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Documentation Index

Fetch the complete documentation index at: https://docs.shieldbase.ai/llms.txt

Use this file to discover all available pages before exploring further.

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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

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

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Types of Data Visualization

Reporting allows you to transform structured data into insights and visualizations in a report. After selecting one or more datasets from the Library and generating an analysis, Shieldbase renders different chart types based on your data and prompt.
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 forced to pair with an incompatible dataset.
Chart TypeBest Used For
TableDisplay raw or aggregated data in rows and columns. Best for detailed views, reference tables, and drill-downs.
Bar ChartCompare values across categories (e.g., revenue by region, tickets by status). Supports vertical or horizontal bars.
Stacked Bar ChartShow the composition of each category (e.g., revenue by region broken down by product line) while still comparing totals across categories.
Line ChartVisualize trends over time (e.g., daily active users, monthly sales). Ideal for time-series data and monitoring changes.
Area ChartSimilar to line charts but with the area under the line filled. Useful for showing cumulative values and emphasizing volume over time.
Stacked Area ChartShow how multiple series contribute to a total over time (e.g., traffic by channel over months).
Pie ChartShow the proportion of each category as a percentage of a whole (e.g., market share, budget allocation).
Donut ChartA variation of the pie chart with a hollow center, often used to highlight a key metric in the middle while still showing category proportions.
Column ChartA vertical variation of the bar chart, often used interchangeably, to compare discrete categories or time buckets.
Scatter PlotShow the relationship between two numeric variables (e.g., marketing spend vs. revenue). Useful for detecting correlations and outliers.
Bubble ChartA scatter plot with an extra dimension represented by bubble size (e.g., x = revenue, y = profit margin, size = number of customers).
HistogramShow the distribution of a single numeric variable by grouping values into bins (e.g., deal sizes, response times).
HeatmapUse color intensity to represent values in a matrix (e.g., performance by region and product, activity by hour and weekday).
Funnel ChartRepresent staged processes such as sales funnels or onboarding flows, illustrating drop-offs between stages.
Radar (Spider) ChartCompare multiple metrics across different dimensions (e.g., feature scores, department KPIs) on a radial layout.
Gauge ChartHighlight a single key metric, often compared against a target or threshold (e.g., SLA adherence, utilization rate).
Tornado ChartA specialized bar chart with bars extending left and right from a central axis, typically used in sensitivity or scenario analysis to compare the relative impact of different variables on an outcome.
Gantt ChartVisualize tasks or activities over time, showing start and end dates, durations, and overlaps. Ideal for project timelines, roadmap planning, and tracking dependencies.
Control ChartPlot a metric over time with upper and lower control limits to monitor process stability and variation. Useful in quality control to detect anomalies or trends that signal process changes.
Org ChartShow hierarchical relationships between people, roles, or entities in a tree-like diagram. Helpful for visualizing organizational structure or ownership relationships.
Sankey DiagramVisualize flows and their relative magnitudes between stages or categories (e.g., traffic sources to pages, budget allocations to spending categories). The width of each flow is proportional to its value.
Checklist MatrixDisplay items (e.g., features, requirements, tasks) against a set of categories or entities, indicating presence, completion, or status in a grid format. Useful for audits, feature comparisons, and tracking implementation coverage.

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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

  • 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