1. October Ivins, MLS
November 8, 2013
Introduction - Christine
Why is Data Important? - October
The Causes of Bad Data – October
Getting Ready to Clean - Christine
How to Clean - Christine
◦ Easy tips
◦ More long term solutions
Questions and Discussion - You
3. Just because it’s a cliché doesn’t mean it isn’t
 Garbage in, garbage out.
 If you can’t measure it, you can’t manage
o How can you tell whether a change made a
difference, either positive or negative?
o Are you collecting all of the revenue you
o You can’t support good customer service,
marketing or sales with bad data.
o Messy data wastes time.
4. Multiple names for the same institution:
CSU Sacramento vs. Cal State Sacramento
UMass vs. Univ. of Mass Amherst vs. Univ
Berkeley vs. UC Berkeley vs. Univ of CA Berkeley
Same institution, different locations
Queens College (New York USA, Newfoundland,
Canada; Cambridge and Oxford, UK;
Melbourne, Australia and more)
 Punctuation matters in alphabetical sorts
 Transposed letters
Even harder- STM, research institutes at campus,
5. State, Country confusion
o Ala, (AL Alabama, AK Alaska)
o Canadian addresses with no country
o Countries change names
Partial names- “Trinity” College, University,
Community College, etc.?
Phone numbers with no area code
Inappropriate use of notes field
7. An association moved to package pricing
for all of its titles….
 Contracted for a project for subcontractors
to call and offer trial subscriptions….
 De-duping institutional and individual….
 Domestic decline masked by international
 Determine Carnegie Classifications for
market analysis or tiered pricing…
Offer from a consortium. Which members are
already customers and how much are they
Setting prices for the next year. How many
cancellations were there after the last one? Any
evidence they were related to the increase?
Your press has an opportunity to acquire a
journal. How do you assess your ability to
increase subscriptions? What should you offer?
A librarian insists electronic access has been
canceled in error. Can you tell from your data
or do you just take his word for it and
◦ May lack key fields, too few for addresses
◦ No unique customer number
◦ Free text without review
◦ Drop down menus better
◦ Standard source for institutional names?
Multiple Entry Points/No Style sheet
◦ Many departments/offices/staff enter without coordination
◦ Poor data integration from multiple vendors
◦ Style sheet?
◦ Instructions that limit creating new customer records?
◦ Checklist, review of new employees’ work?
11. October Ivins, MLS