Most data analysis software is bought for analysts and abandoned by everyone else. Easy Pivot is built the other way round — a self-service analysis workspace where a business user connects a data source, drags dimensions and measures to explore, and shares what they found, without writing SQL and without filing a request.
When every question needs a query written by someone else, the answer arrives after the decision. Self-service means the person with the question can answer it.
A lot of data analysis software is really an IDE with a chart on top. If the interface starts with a query editor, most of the business will never open it.
Ad hoc work done in a personal file cannot be reviewed, reused or scheduled. Analysis that cannot be shared is analysis that gets redone.
Build cross tables, charts and pivot views by dragging dimensions and measures. No query language, no formulas, no scripting.
A dataset defines the queries, dimensions, measures and aggregation rules once. Every chart built on it agrees on what a metric means.
Derive new measures from existing fields, and fix the granularity a measure is computed at with Level of Detail expressions.
Relational databases over JDBC, Elasticsearch, HTTP APIs returning JSON, and Excel, CSV or plain text files — so the analysis starts where the data already is.
Anything you explore can be saved, added to a dashboard, scheduled for email delivery, or embedded in another application.
Row-level and column-level permissions mean self-service does not mean everyone sees everything. Central data models stay reusable and consistent.
Business users answer their own questions against a governed dataset, instead of queuing for a custom report each time.
When a number looks wrong, drill from the dashboard into the underlying rows and find out why, in the same tool.
Finance, sales and operations analyse their own domains while reading from shared definitions, so the numbers reconcile.
The analysis workspace is a drag-and-drop designer: dimensions and measures are dragged onto rows, columns and the data area. Anyone who has used a spreadsheet pivot table will recognise the interaction.
A dataset is the semantic layer — the query, dimensions, measures and aggregation rules. A chart is a view built on top of it. Defining metrics in the dataset is what keeps every chart consistent.
Yes. A database dataset is defined by a SQL query, and variables can be used inside it. The point is that SQL is optional for the people analysing the data, not that it is unavailable.
The analysis runs against your live data source rather than a file, so it is not capped by spreadsheet row limits and does not go stale. Results are shareable, schedulable and governed by permissions.
ChatBI — querying your data in plain English — is coming soon. It is not released yet, and every page on this site describes it as coming soon.