Database Management Methodology for small Non-Profits - Susanne Petersson
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The document emphasizes the importance of maintaining a clean membership database for the Chicago Art Deco Society, highlighting that members are vital to the organization's success. It outlines the reasons for database cleanliness, the fields to clean, and the processes for regular data cleansing based on specific schedules. It also addresses the necessity of accurate data for organizational decision-making and security measures to protect the database.
![When to Cleanse a Database
Dependent upon
Database size [number of records]
Significant events – financial, social,
marketing
Number of persons altering data
Susanne Petersson 27](https://image.slidesharecdn.com/24973f81-8407-469e-81d4-a3739d691c45-160121224527/85/Database-Management-Methodology-for-small-Non-Profits-Susanne-Petersson-27-320.jpg)
Database Management Methodology for small Non-Profits - Susanne Petersson
- 1. Susanne Petersson Project Manager, Chicago Art Deco Society
- 2. Membership = Customers Members are our Fans ! Members are our Bread and Butter ! http://mrshealy-usii.wikispaces.com Susanne Petersson 2
- 3. Membership = Customers Members are our Fans ! Members are our Bread and Butter ! ………… manage Member information with diligence http://mrshealy-usii.wikispaces.com Susanne Petersson 3
- 4. A Clean Database is: Susanne Petersson 4
- 5. Reasons for a Clean Database Database Fields to Clean When to Cleanse a Database Susanne Petersson 5
- 6. Reasons for a Clean Database A. Data is captured/modified by other areas B. Data is included in decision- making Susanne Petersson 6
- 7. Reasons for a Clean Database A. Data is captured internally Mailings of publications, thank-you gifts Direct communications (letters) Email announcements Susanne Petersson 7
- 8. Reasons for a Clean Database A. Data is modified externally Members enter, update their own data Non-members join events, request information Susanne Petersson 8
- 9. Susanne Petersson 9 Rate of Internal/External Forces …
- 10. Rate of Internal/External Forces shall continue to Increase Susanne Petersson 10
- 11. Reasons for a Clean Database B. Data is included in decision-making Affects your non-profit goals Uncovers strategic opportunities Influences recurring activities Impacts financials Susanne Petersson 11
- 12. Reasons for a Clean Database B. Data is included in decision-making – and decision-making is based on.. Accurate Statistics Susanne Petersson 12
- 13. Reasons for a Clean Database Database Fields to Clean When to Cleanse a Database Susanne Petersson 13
- 14. Database Fields to Clean Those key to identifying: Errors Typographical Inconsistencies Duplicates Incomplete data Field formatting Susanne Petersson 14
- 15. Database Fields to Clean The core fields are: Street (Address 1) Unit (Address 2) State (province, territory) Country It is that Simple.. Susanne Petersson 15
- 16. Database Fields to Clean Standardize these first: Street (Address 1) Unit (Address 2) State (province, territory) Country ………… and then Susanne Petersson 16
- 17. Database Fields to Clean Organize the data to: Fix errors Address inconsistencies Expand search to other fields Susanne Petersson 17
- 18. Database Fields to Clean 1. Standardize: Street (Address 1) Abbreviate the direction Abbreviate the street type No periods needed Susanne Petersson 18
- 19. Database Fields to Clean 2. Standardize: Unit (Address 2) Remove terms associated with multi-unit tenancy Replace verbiage with the symbol “#” Susanne Petersson 19
- 20. Database Fields to Clean 3. Standardize: State (Province, Territory) Maximum of 2 to 4 alpha-characters Abbreviations are accepted standard world-wide No periods necessary Susanne Petersson 20
- 21. Database Fields to Clean 4. Standardize: Country Consider leaving field empty, when unnecessary Maximum of 2 to 8 alpha-characters No periods necessary Susanne Petersson 21
- 22. The ability to retain a clean database is based on consistency Susanne Petersson 22
- 23. The 4 core fields are used as the base structure to sort data Susanne Petersson 23
