I am working on a data management tool called Ottava. Essentially, it's a tool that enable users to input data in a format resembling a pivot table, allowing them to conduct data analysis without the need for data transformation. For instance, users can create a gradebook with student names in the row header and subject in the column header.
The tool automatically converts this layout into database (unpivot), enabling users to perform aggregations such as SUM or AVERAGE without having to write formulas. Because the structure designed by the user often carries significance, such as organizing dates into columns with a hierarchical structure like Year -> Quarter -> Month.
Users can organize their data with grouped rows/columns and insert aggregations. Simultaneously, Ottava can derive insights from the structure metadata and propose various types of charts for data exploration. Subsequently, users can select the charts they are interested in, choose datasets, or drill down into the charts, providing further insight for Ottava to offer more precise chart suggestions based on user interactions.
Unlike AI with the capability to autopilot, our objective is to build a Mazda MX-5 for data analysis: providing users with both enjoyment and control while exploring data.
Thank you for your information. I love all the posts by Joel, he always keeps it inspiring and entertaining. Currently, at Ottava, we are in the initial stages of verifying essential features, such as pivoted data entry and the integrated workflow combining data manipulation and analysis. While the option to export to Excel is not yet available, it is certainly planned for future roadmap.
The tool automatically converts this layout into database (unpivot), enabling users to perform aggregations such as SUM or AVERAGE without having to write formulas. Because the structure designed by the user often carries significance, such as organizing dates into columns with a hierarchical structure like Year -> Quarter -> Month.
Users can organize their data with grouped rows/columns and insert aggregations. Simultaneously, Ottava can derive insights from the structure metadata and propose various types of charts for data exploration. Subsequently, users can select the charts they are interested in, choose datasets, or drill down into the charts, providing further insight for Ottava to offer more precise chart suggestions based on user interactions.
Unlike AI with the capability to autopilot, our objective is to build a Mazda MX-5 for data analysis: providing users with both enjoyment and control while exploring data.
https://ottava.io/