Creating Datasets in Analyze: Your Guide to Data Visualization

Created by datHere Support, Modified on Thu, 15 Feb 2024 at 03:33 PM by datHere Support

Introduction

Welcome to Analyze, the powerful business intelligence tool from datHere. This guide is designed to help you create datasets in Analyze, a crucial step for generating insightful data visualizations and dashboards.

1. Understanding Datasets in Analyze

A dataset in Analyze is a structured representation of your data, essential for crafting charts and dashboards. It acts as a bridge between the raw data in your database and the visual insights you wish to create.

1.1 Physical Dataset

  • Definition: Directly corresponds to a table or view in your database.
  • Use-case: Ideal for straightforward scenarios where data needs little to no transformation.

1.2 Virtual Dataset

  • Definition: A more flexible dataset that can involve complex SQL queries, joining multiple tables, or applying transformations.
  • Use-case: Best suited for advanced analytics where data from multiple sources needs to be consolidated or extensively processed.

2. Creating a Dataset

Before creating a dataset, ensure your database is connected to Analyize. This process involves coordination with your system administrator or IT department.

2.1 Accessing the Dataset Module

  • Navigate to the Analyze dashboard.
  • Select the ‘Datasets’ option from the main menu.

2.2 Adding a Physical Dataset

  1. Select ‘New Dataset’: Choose the option to create a new dataset.
  2. Database Selection: Choose the database from the connected databases list.
  3. Table Selection: Select the table you wish to turn into a dataset.
  4. Dataset Configuration:
    • Name your dataset for easy identification.
    • Optionally, add a description for clarity and future reference.

2.3 Adding a Virtual Dataset


  1. SQL Editor: Use the SQL editor to craft your query. This may involve:
    • Joining tables.
    • Applying filters or transformations.
    • Selecting specific columns.
  2. Dataset Configuration:
    • Name the dataset.
    • Provide a detailed description, including information about data sources and transformations used.

2.4 Customizing Datasets

  • Metrics and Dimensions: Define the key metrics (quantitative data) and dimensions (qualitative data) relevant to your analysis.
  • Data Availability: Specify which fields should be available for analysis.
  • Formatting: Set formats for dates and numeric fields according to your regional or business standards.

2.5 Validation and Saving

  • Review the dataset configuration for accuracy.
  • Save the dataset, making it available for use in visualizations and dashboards.

Best Practices

  • Consistent Metric Definitions: Maintain uniformity in metrics across all datasets.
  • Regular Updates: Keep datasets updated to reflect changes in the underlying data.
  • Collaboration: Encourage team members to share insights and feedback on datasets.

Conclusion

Datasets are foundational to the functionality of Analyze in datHere. By following these guidelines, you can leverage the full potential of Analyze for creating powerful, data-driven visualizations. For further assistance, visit our support center or join our community forum.


This document is intended as a basic guide. For more complex scenarios or specific use cases, consult our detailed documentation or reach out to our support team.

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