision-ready insights.

✅ With Auto KPIs, Elldy transforms your Master Screen into a powerful command center — giving you the pulse of your business at a glance.

Create Screen

Follow these steps to create a new Screen under your selected Master Screen.

📝 Steps to Create a Screen

1. Go to the Screen menu from the sidebar.
2. Click on Create Screen.
3. Select the Master Screen under which you want to create this Screen.
4. Enter a Screen Name (e.g., “Sales Trends”, “Customer Insights”).
5. Optionally, add a Description to explain the purpose of the Screen.
6. Click Save to create your Screen.

✅ Tips

• Use clear and descriptive names so your team can quickly identify the purpose.
• You can create multiple Screens under one Master to organize different analyses.
• Screens can later be customized with Charts, KPIs, and other insights.

Charts in Elldy BI + DI

Charts are the core of Elldy’s data visualization and analysis. With 25+ chart types, you can represent your data in the most meaningful way, making it easier to identify patterns, compare values, and draw actionable insights.

🎨 Supported Chart Types

• Comparison Charts: Bar, Stacked Bar, Grouped Bar, Column, Stacked Column, KPI Cards
• Distribution Charts: Histogram, Box Plot, Violin Plot
• Trend & Relationship Charts: Line, Area, Stacked Area, Scatter, Bubble, Heatmap
• Composition Charts: Pie, Donut, Funnel, Treemap, Sunburst
• Tabular & Advanced Views: Pivot Table, Sample Table, Gauge, Maps, Custom KPI Boards

⚡ How to Add a Chart

1. Select X and Y Variables: Choose the dimension (X-axis) and measure (Y-axis) from your dataset.
2. Apply an Operation: Group By, Trend Analysis, Forecasting, Correlation, or Anomaly Detection.
3. Choose a Chart Type: Elldy suggests the most suitable chart types automatically.
4. Render the Chart: Click Apply to generate your interactive chart.

🎯 Example

X: Region
Y: Sales
Operation: Group By
Suggested Charts: Bar Chart, Pie Chart, Map View

👉 Click Apply → Interactive chart is displayed in your Screen.

🌟 Why Elldy Charts?

• Smart Recommendations – Chart suggestions based on data type & operation.
• Interactive Visuals – Zoom, drilldown, filter, and highlight.
• Dynamic Updates – Charts refresh automatically as data changes.
• Advanced Insights – Overlay KPIs, forecasts, anomalies, and moving averages directly on charts.

✅ With Charts, Elldy turns raw data into interactive visual stories — helping you understand, explain, and act on insights faster.

Analysis Process Overview in Elldy BI + DI

Elldy makes data analysis simple, smart, and fast. Within 5–10 seconds, you can transform raw data into interactive visual insights.

⚡ How Analysis Works

• Auto X & Y Selection: Elldy auto-detects categorical dimensions (X) and numeric measures (Y).
• Choose an Operation: Apply Quick Stats, Grouping, Trends, Comparisons, Correlations, or AI Insights.
• Select a Chart: Elldy recommends the best chart type for your operation.
• Confirm & Render: Instantly generate an interactive chart.

🎯 Example Workflow

Dataset: Sales Data
Auto Picks → X: Region, Y: Sales
Operation → Group By
Suggested Charts → Bar, Pie, Map
Confirm → Chart rendered instantly.

🌟 Why Elldy Analysis?

• Smart Auto Detection – No guesswork, just insights.
• Wide Range of Operations – From basic stats to AI-driven insights.
• Chart Suggestions – The right visual, every time.
• Ultra-Fast Experience – Insights in seconds.
• Seamless Workflow – Operations + Charts = 1 smooth process.

Add Steps Documentation (Operations Guide)

🔹 General Workflow

• Select Dataset – Upload or choose your dataset.
• Choose Operation – From categories like Quick Stats, Comparative Analysis, Correlation Analysis, Trend Analysis, AI Insights.
• Select Variables (X, Y) – Based on the operation’s requirement (numeric/categorical/date).
• Select Chart Type – Recommended chart types are suggested for each operation.
• Run Analysis – View results in chart + table format.

