Enhanced Results Explorer And Cube Operations
Our customers are continuously dealing with larger and larger data. As data sizes have increased so have the sizes of results from BusinessOptics models. Over a certain size investigating the results of models on a row level basis becomes impractical and the modeller needs a quick and flexible way to investigate them at a more aggregate level, easily checking for invariants and outliers.
Our solution to this problem is the results explorer and it’s associated cube operations. Cube operations are simplistic yet powerful operations on query results which are run entirely on BusinessOptics infrastructure. Because the cube operations are applied in the cloud, they can be scaled to extremely large datasets with trillions of rows. These only apply to the results of a query, the actual query is not re-run each time. So they can be quickly applied, changed and reordered.
The cube operations include:
- Filtering for rows using our expression language, or simple Drill Downs to only show the data you want
- Aggregating over dimensions with example aggregation functions including:
- sum, product
- min, max
- mean, median
- standard deviation
- Sorting columns (with multi-column hierarchical sorting)
- Hiding columns
Upon applying cube operations you will see them appear in a stack on the right hand side of the cube. The parameters of the cube operations can be adjusted in this stack and the cube operations will be reapplied. This gives the user immediate feedback, allowing for results to be quickly explored and investigated.
The results can simultaneously be viewed in a variety of graphical ways, such as line, bar and bubble charts as well as maps. These act as a lite version of the possible visualisation options available in the dashboarding system, and are used to quickly and visually search for insights while modelling.
Should you subsequently download a CSV, the net result of all the operations is visible – so hidden columns will not be included, rows will be sorted in the same manner that’s specified in the query result explorer and all of the aggregation and filtering operations are applied. This makes it easy for the modeller to export results to other tools or their operational systems.
These extension to the cube operations both in utility and scalability provide for a quicker modelling experience especially at scale. Allowing modellers to rapidly tackle problems that would be far outside their capabilities using traditional tools.
Interested in seeing the solution for yourself? Schedule a demo today!