ontologies for business intelligence
Excel
Timbr enhances Excel by enabling governed, high-performance data access and consistent analytics.
Through ODBC/MDX connectivity and semantic caching, it replaces laborious SQL and JOINs with a unified semantic model powered with relationships.
Analysts can use Natural Language or familiar interfaces, enriched with governed measures and hierarchies, for seamless cross-tool reporting.
Key Capabilities
Convenient
Access
Connect to Timbr via ODBC or MDX using PivotTables, no SQL or JOINs required.
Consistent Metric
Definitions
Use the same semantic model shared across all BI tools for consistent definitions and KPIs.
Controlled
Access
Enforce row-level and role-based access controls directly within the semantic layer.
Access
Big Data
Query big data with confidence, Timbr pushes compute to the source and handles caching.
Remain in
Excel
Stay in Excel, no need to jump between tools, download extracts, or wait on data teams.
Access Virtualized
Data
Avoid workbook bloat and crashes with virtualized, on-demand queries.
Enjoy NLQ
for Data Retrieval
Use the Timbr NLQ Add-in to ask questions in plain English and populate data instantly.
Warrant
Accuracy
Autocomplete, controlled vocabulary, and semantic validation ensure accurate, secure answers.
Benefit from
Self-Serve
Empower business users to self-serve without learning SQL or data models.
What Every User of Excel
Should Know
OLAP in Excel with Timbr
Timbr brings modern OLAP capabilities to Excel, without the need for legacy cubes or complex setup. Excel’s PivotTables become truly powerful when connected to Timbr’s semantic model.
With Timbr, Excel users can:
- Explore cubes and hierarchies using familiar PivotTable workflows
- Access governed, reusable measures directly from the semantic layer
- Drill down and slice data across dimensions—just like OLAP cubes, but fully virtualized
- Leverage MDX support for backward compatibility and NLQ for forward-thinking self-service
Natural Language Query Add-In
Timbr’s NLQ Excel add-in empowers users to fetch data from any data source connected to Timbr (i.e Databricks, Snowflake, Fabric, etc.) through natural language in seconds:
- Install via Excel Add-ins, search “Timbr NLQ”.
- Connect using environment URL and user token, then select your knowledge graph.
- Ask a question like “Find sales by region for Q1 2025” and get autocomplete help.
- Run the query: Timbr converts it into optimized SQL, retrieves results, and populates a new sheet.
Why Timbr is Essential
for Big Data Access in Excel
Pulling big data into Excel is technically possible, but not built for scale, consistency, or governance. Without Timbr, Excel users must stitch together raw data from multiple sources, write manual joins, and hope their workbooks don’t break under volume. Timbr solves this by virtualizing access to distributed data and wrapping it in a governed semantic model built for Excel users.
| Without Timbr | With Timbr |
|---|---|
| Manual connections via ODBC/Power Query | One governed semantic endpoint for all sources |
| Complex joins and flat tables | Virtualized concepts with relationships and hierarchies |
| Inconsistent KPIs across workbooks | Single source of truth and shared measures, cubes, and definitions across teams |
| Risk of performance issues with large data extracts | Lean, pre-aggregated query results optimized by semantic caching and pushdown |
| No source federation | Query across multiple data sources as a single unified model |
| No security beyond workbook-level protections | Role-based access down to row-level within the semantic layer |
| Requires technical skills | NLQ Excel Add-in allows business users to ask questions in plain English |
Timbr turns Excel into a secure, governed, high-performance window into all your data, no matter where it lives, without compromising the simplicity users expect.
How it Works
Timbr exposes a governed semantic model via ODBC, MDX, or the NLQ API. Excel connects directly to this model, whether through pivot tables (MDX/ODBC) or the NLQ add-in. Queries are translated into optimized SQL, which are executed on your data platform or served from the semantic caching layer. The result: secure, consistent metrics and hierarchy-based analysis with minimal Excel-side complexity.