⚡ Quick Stats

1. Sum
   Variables: x, y (numeric)
   Charts: Bar, Column, Pie
   Example: Total sales per product category
2. Count
   Variables: x, y (categorical/numeric)
   Charts: Bar, Column, Table
   Example: Count orders per region
3. Mean
   Variables: x, y
   Charts: Bar, Line
   Example: Average sales per store
4. Median
   Variables: x, y
   Charts: Bar, Table
   Example: Median salary per department
5. Variance
   Variables: x, y
   Charts: Table, Line (with error bars)
   Example: Variance in monthly expenses
6. Group Head Count
   Variables: x (categorical)
   Charts: Pie, Bar
   Example: Employees per department

📊 Comparative Analysis

1. Top N / Bottom N – Example: Top 5 products by sales
2. % of Total – Example: Market share % of each brand
3. Cumulative Sum (CUMSUM) – Example: Running cumulative revenue over the year

🔗 Correlation Analysis

1. Pearson Correlation – Relationship between Ad Spend & Sales
2. Spearman Correlation – Rank correlation between Satisfaction & Repeat Purchases
3. Kendall Correlation – Agreement between two rating scales
4. Cramér’s V – Association between Region and Product Preference

📈 Trend Analysis

1. Moving Average – Example: 7-day avg of visitors
2. Trend Break Detection – Example: Detect sales drop
3. Time Series Forecast – Example: Forecast next 6 months revenue

🤖 AI Insights

1. Anomaly Detection – Detect unusual spikes
2. Forecast Suggestions – Auto forecast overlay
3. Correlation Warnings – Highlight suspicious metric moves
4. Trend Direction Prediction – Predict stock rise/fall
5. Seasonality Detection – Identify seasonal sales spikes
6. Top Drivers / Influencers – Features influencing churn
7. Productivity Predictions – Predict employee productivity trends
8. Business Risk Warnings – Alert risk of revenue drop

FlexiFilter — User Guide

Elldy BI provides a FlexiFilter system that makes filtering datasets effortless and intuitive. Instead of writing complex queries, you can use human-friendly expressions to filter rows by dates, numbers, text values, and combined conditions — all from a single filter box.

1. Date Filters

• Single Year: 2022
• Month: 2023-07
• Exact Date: 2024-05-10
• Date Range: 2024-05-01 to 2024-05-31
• Year Range: 2022-01-01 to 2023-12-31
• Relative Dates: this week, last month, this year

2. Numeric Filters

• > 1000 → Rows greater than 1000
• < 500 → Rows less than 500
• >= 100 and <= 500 → Range filter
• sales > 50000 → Column-specific filter

3. Text Filters

• country == "India"
• status != "Closed"

4. Global Text Search

• pending → finds rows with pending
• india || china → matches India OR China

5. Combined Filters

• country == "India" and sales > 50000
• status == "Pending" || status == "In Progress"
• (status == "Pending" || status == "In Progress") and sales < 1000

6. Tips & Notes

• Dates follow YYYY-MM-DD format unless using relative keywords.
• Strings with spaces must be quoted → city == "New York"
• Numeric + text filters can be combined.
• Text matching is case-insensitive unless exact case is required.

👉 With FlexiFilter, Elldy BI makes complex data filtering simple, powerful, and intuitive for business users and analysts alike.

Quick Stats Operations in Elldy BI

Quick Stats are the fastest way to generate insights from your dataset. Elldy automatically identifies X (categorical variables) and Y (numeric measures) so you can apply instant calculations.

1. Sum

Description: Calculates the total of a numeric column (Y) for each category (X).

Example: X = Region, Y = Sales → Sum gives total sales per region.

2. Count

Description: Counts how many records fall into each category (X).

