Creating business intelligence dashboards can be an overwhelming task. This task can be prone to error when dashboards are created from multiple data sources, each in a different format.
Modelling data sources simplifies this task, allowing users to clean, format and prepare the data before building dashboards. Modelling data sources enables users to take the raw data, clean it up, and prepare it for building dashboards. Users can change the name of the data source or the columns, remove unnecessary columns, and change the format of the data. While modelling the data, any changes users make will carry over to everything built, so users only have to make those changes once.
The Data Modeller brings significant efficiency gains for the users and simplifies the visualization building experience. The Data Modeller reduces duplication of work effort, improving consistency between visualizations and within dashboards.
Also, the Data Modeller helps users can make sense of messy JSON data. Imported JSON data is often challenging to read. By modelling it, users can take that tree structured format and convert it into a more familiar tabular format, helping to eliminate the noise in JSON data and only deliver what matters.
Merging modelled data sources enables users to bring their information from different services together. Users can consolidate complementary modelled data sources to create a modelled data source that provides a complete version of their data and is easier to understand.
Users can take two data sources with the merge feature and join them to fill in the gaps. For example, our sales data only has an employee ID number. By matching that employee ID number with our database of employees, we can then import and bring in our employee names to make our data make a lot more sense. Take the time to model your data before you build. You'll thank yourself later.
