Conduit vs Google Looker Studio

While Conduit and Google Looker Studio both create dashboards, they employ distinct methodologies to achieve this.

The primary difference between Conduit and Google Looker Studio lies in how you configure your reports.

In Looker Studio, you can define SQL queries, apply filters, adjust dimensions in the UI, and customize widgets within a report.

In Conduit, you utilize natural language to define your requirements, and AI generates the report accordingly. For instance, you could simply state, "Take my data.csv, display rows for clients, columns for each month, and format ROI as a percentage," and the AI fulfills your request. Then, with a click of the "Add to Dashboard" button, you've created a table view on your dashboard.

Looker tends to have higher user requirements, expecting familiarity with data manipulation concepts in general and SQL specifically, which may not be ideal for business users. From my experience, successful Looker deployments were typically designed by data analysts rather than business users.

Another limitation of Looker is its lack of ETL capabilities. If data source consolidation is necessary, another tool must be employed, significantly increasing the overall cost compared to Conduit.

Conduit, on the other hand, is designed with business users in mind, requiring no SQL knowledge. Even if your organization already utilizes Looker, Conduit remains valuable as it empowers business users to create ad hoc reports without relying on time-consuming reconfigurations of reports or extensive communication with data analysts.

However, it's important to note that Conduit provides less control over the visual aspects of reports. "Polished" reports are built by exporting data to a spreadsheet and applying formatting (such as colors and fonts) there.