Example: X = Product Category → Count gives number of transactions per category.

3. Mean (Average)

Description: Finds the average value of a numeric field (Y) for each category (X).

Example: X = Product, Y = Profit → Mean shows average profit per product.

4. Median

Description: Finds the middle value in your data distribution for each category.

Example: X = City, Y = House Price → Median shows the central house price per city.

5. Variance

Description: Measures the spread of values around the mean.

Example: X = Department, Y = Employee Salary → Variance shows salary distribution difference across departments.

6. Group Head Count

Description: Calculates the total number of categories (X) selected (distinct groups).

Example: X = Country → Group Head Count shows total number of unique countries in dataset.

📘 Group Aggregation & Ranking – Operation Documentation

📊 Trend Analysis — Documentation

Trend Analysis in Elldy BI helps you uncover hidden patterns, forecast the future, and detect important shifts in your data. These operations are automatically applied to time-series data (datasets with a date or time field).

🔎 Summary of Defaults

⚡ Tip: These defaults ensure quick insights without manual setup. Advanced users can still customize parameters for deeper analysis.

📊 Comparative Operations

These operations let you explore cumulative trends, contributions, top/bottom performers, averages, and comparative insights beyond basic statistics.

1. 1. Cumulative Sum (cumsum)

   Adds values sequentially to show a running total.

   How to Use:
   • Select X = Time-series field (e.g., Date, Month).
   • Select Y = Numeric field (e.g., Sales, Expenses).
   • Apply Cumulative Sum to compute progressive totals.
   Example Use Cases:
   • Track cumulative revenue across months.
   • Monitor total expenses accumulated per quarter.
   Suitable Charts: Line, Area

2. 2. % of Total (percent_total)

   Shows each group’s share as a percentage of the overall total.

   How to Use:
   • Select X = Category (e.g., Region, Product).
   • Select Y = Numeric field (e.g., Sales, Profit).
   • Apply % of Total.
   Example Use Cases:
   • Show sales contribution per region.
   • Analyze market share per brand.
   Suitable Charts: Pie, Donut, 100% Stacked Bar

3. 3. Top Performers (Top N) (top_n)

   Displays the top N categories based on a chosen metric.

   How to Use:
   • Select X = Category (e.g., Product, Store).
   • Select Y = Numeric value (e.g., Sales, Revenue).
   • Enter N (e.g., Top 5).
   Example Use Cases:
   • Identify top 5 best-selling products.
   • Find top 3 branches with highest revenue.
   Suitable Charts: Bar, Column, Treemap

4. 4. Bottom Performers (Bottom N) (bottom_n)

   Displays the lowest N categories based on a chosen metric.

   How to Use:
   • Select X = Category.
   • Select Y = Numeric value.
   • Enter N (e.g., Bottom 3).
   Example Use Cases:
   • Find bottom 3 least profitable products.
   • Identify slowest-selling categories.
   Suitable Charts: Bar, Column

5. 5. Running Average (running_avg)

   Calculates a moving average over time or sequence to smooth fluctuations.

   How to Use:
   • Select X = Time or Sequence field.
   • Select Y = Numeric value.
   • Apply Running Average (e.g., 7-day average).
   Example Use Cases:
   • Track 7-day running average of website visitors.
   • Smooth monthly sales trends.
   Suitable Charts: Line, Area

6. 6. Side-by-Side Comparison (side_by_side)

   Compare two categories across the same numeric measure.

   How to Use:
   • Select X1 = First Category (e.g., Region).
   • Select X2 = Second Category (e.g., Year).
   • Select Y = Numeric value (e.g., Sales).
   Example Use Cases:
   • Compare sales across regions between 2023 vs 2024.
   • Compare expenses by department across two years.
   Suitable Charts: Grouped Bar, Clustered Column

7. 7. Contribution to Total (contribution_total)

   Shows how each sub-category contributes to its group’s total.

