Richard discusses what a data warehouse is and why schools are setting them up. He explains that a data warehouse makes it easier for schools to optimize classroom usage, refine admissions systems, forecast demand, and more by bringing together data from different sources. It provides better information to make better admissions, retention, and fundraising decisions. He then discusses key data warehouse concepts like OLTP, OLAP, ETL, star schemas, and metadata to help the audience understand warehouse implementations.

1. What is a Data Warehouse?
And Why Are So Many
Schools Setting Them Up?
Richard Goerwitz

2. What Is a Data Warehouse?
 Nobody can agree
 So I’m not actually going to define a DW
 Don’t feel cheated, though
 By the end of this talk, you’ll
• Understand key concepts that underlie all
warehouse implementations (“talk the talk”)
• Understand the various components out of
which DW architects construct real-world data
warehouses
• Understand what a data warehouse project
looks like

3. Why Are Schools Setting Up
Data Warehouses?
 A data warehouse makes it easier to:
• Optimize classroom, computer lab usage
• Refine admissions ratings systems
• Forecast future demand for courses, majors
• Tie private spreadsheet data into central repositories
• Correlate admissions and IR data with outcomes such as:
 GPAs
 Placement rates
 Happiness, as measured by alumni surveys
• Notify advisors when extra help may be needed based on
 Admissions data (student vitals; SAT, etc.)
 Special events: A-student suddenly gets a C in his/her major
 Slower trends: Student’s GPA falls for > 2 semesters/terms
• (Many other examples could be given!)
 Better information = better decisions
• Better admission decisions
• Better retention rates
• More effective fund raising, etc.

4. Talking The Talk
 To think and communicate usefully about data warehouses
you’ll need to understand a set of common terms and
concepts:
• OLTP
• ODS
• OLAP, ROLAP, MOLAP
• ETL
• Star schema
• Conformed dimension
• Data mart
• Cube
• Metadata
 Even if you’re not an IT person, pay heed:
• You’ll have to communicate with IT people
• More importantly:
Evidence shows that IT will only build a successful warehouse if you
are intimately involved!

5. OLTP
 OLTP = online transaction processing
 The process of moving data around to
handle day-to-day affairs
• Scheduling classes
• Registering students
• Tracking benefits
• Recording payments, etc.
 Systems supporting this kind of activity
are called transactional systems

6. Transactional Systems
 Transactional systems are optimized primarily for
the here and now
• Can support many simultaneous users
• Can support heavy read/write access
• Allow for constant change
• Are big, ugly, and often don’t give people the data they
want
 As a result a lot of data ends up in shadow databases
 Some ends up locked away in private spreadsheets
 Transactional systems don’t record all previous
data states
 Lots of data gets thrown away or archived, e.g.:
• Admissions data
• Enrollment data
• Asset tracking data (“How many computers did we
support each year, from 1996 to 2006, and where do we
expect to be in 2010?”)

7. Simple Transactional Database
 Map of Microsoft
Windows Update
Service (WUS)
back-end database
• Diagrammed using
Sybase
PowerDesigner
 Each green box is a
database “table”
 Arrows are “joins” or
foreign keys
 This is simple for an
OLTP back end

8. More Complex Example
 Recruitment Plus back-end
database
 Used by many admissions
offices
 Note again:
• Green boxes are tables
• Lines are foreign key
relationships
• Purple boxes are views
 Considerable expertise is
required to report off this
database!
 Imagine what it’s like for
even more complex
systems
• Colleague
• SCT Banner (over 4,000
tables)

9. The “Reporting Problem”
 Often we require OLTP data as a snapshot, in a
spreadsheet or report
 Reports require querying back-end OLTP support
databases
 But OLTP databases are often very complex, and
typically
• Contain many, often obscure, tables
• Utilize cryptic, unintuitive field/column names
• Don’t store all necessary historical data
 As a result, reporting becomes a problem –
• Requires special expertise
• May require modifications to production OLTP systems
• Becomes harder and harder for staff to keep up!

