Business Intelligence

 

Business Intelligence does not have to be complex. CTS believes that large data warehousing efforts should be divided into several small iterations.

 

This approach, coupled with requirements gathering from data consumers, reduces complexity and achieves quick wins by controlling the growth of the DW/BI system, incorporating lessons learned more quickly, and increasing the quality of the end solution. This iterative approach also allows time for data governance policies to mature, thereby increasing the likelihood of achieving "a single version of the truth". Our experience implementing BI solutions using our Sentinel Business Intelligence methodology has contributed to our high success rate. From understanding business requirements to deploying solutions, we are equipped to add value successfully at any stage in the BI lifecycle.

Business Intelligence at CTS falls into three specialties:

 

Our database service team focuses on your database solution's underlying data storage.

 

The CTS database service team provides leadership in the areas of database modeling (dimensional and third normal form), development (T-SQL, PL-SQL, etc.), and storage (indexing, partitioning, etc.). This team has worked with Microsoft SQL Server, Oracle, and IBM DB2 databases.

Data integration (DI), also known as extract, transform, and load (ETL), is the hub of activity of all data warehousing projects.

 

Our Data Integration Team ensures that each solution has a solid foundation of data mapping processes and metadata management. Data Integration efforts also have to conform to any existing master data management or data quality processes, or help implement these procedures.

The reporting and analytics team deals with the consumption of data.

 

The focus our Reporting and Analytics team is the delivery of information. Users may need dashboards, scorecards, reports, ad hoc, or OLAP cubes for analysis purposes. The Reporting and Analytics team gives consideration to performance indicators, timely delivery of data, and the most appropriate visualization for the data and the user's analysis.