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Overview

Transform your data into actionable insights and visualizations. Shieldbase Reporting uses AI to analyze datasets and automatically generate relevant charts, graphs, and analytical insights.
Reporting works best with datasets in tabulated data format. The cleaner the dataset, the more accurate the analysis.

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Getting Started

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Video Tutorial - Identify Insights

💡 Tip: Adjust video playback speed using the gear icon (⚙️) in the video player. We recommend 0.5x speed for detailed tutorials.

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Create Your First Report

1

Start New Report

Click New Report in the Reporting section
2

Select Data Sources

Choose one or multiple datasets from the Library to analyze
3

Generate Insights

Enter a prompt in Analysis to generate insights with relevant charts in Visualization
4

Review and Refine

Review the generated insights and refine your prompts for better results

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Pro Tip: Insight Discovery

Not sure what insights to generate? Use this powerful prompt: 
Suggest what insights can be generated from this dataset. Generate the prompt to generate the insights and suggest the chart type to visualize the data. Generate a table for the response.

Column 1: Type of data (descriptive, diagnostic, predictive, prescriptive).
Column 2: Question to be asked in the form of a prompt.
Column 3: Chart type

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Video Tutorial - Insight Discovery

💡 Tip: Adjust video playback speed using the gear icon (⚙️) in the video player. We recommend 0.5x speed for detailed tutorials.

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Types of Analysis

  • Descriptive
  • Diagnostic
  • Predictive
  • Prescriptive
What happened?Summarize historical data to understand past performance:
  • Sales totals by quarter
  • Customer demographics
  • Product performance metrics
  • Regional distribution

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Visualization Types

Choose the right chart for your data:

Bar Chart

Compare categories, show rankings, display frequencies

Line Chart

Show trends over time, track progress, display patterns

Pie Chart

Show composition, display proportions, highlight shares

Scatter Plot

Show correlations, identify clusters, find outliers

Heatmap

Display intensity, show patterns, visualize matrices

Area Chart

Show cumulative values, track volume changes, display stacked data

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Best Practices

Data Quality is Critical: The cleaner the dataset, the easier it is for AI to understand the context, and thus the more accurate the analysis.

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Data Preparation

1

Clean Your Data

Remove duplicates, fix inconsistencies, handle missing values
2

Structure Properly

Use consistent column names, proper data types, clear headers
3

Validate Accuracy

Verify data accuracy before analysis
4

Document Context

Include metadata about data sources and definitions

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Visualization Guidelines

Match Chart to Data: Choosing the right visualization type for the right data is essential to quickly understand key insights. Data visualization may not show if the chart type is incompatible with the dataset.
Comparison: Bar charts, column charts Trends: Line charts, area charts Composition: Pie charts, stacked bars Distribution: Histograms, box plots Correlation: Scatter plots, bubble charts Geographic: Maps, regional charts

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Integration Options

Reporting can be used in Dashboard, Chatbot, and Workflows for comprehensive automation.

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Use in Dashboards

1

Create Reports

Build individual reports for different metrics
2

Add to Dashboard

Combine multiple reports in a single dashboard view
3

Organize Tabs

Group related reports into logical sections
4

Share Access

Provide dashboard access to stakeholders

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Use in Workflows

Automate report generation:
  • Schedule regular reports
  • Trigger based on data updates
  • Distribute via email
  • Archive for compliance

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Use in Chatbots

Enable conversational analytics:
  • Answer data questions
  • Generate on-demand reports
  • Provide insights interactively
  • Explain trends and patterns

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Common Use Cases

  • Sales Analytics
  • Marketing Reports
  • Operations Metrics
  • Financial Analysis
  • Revenue trends by product/region
  • Sales team performance
  • Customer acquisition costs
  • Pipeline conversion rates
  • Forecast accuracy

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Advanced Features

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Multi-Dataset Analysis

Combine multiple data sources for comprehensive insights:
  1. Select multiple datasets from the Library
  2. AI automatically identifies relationships
  3. Generate unified insights across sources
  4. Create consolidated visualizations

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Custom Prompts

Examples of effective analysis prompts: 
"Show me the top 10 performing products by revenue with monthly trend"

"Identify seasonal patterns in customer behavior and suggest optimal marketing periods"

"Compare this quarter's performance with the same quarter last year and highlight key differences"

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Troubleshooting

  • Check data format compatibility
  • Verify chart type matches data structure
  • Ensure dataset has required columns
  • Try a different visualization type
  • Review data quality and completeness
  • Provide more specific prompts
  • Check for data inconsistencies
  • Verify date formats and ranges
  • Reduce dataset size for initial analysis
  • Use data sampling for large datasets
  • Optimize queries before analysis
  • Consider data aggregation

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Pro Tips

Start Broad

Begin with high-level insights, then drill down into specifics

Iterate Prompts

Refine your prompts based on initial results for better insights

Combine Views

Use multiple chart types to tell a complete data story

Regular Updates

Schedule automated reports for consistent monitoring
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