   How to Use:
   • Select X = Category (e.g., Product).
   • Select Y = Numeric value.
   • Apply Contribution to Total.
   Example Use Cases:
   • Analyze product contribution within each region.
   • Show department contribution to company expenses.
   Suitable Charts: Stacked Bar, Stacked Column

8. 8. Change Over Period (change_period)

   Calculates the difference or growth rate between time periods.

   How to Use:
   • Select X = Date/Period field.
   • Select Y = Numeric value.
   • Apply Change Over Period.
   Example Use Cases:
   • Show sales growth from Jan to Feb.
   • Calculate quarterly expense changes.
   Suitable Charts: Line, Bar (Growth % per period)

Correlation Analysis

Correlation Analysis in Elldy BI measures the strength and direction of relationships between two variables. It helps identify whether variables move together, oppositely, or independently.

🔹 Supported Methods

🔹 When to Use Each

📘 AI Insights — Documentation

Artificial Intelligence (AI) powered insights go beyond descriptive and diagnostic analysis, enabling predictive and prescriptive analytics. These operations automatically detect hidden patterns, anomalies, and risks, helping you make smarter business decisions.

1. 🔍 Anomaly Detection

Key: anomaly_detection
Required Fields: y (1 variable)
Description: Detects unusual spikes, drops, or irregular patterns in your data series. Useful for quality monitoring, fraud detection, and KPI anomalies.

Example:
Variable: Revenue
Insight: "Revenue spiked 80% higher than normal on July 15 — potential campaign impact."
Visualization: Line chart with anomalies highlighted in red.

2. 📈 Forecast Suggestions

Key: forecast_suggestions
Required Fields: date, y
Description: Provides predictive forecasts with confidence intervals for future planning.

Example:
Variables: Date, Sales
Insight: "Sales are projected to grow 12% over the next 3 months."
Visualization: Forecast line with shaded confidence interval.

3. ⚠️ Correlation Warnings

Key: ai_correlation_alerts
Required Fields: y_list (2–5 variables)
Description: AI highlights unexpected correlations that may affect outcomes.

Example:
Variables: Marketing Spend, Leads, Sales
Insight: "Strong correlation (0.82) between Marketing Spend and Sales detected."
Visualization: Correlation heatmap.

4. 🧭 Trend Direction Prediction

Key: trend_direction
Required Fields: date, y
Description: Predicts whether your metric will trend upward, downward, or stay stable.

Example:
Variable: Customer Retention
Insight: "Retention rates are predicted to decline by 5% in the next quarter."
Visualization: Line chart with AI-predicted arrows.

5. 📊 Seasonality Detection

Key: seasonality_detection
Required Fields: date, y
Description: Identifies repeating cycles (weekly, monthly, yearly).

Example:
Variable: E-commerce Orders
Insight: "Order volume peaks every Friday and dips on Mondays."
Visualization: Seasonal decomposition plot.

6. 🚀 Top Drivers / Influencers

Key: top_drivers
Required Fields: target, x_list
Description: Detects which features have the strongest impact on outcomes.

Example:
Target: Profit
Features: Marketing Spend, Discount, Customer Visits
Insight: "Profit is most influenced by Customer Visits (35%), followed by Discount (25%)."
Visualization: Bar chart ranking top drivers.

7. ⚡ Productivity Improvement Predictions

Key: productivity_forecast
Required Fields: date, y
Description: Predicts efficiency changes over time for workforce or operations.

Example:
Variable: Units Produced per Hour
Insight: "Productivity expected to improve by 8% in the next 2 weeks."
Visualization: Forecast curve with improvement markers.

8. 🛑 Business Risk Warnings

Key: business_risk_alerts
Required Fields: date, y
Description: AI flags risks like sales declines, bottlenecks, or stockouts.

Example:
Variable: Inventory Levels
Insight: "Inventory dropping below safety threshold by next month — risk of stockouts."
Visualization: Line chart with red risk zones.