1. BI Studio
  2. Introduction to BI Studio
  3. Structure


Some key functions of the tool are:

  • Standardisation to a consistent set of search values
  • Cleaning through validity checks to remove or modify problematic data
  • Transposition, typically by denormalisation and reorganisation into a dimensional model to optimise reporting
  • Creation of surrogate keys which are new values applied to similar data from different source systems.

This component allows you to Convert or Transform the extracted data into the correct form so that it can be placed in the BI Studio Datawarehouse or standardised database. This process is crucial and allows to connect business management systems designed for other purposes (ERP, platforms Ecommerce, CRMetc.) to be analysed with the Analysis Panels.


Data quality

The first type of transformation process is the determination and qualification of various data as high quality, complete and acceptable. In this case, the system must ensure that the various data points are complete, adhere to the expected schema, and do not contain data that is unreadable or corrupted and inconsistent. Another type of data quality check uses past data patterns associated with a data set to determine if there have been unexpected changes in the data just received compared to past arrivals. If such changes are noted, the data quality can be flagged as suspect.


Business quality

The second type of transformation process ensures that the data is deemed appropriate according to the business quality requirements of the intended data analysis. Here, the data is inspected and analysed for completeness from a business relevance perspective and, if key elements that are necessary to drive business workflows are found to be missing, the data is flagged as suspect.


Business logic

The third type of transformation process ensures that data is processed to take the form required for the business purpose of data analysis. Here, the data can be aggregated, grouped, filtered, sampled, processed through algorithms to produce a transformed dataset that is ready to support the intended business use case.


Because the same data can be used for multiple business use cases, transformations typically have a one-to-many relationship, and a dataset is transformed multiple times across multiple business logics to produce multiple transformed datasets.



When data is written to the target database or data warehouse, BI Studio DataWarehouse.

This process is integral to business intelligence because it means that data from multiple sources can be brought together in a way that provides important information; regardless of the original format or location. To be successful in this endeavour, it starts with data mapping, where the relationship between the source establishes instructions on how the data should be transformed before arriving at the designated location.

How can we help?