An enterprise data warehouse combines data from multiple sources across an organization into one harmonious, comprehensive database that is easily manipulated. It separates the work of processing transactions from the analysis workload.
At BGSU, enterprise level transactions are completed in the financial sector using FMS and many student-related transactions are processed using CSS. As designed by each system, the data related to these transactions are stored in their respective databases. With Falcon Info, a means of extracting the data from these systems and putting it into the Falcon Info database will be established. Falcon Info will then be the resource for obtaining system wide data for analysis, forecasting, and decision-making.
The overall goal of an enterprise warehouse is to create a definitive version of an organization’s business data for use in reports, analysis, and decision-making. While data can be stored in a database, a well-designed enterprise data warehouse deals with data by:
- Staging the data: raw data is extracted from the applications that support BGSU’s business processes, it is organized and standardized in order to be ready for integration.
- Integrating the data: the data is further arranged and logically stored in a single database in order to provide efficient access to uniform data.
- Accessing the data: data contained in the resulting data warehouse is available at the desktop level for use in analyzing, forecasting and planning. Users can use reports and dashboards to see the “big picture” view this coherent data provides.
Large amounts of data already exist in the PeopleSoft systems (CSS and FMS) and data from these systems will be integrated into the data warehouse. As identified, data from other BGSU transactional systems will also begin to be integrated into Falcon Info.
Falcon Info data will be updated at specified intervals, typically once a day and as the available data grows it will provide a historical perspective that is valuable in longer-term analysis.
There are thirteen data marts that comprise the three data warehouse system. The instillation process will initially focus on three key data marts that house a majority of the product’s delivered reports. These three data marts are the Student Records Mart, Workforce Profile Mart, and General Ledger Mart.
The entire list of data marts within each warehouse are:
- Campus Solutions Warehouse
- Student Records Mart
- Admissions & Recruiting Mart
- Student Financial Services Mart
- Campus Community Mart
- Institutional Research Mart
- Human Capital Management Warehouse
- Workforce Profile Mart
- Learning & Development Mart
- Compensation Mart
- Recruiting Mart
- Financials Warehouse
- General Ledger Mart
- Payables Mart
- Receivables Mart
- Enterprise Service Automation Mart
The initial three data marts were created in the Development Environment on 10 February, 2012. Work is currently being conducted to establish these marts in the following environments:
Quality and Assurance: 15 April, 2012
Pre-Production: 11 May, 2012
Production: 30 June, 2012
The remaining 10 data marts are scheduled to be completed in the production environment by 1 September, 2012.
A data mart is a subsection of a data warehouse that deals with specific information. Similar to the sections of a book, a data mart contains related information on the same subject. For example as a part of FalconInfo there will be a Campus Solutions Warehouse that will contain a Student Records Data Mart, an Admissions & Recruiting Data Mart, a Student Financial Services Data Mart etc.
The Data governance Council is responsible for establishing the policies and procedures under which the Data Warehouse will operate as well as enforcing standards of operation. The council will approve a data use and protection policy, produce a common data dictionary, make decisions regarding data security, and prioritize decision support and report production.
A data dictionary stores all the data elements used within an organization along with their definitions. As different systems may use or represent data in different ways, the data dictionary provides the roadmap to how the data is stored and represented within the data warehouse.