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Data Warehouse Considerations. Monty Bieber Teradata National Practice Partner October 28, 2005. Data Warehouse Considerations. Monty Bieber Teradata National Practice Partner October 28, 2005. The Classic Rock Guide to Data Warehousing. Monty Bieber Teradata National Practice Partner

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Data Warehouse Considerations

Monty Bieber

Teradata National Practice Partner

October 28, 2005

slide2

Data Warehouse Considerations

Monty Bieber

Teradata National Practice Partner

October 28, 2005

monty bieber teradata national practice partner october 28 2005

The Classic Rock Guide to Data Warehousing

Monty Bieber

Teradata National Practice Partner

October 28, 2005

introduction

Monty R, BieberNational Practice Partner

National Accounts Division

2837 Dell Ridge Dr.

Holt, MI 48842-8718

Phone: 517-882-5521

Cell: 517-203-8417

monty.bieber@ncr.com

Introduction
  • Professional Services Partner
  • Teradata Presales specialist for 8 years
  • Teradata DBA and project manager
  • Former State employee
  • Former music teacher
  • Even more former bass player in a completely unknown (thank goodness) band
ten points to ponder
Ten Points to Ponder
  • Integration
  • Dirty data
  • Building the perfect beast
  • Who’s paying for this?
  • The organization
  • Who needs it?
  • Tools
  • It’s not a mainframe
  • Great expectations
  • Data marts
who needs it
Who Needs It?
  • A project of this scope needs a sponsor
    • Someone high up needs to care and commit
  • Who really benefits from the work?
    • The users may not be the ones to benefit most
  • Are you designing for the right people?
    • What problems is the warehouse supposed to solve?
tools
Tools
  • All users are not alike
  • Many business questions require more than a simple query
  • Different tasks require different tools
  • Different demands call for different approaches
it s not a mainframe
It’s Not a Mainframe
  • A data warehouse can’t and shouldn’t be run like a mainframe
  • Administrative concerns are very different
  • Space and use allocations are different, and always unpredictable
  • Restrictions should be applied sparingly
great expectations
Great Expectations
  • It’s possible to oversell the value or performance of a data warehouse
  • User expectations can be set so high that they can never be fulfilled
  • Demos make everything look easy
  • Not all queries take3 seconds or less
data marts
Data Marts
  • Outside your warehouse
    • No matter what you read, it’s VERY difficult to bring together data from separate platforms
      • Different sources have different data issues
      • The web is a VERY poor place to bring data together
  • Inside your warehouse
    • Depending on why you do it, this is not necessarily a bad idea
    • Virtual data marts can be more effective than physical ones
integration
Integration
  • It’s very tough to build an entire enterprise warehouse
  • It’s a major project with high probability of failure
  • Requirements will change
  • Just because you can’t do it all at once doesn’t mean it’s a bad idea
  • A solid and extensible model is the key
dirty data
Dirty Data
  • The quality of your data is worse than you think
  • Bad data leads to bad answers
  • Cleaning up the data is a much larger task than you expect
  • The warehouse itself is often the most powerful data cleansing platform
building the perfect beast
Building the Perfect Beast
  • It’s possible to get caught up in the design and never deliver anything useful
    • Don’t try to model or build it all at once
    • Don’t try to capture every entity and attribute
    • Start with a self-contained area with high payback
  • “Good” now is better than “perfect” later
who s paying for this
Who’s Paying for This?
  • The enterprise warehouse is a corporate asset
  • How do you charge it back?
    • Storage locker?
    • Taxi?
    • Make it an overhead cost?
  • Consider other factors
    • Active vs. dormant data
    • “Data velocity”
the organization
The Organization
  • A data warehouse has major organizational impacts
  • Job responsibilities will change
  • Labor issues may arise
  • Power bases will shift
  • Bad politics will defeat great technology any day
  • Encourage, don’t force
  • Demonstrate benefits for skeptics
  • Help power users find new power base
we re doomed aren t we
We’re Doomed, Aren’t We?

It may seem fiendishly difficult, but data warehousing projects can succeed!

management support
Management Support

DILBERT reprinted by permission of United Feature Syndicate, Inc.

conclusion happy ending
Conclusion (Happy Ending)
  • Solve a visible business problem that has high priority
  • Include business experts, knowledge workers, users on your project team
  • Architect the warehouse to enable an enterprise view of data
  • Deliver some new magic regularly