Introduction

In the current era of data inundation, precision in data analysis cannot be overstated. Enter Inqqa, an AI-driven insights tool crafted to aid professionals in bridging the gaps in surveys, sentiment analysis, and market research. Its simplicity lies in a trio of actions: connect your data, highlight what’s crucial, and reveal insights that captivate. This guide walks you through the art of choosing text fields for analysis using Inqqa, with Excel files serving as our illustrative backdrop.

Uploading an Excel file into Inqqa is the first step in workflow automation. Imagine a file teeming with columns: identifiers, ratings, and text elements like review text, review name, review text, review name, employee feedback, and customer feedback. Inqqa endeavors to spot text data columns on its own. Yet, this isn’t always flawless, and misplaced columns can occur. Users can swiftly correct this by selecting relevant text columns, ensuring the analysis zones in on the data fields of importance, facilitating data insights and better decision-making. Moreover, users can fine-tune their analysis by opting for columns that align with their specific research aims, thus refining the pertinence of their insights.

The Power of Customization

Inqqa offers a flexible approach to column misclassifications. When a column is incorrectly placed under filters rather than text, users can simply drag and drop columns to their rightful place. This function is pivotal for tailoring data analysis workflows, letting professionals interpret fields to suit their exact needs. For instance, a review ID, though text-based, might be more beneficial as a filter than an analysis field. Additionally, when tackling vast datasets, prioritizing columns that will deliver the most insightful results through natural language processing is crucial. This pliability allows users to hone their data analysis process across various scenarios and data types.

The platform’s intuitive interface allows users to seamlessly choose which columns from their data set to focus on. Whether dealing with customer reviews, employee surveys, or any other text-based data, Inqqa gives users the control to tailor their text analysis process.

Dynamic Data Management

The drag-and-drop feature is a standout aspect of Inqqa, giving users the ability to reorganize their data fields with ease. This capability enables shifting columns between text analysis and filtering categories dynamically to improve workflow efficiency. In situations where columns carry multiple unique identifiers, it’s wise to evaluate their use as filters, as an overload of entries can complicate the analysis. Reviewing selected fields periodically to ensure alignment with analysis goals is advantageous, sustaining a streamlined and effective analytical method.

Navigating Extensive Datasets

Additionally, handling large Excel files within Inqqa becomes more manageable through browser zoom functions. Users can zoom in and out with straightforward keyboard and mouse commands, facilitating easy navigation and column selection even in elaborate spreadsheets. This capability ensures that even with intricate spreadsheets, drag-and-drop management remains approachable and efficient, optimizing workflow automation. New users are urged to delve into these features by signing up for a free trial, boosting their data analysis prowess with Inqqa’s intuitive platform.

By mastering these tools, users can notably enhance the accuracy and efficiency of their data analyses, leading to more impactful insights and decisions. Furthermore, the ease of view adjustment and data management empowers users to confidently and precisely tackle the most challenging datasets.

Our tutorial is designed to make your interaction with Inqqa AI as seamless as possible. Whether you’re new to text analysis, sentiment analyse, or a seasoned researcher, our platform provides the tools you need to achieve accurate, insightful results.