blackbaud conference keeping your data clean

Keeping Your Data CleanCassie Hunt and Wendy Jaccard

Cassie Hunt and Wendy Jaccard | October 20–22, 2010 | Page #2

Topics

� Why worry about data integrity?

� Measuring the ROI

� Questions to ask Yourself

� Data Management Tools

� Case Study #1: Database Assessment

� Case Study #2: Starting from Scratch

� Increasing Data Integrity using Raiser’s Edge


The Impact of Bad Data

� Returned mail:

– Lost donations.

– Wasted resources.

� Loss of trust internally:

– Anecdotal incidents live on and become ‘fact.’

� Unhappy constituents.


Return on Investment of Data Integrity

� Cost to raise a dollar.

� Increased donor satisfaction.

� Increased level of trust from colleagues.

� Allows additional staff members to input data.

– Training is still the key.


Understanding Data Management

� How should data management be prioritized?

� Who is responsible for data management?

� How much information is too much?

� When should you stop searching?

� How automated can data management be?

� What can be assumed?


Reactive Data Management

� Addresses are updated as mail is returned.

� Obituaries are sought when a memorial gift comes in.

� Debriefings after visits.

� Correct individual instances of bad data.

� Clean information in shadow systems, not in RE.


Proactive Data Management

� Constant monitoring and cleaning of your data.

� Expanding your internal team.

� External efforts to refresh data:

– Alumni directory project.

– Rewards for finding lost alumni.


Data Integrity Tools: At a Cost

� Address update service

� National change of address

� ObitFinder

� AlumniFinder

� Blackbaud delivered:

– AddressFinder

– DeceasedRecordFinder

– EmailFinder

– PhoneFinder

– AddressAccelerator


Data Integrity Tools: Free Resources

� Social Security Death Index

� Legacy.com

� Switchboard.com, whitepages.com

� Social Media Sites: LinkedIn, Facebook, etc.

– What constitutes stalking?

� Your staff and volunteers


Data Integrity in a Complicated Shop

� Challenges when you are the ‘golden source’ for data…and when

you are not.

– Who owns parent data?

– Who owns student data?

– Does “new” info trump “confirmed” info?

– Which sources do you trust the most?


The Nitty Gritty:

Using RE to Identify

(and Solve!) Data Problems


Two Perspectives

� Performing a mid-stream database assessment.

� Setting your database up correctly from the start.


Performing a Mid-stream Database Assessment

� Where to begin:

– Record counts:

• Admin/system statistics


What does this tell us?

� Signs of program maturity.

� Indicators of database usage.

� Value of constituent records.



� Where to begin:

– Code table review.



� Where to begin: Code table review.

� Ask Yourself:

– Can I define all of my table values?

– Can some be combined?

– Can some be inactivated?

– What tables are not being used?

– What tables have inconsistent values?

– What is duplicative?


Code Table Review: Common Problem Areas

� Addressee/Salutation table

� Ratings

� Titles

– Lt.

– LT

– LT.

– Lieutenant

� Relationships

� Benefits

� Profession/Industry

� Solicit codes



� Where to begin:

– Record counts:

• Admin/System statistics

– Table review:

• Common problem areas.

– Attribute review:

• Information that has a natural location elsewhere.

– Detailed record review:

• Chairman of the Board is a good place to start!.


Rule of Thumb #1

When looking at data integrity,

focus on the quality of the

information first, and how

it is used second.


Rule of Thumb #2

Build your data integrity

queries around your

policies and procedures.


Data Integrity Queries

� Track in a separate folder that is checked routinely:

– Appoint a ‘data czar.’

� Be disciplined:

– When you see one example, create a query to search for others.

� Use the queries for initial cleanup, then ongoing maintenance:

– An empty data integrity query is a good query.


Data Integrity Queries: What to Look For

� Think about pieces of data that tie to others.

� Example:

– If deceased equals ‘yes’, then:

• Spouse’s marital status should equal ‘Widowed.’

• Constituent should be off all mailing lists.

• There should be no open pledges.

• Solicitor assignments should end.

• Salutations should be updated for spouse.




� Then turn it around:

– If deceased equals ‘no’, then:

• There should not be a deceased date.

• There should be no memorial gifts.




� Example:

– If someone has a ‘No Contact’ solicit code, then:

• Constituent should be off all mailing lists.

• Constituent should be marked ‘Inactive.’

• There should be a notepad explaining the reason behind the code.


Simple Data Integrity Queries: Biographical

� Title versus gender, addressees and salutations:

– Gender versus title.

– Gender versus spouse title.

– Gender versus other relationships.

� Reciprocity of relationships.

� Activity level of constituents.

� No valid addresses vs. send mail vs. inactive.


Data Integrity Queries: Start Simple


Simple Data Integrity Queries: Gift

� Gifts marked as ‘Unacknowledged’ or ‘Not Receipted.’

� Open pledges with last payment date over 3 years ago.

� Funds aligned with incorrect campaigns.

� Anonymous gift vs. anonymous person.

� Gifts from those marked ‘no solicit.’


Data Integrity Queries: Deceased with Pledges


Easy Cleaning in Raiser’s Edge

� Default/limit funds to campaigns and appeals to funds.

� Export/Clean/Import:

– Never, ever export without an import ID!

� Global change/Global add/Global delete.

� Use default sets for constituents, actions and events.

� Making fields required.


Using Gender with Relationships


Using Gender with Relationships

� Options for Cleanup:

– One-by-one

– Global Change

– Export Clean Import


Starting From Scratch

� Where to begin:


Questions?

Cassie Hunt, Bentz Whaley Flessner, [email protected]

Wendy Jaccard, Blackbaud, [email protected]

92401:CRH/cry:092110

blackbaud conference keeping your data clean

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