Citation :

Ankit Bansal, 2024. "Maximizing ThoughtSpot Capabilities for Pre and Post Analysis" ESP International Journal of Advancements in Computational Technology (ESP-IJACT)  Volume 2, Issue 4: 26-32.

Abstract :

In today's data-driven landscape, organizations increasingly rely on robust analytics tools to make informed decisions. ThoughtSpot, with its AI-driven search and analytics capabilities, offers a powerful platform for data visualization and analysis. However, users often encounter challenges when conducting pre and post analysis, particularly in comparing metrics over distinct time periods. This paper explores strategies to maximize ThoughtSpot's capabilities for effective pre and post analysis by addressing common limitations, such as data aggregation, visualization techniques, and user interface constraints. By implementing best practices for data preparation, leveraging advanced visualization options, and utilizing ThoughtSpot's unique features, organizations can achieve deeper insights and clearer comparisons. This study aims to provide actionable recommendations that empower users to enhance their analytical workflows and derive meaningful insights from their data, ultimately driving better decision-making processes.

References :

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Keywords :

ThoughtSpot, AI-powered Analytics, Liveboards, Business Intelligence, Data Visualization, Real-Time Analytics, Natural Language Processing, Self-Service Analytics, Pre Post Analysis.