Data Clean-Up and Management

Margaret Hogarth is Electronic Resources Coordinator and Subject Specialist for Environmental Sciences, Water and Soils for the University of California, Riverside Libraries. She has a B.A. in English from the University of California, Santa Barbara, an MLIS from San Jose State University and an M.S. in Environmental Studies from California State University, Fullerton. She has been a librarian since 1999. Kenneth Furuta, a Reference/Information Technology librarian for the University of California, Riverside Libraries. His B.A. was a double major in Psychology & Music from the University of California, Riverside. In addition to his MLS from the University of Arizona he has a Masters of Administration (emphasis in Management Information Systems) from UC Riverside. He has been a librarian since 1990.

List of figures
List of tables
About the authors
Chapter 1: Introduction (why this book is needed)


Abstract:
What makes this book unique?
Why library data is important
The book’s outline

Chapter 2: Commonalities


Abstract:
Microsoft Office Excel
MarcEdit
Microsoft Access
XML
Commonalities
Capture and use
Standardization
Data import issues
Technical skills
Project management challenges

Chapter 3: Defining data


Abstract:
Rule 1: define data points
Rule 2: apply data point definitions
Rule 3: count the right apples
Rule 4: avoid capturing redundant data

Chapter 4: Types of data issues


Abstract:
Microsoft Excel vs Microsoft Access
General data-handling edicts
Data issues: importing data

Chapter 5: Microsoft Excel techniques


Abstract:
Creating datasheets
Selecting cells
Copying
Sorting
Filter
AutoSum
Sum
Fill

Chapter 6: Data clean-up in Excel


Abstract:
Common dirty data scenarios
The usefulness of delimiting
System limitations
Removing extra characters

Chapter 7: Excel: combining data


Abstract:
IF statements
The TEXT function
PivotTables and filtering
VLOOKUP
HLOOKUP
MATCH

Chapter 8: Additional tools


Abstract:
PDFs
Notepad
Microsoft Word
Global update in an integrated library system
Regular expressions
Excel
Access
Macros
XML
MarcEdit
The MARC tools window

Chapter 9: Access techniques


Abstract:
What is a database?
Access
Planning a database
Preparing data for a database
Adding a table to a database

Chapter 10: Access forms


Abstract:
Types of form
Parts to a form
Form controls
Validating data
Option buttons
Combo boxes
For a Spin Button:
Tab control techniques
Multiple-table forms

Chapter 11: Access reports


Abstract:
Creating a report using the Report Wizard
Controls
Making additions to a report
AutoFormat a report
Working with report properties
Inserting a control into a report
Conditional formatting
Sizing reports
Moving controls in Access
Publishing reports
Sorting and grouping options
Adding calculations to reports
Launching reports
Creating a subreport

Chapter 12: Access queries


Abstract:
Sorting in Access
Filtering in Access
Queries
Entering data
Query properties
Access relationships

Chapter 13: Data clean-up in Access


Abstract:
Prevention is the best cure
Extra characters
Access data upload errors
ISSN issues

Chapter 14: Access -- combining data


Abstract:
Combining data from one or moredata sources
Query with a sum
Types of operators
Totals queries
Parameter queries
Action queries
Update queries
Delete queries
Make-Table queries
Append queries
PivotTable queries
SQL in Access
Parameter Queries in SQL
Export data to Excel
Finding unique values in a dataset
Matching on ISSN

Chapter 15: Strategies for missing data


Abstract:
Resources are missing ISBNs
Resources are missing ISSNs
Richard Jackson’s OCLC look-up strategy
Chapter 16: Qualitative data
Abstract:
The definition of qualitative data
Qualitative data is valuable
Types of qualitative data
Qualitative data techniques
SWOT analysis
Tools
The whole picture

Chapter 17: ROI


Abstract:

Chapter 18: Data collection and analysis


Abstract:
What data do you need to answer the question?
Does the data measure what you need to measure?
Analysing data
Data presentation
Charts
Stacked charts

Chapter 19: Data quality policy


Abstract:
Poor data quality
Data as an asset and a product
Apply quality principles
Process design
Framework for a data quality policy

Chapter 20: Next steps


Abstract:

Appendix 1: Excel techniques
Appendix 2: Excel functions
Appendix 3: Access quick keys
Appendix 4: Redman's model data policy
Bibliography and references
Index