Trinity also specializes in helping clients implement information delivery systems to allow users to access the data in the warehouse. These systems include decision support tools, data mining and analytic tools. We start by implementing a data model that ensures data across all functional areas is integrated to support cross-functional analyses. In addition, we ensure all reporting and access requirements are met. This may include fixed-frequency static reports; ad-hoc reports; dynamic, multidimensional queries; Internet/intranet application interfaces; and data mining.
The physical design of the Data Warehouse is a key component to ensure efficient access to data to produce useful and effective queries. Trinity's team of professionals is familiar with current design techniques to build a framework to support high-performance data access. This process includes the analysis and translation of business requirements into the design of fact and dimension tables. Our experienced professionals have the skills necessary to build denormalized views of the data to produce a ‘star-schema’ to support the most complex business reporting requirements.
Data Acquisition: Extract, Transform, and Load (ETL)
An integral part of any Data Warehouse solution is the data extract, transform and load (ETL) process. Trinity's team of professionals place a strong emphasis on high performance ETL and data aggregation to deliver effective data acquisition solutions. This includes mapping, cleansing, transforming, and aggregating data using parallel technology tools to build industrial-strength ETL processes that accommodate high data volumes from disparate sources. We identify the best sources for data elements, reconstructing data when required and deploying the most appropriate tools to retrieve the data from its primary sources. Through the cleansing process, we enhance data quality by ensuring data accuracy, type, and consistency.
Our ETL capabilities are alos used in our data conversion and middleware practices. The same skills to analyze data, map from source to target systems, find data anomalies, and perform data transformations are used in the same way in:
- Design and build of the data warehouse and data marts;
- Data conversion,
- And message-based middleware data transformations to enable interoperability across internal and external systems.