10. Workarounds
 Ways of working around the reporting
problem include:
1. Have OLTP system vendors do the work
• Provide canned reports
• Write reporting GUIs for their products
2. Hire more specialists
• To create simplified views of OLTP data
• To write reports, create snapshots
3. Periodically copy data from OLTP systems to
a place where
• The data is easier to understand
• The data is optimized for reporting
• Easily pluggable into reporting tools

11. ODS
 ODS = operational data store
 ODSs were an early workaround to the “reporting
problem”
 To create an ODS you
• Build a separate/simplified version of an OLTP system
• Periodically copy data into it from the live OLTP system
• Hook it to operational reporting tools
 An ODS can be an integration point or real-time
“reporting database” for an operational system
 It’s not enough for full enterprise-level, cross-
database analytical processing

12. OLAP
 OLAP = online analytical processing
 OLAP is the process of creating and
summarizing historical, multidimensional
data
• To help users understand the data better
• Provide a basis for informed decisions
• Allow users to manipulate and explore data
themselves, easily and intuitively
 More than just “reporting”
 Reporting is just one (static) product of
OLAP

13. OLAP Support Databases
 OLAP systems require support databases
 These databases typically
• Support fewer simultaneous users than OLTP
back ends
• Are structured simply; i.e., denormalized
• Can grow large
 Hold snapshots of data in OLTP systems
 Provide history/time depth to our analyses
• Are optimized for read (not write) access
• Updated via periodic batch (e.g., nightly) ETL
processes

14. ETL Processes
 ETL = extract, transform, load
• Extract data from various sources
• Transform and clean the data from those sources
• Load the data into databases used for analysis and
reporting
 ETL processes are coded in various ways
• By hand in SQL, UniBASIC, etc.
• Using more general programming languages
• In semi-automated fashion using specialized ETL tools
like Cognos Decision Stream
 Most institutions do hand ETL; but note well:
• Hand ETL is slow
• Requires specialized knowledge
• Becomes extremely difficult to maintain as code
accumulates and databases/personnel change!

15. Where Does the Data Go?
 What sort of a database do the ETL
processes dump data into?
 Typically, into very simple table
structures
 These table structures are:
• Denormalized
• Minimally branched/hierarchized
• Structured into star schemas

16. So What Are Star Schemas?
 Star schemas are collections of data arranged
into star-like patterns
• They have fact tables in the middle, which contain
amounts, measures (like counts, dollar amounts, GPAs)
• Dimension tables around the outside, which contain
labels and classifications (like names, geocodes, majors)
• For faster processing, aggregate fact tables are
sometimes also used (e.g., counts pre-averaged for an
entire term)
 Star schemas should
• Have descriptive column/field labels
• Be easy for users to understand
• Perform well on queries

17. A Very Simple Star Schema
Data Center UPS
Power Output
Dimensions:
Phase
Time
Date
Facts:
Volts
Amps
Etc.

18. A More Complex Star Schema
 Freshman survey
data (HERI/CIRP)
 Dimensions:
• Questions
• Survey years
• Data about test
takers
 Facts:
• Answer (text)
• Answer (raw)
• Count (1)
 Oops
• Not a star
Oops, answers should have been placed in their • Snowflaked!
own dimension (creating a “factless fact table”).
I’ll demo a better version of this star later!

19. Data Marts
 One definition:
• One or more star schemas that present data on a single
or related set of business processes
 Data marts should not be built in isolation
 They need to be connected via dimensional tables
that are
• The same or subsets of each other
• Hierarchized the same way internally
 So, e.g., if I construct data marts for…
• GPA trends, student major trends, enrollments
• Freshman survey data, senior survey data, etc.
 …I connect these marts via a conformed student
dimension
• Makes correlation of data across star schemas intuitive
• Makes it easier for OLAP tools to use the data
• Allows nonspecialists to do much of the work

20. Simple Data Mart Example
UPS
Battery star
By battery
Run-time
% charged
Current
Input star
By phase
Voltage
Current
Output star
By phase
Voltage
Current
Sensor star
By sensor
Temp
Humidity
Note conformed date,
time dimensions!

21. CIRP Star/Data Mart
 CIRP
Freshman
survey data
 Corrected
from a
previous
slide
 Note the
CirpAnswer
dimension
 Note student
dimension
(ties in with
other marts)

22. CIRP Mart in Cognos BI 8

23. ROLAP, MOLAP
 ROLAP = OLAP via direct relational query
• E.g., against a (materialized) view
• Against star schemas in a warehouse
 MOLAP = OLAP via multidimensional
database (MDB)
• MDB is a special kind of database
• Treats data kind of like a big, fast spreadsheet
• MDBs typically draw data in from a data
warehouse
 Built to work best with star schemas

24. Data Cubes
 The term data cube
means different things to
different people
 Various definitions:
• A star schema
• Any DB view used for
reporting
• A three-dimensional
array in a MDB
• Any multidimensional
MDB array (really a
hypercube)
 Which definition do you
suppose is technically
correct?

