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Ad Hoc Query & Analysis

Ad Hoc Query and analysis uses relational OLAP tools to query a database to answer a particular question. Using slice-and-dice techniques, the entire database can be interrogated down to the lowest level transaction.

Ad Hoc Query and Analysis enables true investigative analysis of enterprise data, where 'what is happening' cannot be determined soley by predefined comparisons within a cube. The user may run a parameterized, or answer a prompted set of questions, to create an ad hoc report.

From this ad hoc report, the user can drill down into underlying reports until they find the answer to their question.

The results of the analysis, and the reports can be sent to other parties to collaborate on determing the impact of the issue and identify an appropriate resolution.


Appropriate tools are a critical factor in any Performance Management System and using the right tool for the right purpose, is a key enabler.

Ad Hoc Query and Analysis is designed for BI power users to interrogate all enterprise data using any
combination of data. It extends from the limits of pre-defined reports to allow users to assemble any possible combination of data into a report.

Some cube-based BI applications only allow users to create ad hoc reports against the small subsets of data available in their proprietary cube databases. This is not true ad hoc reporting.

Full Ad Hoc Query and Analysis is founded on Relational OLAP technology that allows users to perform full OLAP analysis against an entire relational database.

Typical features include:

  • Parameter-driven Reporting Using Guided Analysis – reports are compiled by answering a series of questions [prompts] prior to running the report
  • Drill Anywhere – using OLAP functionality to surf to any part of the database following the business model of the data warehouse.
  • OLAP Analysis Against the Entire Database – to conduct report manipulations.
    Filtering Sets – used to segment data according to different business criteria to refine the data set.
  • User-defined Data Grouping – to refine the business model without causing any changes to the database or the overall business model.

NEXT: Data Mining


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