Abstract/Summary/Outline of proposed Session: (This will be the critical information used in determining whether or not your proposal is accepted for inclusion-- please be thorough)
All the data that managers need for day to day decision making, planning, strategic data analysis, etc are right there in the Banner database, but most managers will never find it. The few who do can cripple the system with some of their queries. So what is the best way to turn this data into decision support information?
Data warehousing can be the key that unlocks the immense value of the data held by Banner.
Here is an outline of the process we are following at
1. Develop an understanding amongst senior administrators of the potential role of IT and data warehousing in achieving the institution's goals.
2. Choose the right project team.
3. Take appropriate training, and/or hire selected consultants.
4. Choose strategically important subject areas, (ie areas that are linked to the Strategic Plan), that have high visibility and fast return. (remember the 80-20 rule).
5. Choose the data repository, data warehousing tools, and desktop tools.
6. Start small, using a phased approach, but within the framework of a system-wide architecture.
7. Evolve the data marts iteratively, constructing the architected data warehouse as you go.
Agenda Description for your Session: (This is the description which will appear in the conference agenda and on the web - Maximum of 100 words - descriptions longer than 100 words will be edited by SCT)
Your managers are probably suffering from a data flood and an information drought. The Banner database holds all the data that managers need for day to day decision making, planning, strategic data analysis, but few will ever find it. So what is the best way to turn this data into decision support information?
Data warehousing can be the key that unlocks the immense value of the data held by Banner. This presentation describes an iterative approach for modeling strategically important subject areas in data marts as part of a plan to build an enterprise-wide architected data warehouse.