In many organizations IT people want to huddle and work out a warehousing plan, but in fact
The purpose of a DW is decision support
The primary audience of a DW is therefore College decision makers
It is College decision makers therefore who must determine
Decision makers can’t make these determinations without an understanding of data warehouses
It is therefore imperative that key decision makers first be educated about data warehouses
Once this occurs, it is possible to
Elicit requirements (a critical step that’s often skipped)
Formulate a budget
Create a plan and timeline, with real milestones and deliverables!
Is This Really a Good Plan?
Sure, according to Phil Goldstein (Educause Center for Applied Research)
He’s conducted extensive surveys on “academic analytics” (= business intelligence for higher ed)
His four recommendations for improving analytics:
Key decisionmakers must lead the way
Technologists must collaborate
Must collect requirements
Must form strong partnerships with functional sponsors
IT must build the needed infrastructure
Carleton violated this rule with Cognos BI
As we discovered, without an ETL/warehouse infrastructure, success with OLAP is elusive
Staff must train and develop deep analysis skills
Goldstein’s findings mirror closely the advice of industry heavyweights – Ralph Kimball, Laura Reeves, Margie Ross, Warren Thornthwaite, etc.
Isn’t a DW a Huge Undertaking?
Sure, it can be huge
Don’t hold on too tightly to the big-sounding word, “warehouse”
Luminaries like Ralph Kimball have shown that a data warehouse can be built incrementally
Can start with just a few data marts
Targeted consulting help will ensure proper, extensible architecture and tool selection
What Takes Up the Most Time?
You may be surprised to learn what DW step takes the most time
Try guessing which:
Physical database setup
Acc. to Kimball & Caserta, ETL will eat up 70% of the time. Other analysts give estimates ranging from 50% to 80%. The most often underestimated part of the warehouse project!
Eight Month Initial Deployment 21 days Publicize, train, train 7 days Hook up OLAP tools 28 days Design ETL processes 7 days Design initial data mart 21 days Choose/set up modeling tool 28 days Learn/deploy ETL tool 4 days Deploy physical “target” DB 1 day Secure, configure network Duration Step 20 days Spec/order, configure server 14 days Eval/choose physical DB 21 days Eval/choose ETL tool 1 day Identify project roles 3 days Determine budget 7 days Decide general DW design 14 days Collect requirements 21 days Begin educating decision makers Duration Step
Information is held in transactional systems
But transactional systems are complex
They don’t talk to each other well; each is a silo
They require specially trained people to report off of
For normal people to explore institutional data, data in transactional systems needs to be
Renormalized as star schemas
Moved to a system optimized for analysis
Merged into a unified whole in a data warehouse
Note: This process must be led by “customers”
Yes, IT people must build the infrastructure
But IT people aren’t the main customers
So who are the customers?
Admissions officers trying to make good admission decisions
Student counselors trying to find/help students at risk
Development offers raising funds that support the College
Alumni affairs people trying to manage volunteers
Faculty deans trying to right-size departments
IT people managing software/hardware assets, etc….