​
1. Introduction

Data analysis views in Xtraleap refer to the various ways data can be visualized, summarized, and presented for analysis and decision-making purposes. These views help data analysts and stakeholders understand and interpret complex data by providing different perspectives, highlighting patterns, trends, and relationships in the data. This documentation aims to provide a comprehensive overview of data analysis views in Xtraleap, their types, the role they play, and best practices for designing and working with them.

​
2. Types of Data Analysis Views

There are several types of data analysis views, each offering a unique perspective on the data:

  • Tabular Views: Tabular views present data in a structured table format, with rows and columns displaying individual data points and their attributes.

  • Aggregated Views: Aggregated views summarize data by applying aggregation functions, such as sum, average, or count, and grouping data based on specific attributes.

  • Time Series Views: Time series views display data points over time, allowing analysts to identify trends, patterns, and seasonality in the data.

  • Cross-Tabulation Views: Cross-tabulation views display data in a matrix format, enabling the analysis of relationships between multiple categorical variables.

  • Hierarchical Views: Hierarchical views display data in a tree-like structure, representing parent-child relationships and nested categories.

​
3. The Role of Data Analysis Views in Xtraleap

Data analysis views play a crucial role in Xtraleap by:

  • Simplifying Data Interpretation: Data analysis views make complex data more accessible and easier to understand by presenting it in a structured, summarized, or visual format.

  • Highlighting Patterns and Trends: Data analysis views help analysts identify patterns, trends, and relationships in the data that may not be apparent from raw data alone.

  • Facilitating Decision-Making: Data analysis views provide stakeholders with actionable insights that support data-driven decision-making.

  • Enabling Data Exploration: Data analysis views allow analysts to interactively explore and filter data, revealing deeper insights and driving further analysis.

​
4. Creating and Customizing Data Analysis Views

To create and customize data analysis views, follow these steps:

  • Select a Data Source: Choose the data source or dataset to be visualized and analyzed in the data analysis view with the power of SQL.

  • Choose the Appropriate View Type: Determine the most suitable data analysis view type based on your analysis objectives and the nature of the data.

  • Define View Parameters: Specify the parameters for the chosen view type, such as aggregation functions, groupings, time intervals, or chart types.

  • Iterate and Refine: Continuously refine and update the data analysis view as needed, based on feedback from stakeholders or changes in the data.

​
5. Best Practices for Designing Data Analysis Views

When designing data analysis views, consider the following best practices:

  • Clarity and Simplicity: Design data analysis views that are clear, simple, and easy to understand. Avoid clutter and unnecessary elements that may distract from the key insights.

  • Consistency: Use consistent design elements, such as naming standards across different tables used in joins for views to maintain a cohesive identity.

  • Context: Provide context for your data analysis views by including proper business terminology that explain the data and any relevant calculations.

  • Focus on Key Dimensions and Measures: Highlight the most important elements by prioritizing the most relevant data points, aggregations, or calculations.

​
6. Sharing and Collaborating with Data Analysis Views

Data analysis views can be shared and collaborated on in various ways:

  • Share: Share the views with stakeholders via email or push notification to enable them build their content via self-service.

  • Version Control: Implement version control systems to track changes and maintain a history of data analysis views, facilitating collaboration and iterative development. This feature is under development.

Data Landing ZonesJob Scheduler