- 24. Database Fields to Clean Benefits of standardization Analytics – Accurate counts: memberships, contributions – Proper analysis: demographics, activity Susanne Petersson 24
- 25. Database Fields to Clean Benefits of standardization Communications – Data fits well on forms – Offers a professional look and feel Susanne Petersson 25
- 26. Reasons for a Clean Database Database Fields to Clean When to Cleanse a Database Susanne Petersson 26
- 27. When to Cleanse a Database Dependent upon Database size [number of records] Significant events – financial, social, marketing Number of persons altering data Susanne Petersson 27
- 28. When to Cleanse a Database Based on integrity expectations Accuracy Consistency Relevancy Susanne Petersson 28
- 29. When to Cleanse a Database Two types of schedules a. Ad hoc As notified As needed b. Planned Quarterly Annually Susanne Petersson 29
- 30. When to Cleanse a Database a. Ad hoc schedules As notified – triggered by user activity Review individual or small set of records Generally perform on-line Focus on core and other contact-related fields Susanne Petersson 30
- 31. When to Cleanse a Database a. Ad hoc schedules As notified – triggered by user activity Accuracy-Consistency-Relevance: 60% + Activity accomplished in the midst of other task assignments Susanne Petersson 31
- 32. When to Cleanse a Database a. Ad hoc schedules As needed – 1 week prior to significant event Review bulk of records Generally export to spreadsheet format Focus on core fields, name(s), then other inconsistencies Susanne Petersson 32
- 33. When to Cleanse a Database a. Ad hoc schedules As needed – 1 week prior to significant event Accuracy-Consistency-Relevance: 80% + Quickly identify field inconsistencies Bound by some time constraints Susanne Petersson 33
- 34. When to Cleanse a Database b. Planned schedules Quarterly – regular maintenance Review bulk of records Generally export to spreadsheet format Focus on core fields, name(s), then other inconsistencies Susanne Petersson 34
- 35. When to Cleanse a Database b. Planned schedules Quarterly – regular maintenance Accuracy-Consistency-Relevance: 90% + Quickly identify field inconsistencies Adequate time allotted for thoroughness Susanne Petersson 35
- 36. When to Cleanse a Database b. Planned schedules Annually – confirm statistics Review all records Generally export to spreadsheet format Focus on core fields, name(s), then other inconsistencies Susanne Petersson 36
- 37. When to Cleanse a Database b. Planned schedules Annually – confirm statistics Accuracy-Consistency-Relevance: 98% + Quickly identify field inconsistencies Adequate time allotted for thoroughness Susanne Petersson 37
- 38. Now that you have completed the process: Identified reasons for accuracy Determined the fields to monitor Established recurring schedules … a note about Security.. Susanne Petersson 38
- 39. Do you know what may be heading your way? Susanne Petersson 39
- 40. Data is the lifeblood of your organization Other departments rely on it Accurate data is easily navigated Users expect to see relevant data Susanne Petersson 40
- 41. Secure your system and pc data Protect with a firewall Backup files – local, cloud Activate anti-virus software Susanne Petersson 41
- 42. My name is Susanne Petersson I assisted the Chicago Art Deco Society to develop repeatable processes to manage the membership database. As a LSSGB and MBA, I understand corporate dynamics As a project manager, I get things done As a certified trainer, I understand and value the human element Susanne Petersson 42
- 43. Thank you!Thank you!Thank you!Thank you! SPetersson.usa@gmail.comSPetersson.usa@gmail.comSPetersson.usa@gmail.comSPetersson.usa@gmail.com SusanneSusanneSusanneSusanne----PeterssonPeterssonPeterssonPetersson----USAUSAUSAUSA
Editor's Notes
- #9: Mailings of Publications, Thank-you Gifts Address label size in use Data must fit lengthwise & widthwise, include margin Information legible by postal offices
- #10: Mailings of Publications, Thank-you Gifts Address label size in use Data must fit lengthwise & widthwise, include margin Information legible by postal offices