ZogoTech Data Warehousing Process

ZogoTech's approach to data warehousing differs from traditional data warehousing projects. ZogoTech's process has proven to have:

Far less costs

Higher success rates

Fewer unknowns

Quicker turnarounds

Because ZogoTech has been focused exclusively on higher education since its inception, we already have years of experience with higher education requirements. Where most companies have to spend hundreds of hours trying to gather requirements and create reports, Estudias includes dozens of standard, customizable reports that have been useful in other institutions.

Traditional Data Warehousing Projects		ZogoTech
Spend a lot of time gathering requirements Form committees to look at requirements Very hard (impossible?) to reach consensus No clear end Develop reports from scratch Months (Years?) before first report Big bang release		Give people something immediately, then customize Start with reports that are working at other colleges / universities, customize Start small, show quick success Initially focus on technical issues rather than requirements - clearly defined goals Weeks Grow organically
Proprietary extraction tool Custom programming No clear end Months Rewrite if source system changes Data put into a black box, not accessible		Integrate with text file downloads Clearly defined file formats Straightforward text file export or ODBC connection Weeks Only change the generation of text files if source system changes Access to data at all stages (part of ZogoTech's Open Architecture)
Proprietary tools Locked in to one vendor Difficult to customize How manage security? Limited access to data (only through vendor's tool)		ZogoTech's Open Architecture -- Standard Tools, Standard Protocols Microsoft SQL Server Microsoft Analysis Services Access through ODBC using SAS, SPSS, Microsoft Access, Excel, etc. Open source where available Security can be tied to Windows login IDs, groups (LDAP / Active Directory)
Post-download data cleansing Custom tools to clean data after download Reports generated from source system (i.e. IPEDS) continue to be incorrect / inaccurate Lots of custom logic and consulting hours		Pre-download cleansing (where possible) Clean as much as possible on source system Tools to empower institution to identify/fix data errors on their own without expensive contractors Users get same, correct results whether using data warehouse or source system Flexible, extensible tools to clean remaining data using standard SQL statements See: Improving Data Quality Using Estudias