Reporting and Data Management

We often describe what we do for a living as "beating data into submission". Of course this description, ironically a key feature of any good information system, is done with tongue in cheek. The fact remains that moving data through the enterprise - from source systems that are prone to inherent problems to cleansing and integrating the data - is one of the most important jobs that we do for clients.

Data isn’t useful to anyone unless it is the right data. Information has to be something needed, trusted and easily accessible. While capable tools exist to move data from source systems to the data warehouse/ data mart and to cleanse and augment data, we offer complete solutions: Element knows which tools coupled with the right approach are best for your business needs. We know data modeling and can ensure that high quality information is delivered to end users, just in time.

At Element, we employ a source-move-target (SMT) methodology for moving data through the warehouse. This project methodology contains five major components:

Each component in the warehouse can be considered a separate part in this process, enabling the project to be broken down in terms of a series of conversions. The conversions, a set of programs that have logical cohesiveness and are functionally relevant to one another, require mapping of data sources from source - move - target. Using this logical mapping, we usually begin by examining the existing and proposed reporting targets and work backwards to the source systems.

A variety of source-move-target mappings exist within a data warehouse as the physical repository, for example, may be both a target (from the extraction systems) and a source (for the ad-hoc analysis system). The purpose of this methodology is to map the operational data onto how we use it in the data warehouse. This process includes conversion, enrichment and summarization.

In terms of our process for data modeling, we have used the large-scale warehouse approach of both Bill Inmon and Ralph Kimball. For data mart data modeling, we often streamline this process and focus on a rapid warehouse design that includes: Assessment, Requirements, Implementation, Training and Review.