25. Metadata
 Metadata = data about data
 In a data warehousing context it can mean many
things
• Information on data in source OLTP systems
• Information on ETL jobs and what they do to the data
• Information on data in marts/star schemas
• Documentation in OLAP tools on the data they
manipulate
 Many institutions make metadata available via
data malls or warehouse portals, e.g.:
• University of New Mexico
• UC Davis
• Rensselear Polytechnic Institute
• University of Illinois
 Good ETL tools automate the setup of
malls/portals!

26. The Data Warehouse
 OK now we’re experts in terms like OLTP, OLAP,
star schema, metadata, etc.
 Let’s use some of these terms to describe how a
DW works:
• Provides ample metadata – data about the data
• Utilizes easy-to-understand column/field names
• Feeds multidimensional databases (MDBs)
• Is updated via periodic (mainly nightly) ETL jobs
• Presents data in a simplified, denormalized form
• Utilizes star-like fact/dimension table schemas
• Encompasses multiple, smaller data “marts”
• Supports OLAP tools (Access/Excel, Safari, Cognos BI)
• Derives data from (multiple) back-end OLTP systems
• Houses historical data, and can grow very big

27. A Data Warehouse is Not…
 Vendor and consultant proclamations
aside, a data warehouse is not:
• A project
 With a specific end date
• A product you buy from a vendor
 Like an ODS (such as SCT’s)
 A canned “warehouse” supplied by iStrategy
 Cognos ReportNet
• A database schema or instance
 Like Oracle
 SQL Server
• A cut-down version of your live transactional
database

28. Kimball & Caserta’s Definition
 According to Ralph Kimball and Joe
Caserta, a data warehouse is:
A system that extracts, cleans, conforms, and
delivers source data into a dimensional data
store and then supports and implements
querying and analysis for the purpose of
decision making.
 Another def.: The union of all the enterprise’s data marts
 Aside: The Kimball model is not without some critics:
• E.g., Bill Inmon

29. Example Data Warehouse (1)
 This one is
RPI’s
 5 parts:
• Sources
• ETL stuff
• DW proper
• Cubes etc.
• OLAP apps

30. Example Data Warehouse (2)
 Caltech’s DW
 Five Parts:
• Source systems
• ETL processes
• Data marts
• FM/metadata
• Reporting and
analysis tools
• Note: They’re
also customers
of Cognos!

31. So Where is Colorado College?
 Phil Goldstein (Educause Center for Applied
Research fellow) identifies the major deployment
levels:
• Level 1: Transactional systems only
• Level 2a: ODS or single data mart; no ETL
• Level 2: ODS or single data mart with ETL tools
• Level 3a: Warehouse or multiple marts; no ETL; OLAP
• Level 3b: Warehouse or multiple marts; ETL; OLAP
• Level 3: Enterprise-wide warehouse or multiple marts;
ETL tools; OLAP tools
 Goldstein’s study was just released in late 2005
 It’s very good; based on real survey data
 Which level is Colorado College at?

32. Implementing a Data Warehouse
 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
 Scope
 Priority
 Resources
 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)
 Determine priorities/scope
 Formulate a budget
 Create a plan and timeline, with real milestones and deliverables!

33. 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.

34. 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

35. What Takes Up the Most Time?
 You may be surprised 90
to learn what DW step 80
70
takes the most time 60
Hardware
Try guessing which:
50 East
Database
 ETL
40 West
Schemas
• Hardware 30 North
OLAP tools
20
• Physical database setup
10
• Database design 0
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
• ETL
• OLAP 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!

36. Eight Month Initial Deployment
Step Duration Step Duration
Begin educating decision makers 21 days Secure, configure network 1 day
Collect requirements 14 days Deploy physical “target” DB 4 days
Decide general DW design 7 days Learn/deploy ETL tool 28 days
Determine budget 3 days Choose/set up modeling tool 21 days
Identify project roles 1 day Design initial data mart 7 days
Eval/choose ETL tool 21 days Design ETL processes 28 days
Eval/choose physical DB 14 days Hook up OLAP tools 7 days
Spec/order, configure server 20 days Publicize, train, train 21 days

37. Conclusion
 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….