My Mother's Phonebook. In our house it rivaled something of a “How To” guide for family maintenance. An oversized, brown three-ring binder with all the important papers and numbers – direct lines to pediatricians, immunization records and the complete Christmas List. Unfortunately my Mother’s phonebook also followed it’s own (or her own) logic. Friend’s numbers were on the respective child’s pages, neighbors all filed under “N” instead of last names and the most important documents stuffed in front (as if their placement suggested extreme significance). One thing my Mother’s phonebook didn’t have was uniform logic or data entry that another person could easily pick up and continue. We joked that this was Mom’s form of solidifying her job security, but in an organization or campaign this can be an all too scary reality!

Just like Mother's phonebook, your organization or campaign needs to adopt a sustainable logic to assure easy look ups, minimize duplicates and accurate counts and reporting. Most databases begin as a simple contacts list and grow to include communication tracking, event participation and giving history. All should include written guidelines for anyone entering or manipulating data but what if your organization or campaign doesn’t have established rules for using the data? Or if you are inheriting someone else’s failed logic? Follow these simple steps to ensure you’re not continuing the “trash in – trash out” cycle.

• Establish goals for the database. What is your database going to be used for? For example, if you are filing compliance reports you will want to enter a step in your work flow for batching and bank reconciliation to tie out financial transactions entered. If you are running walk lists you want to mainstream the canvassers options they provide for residents for consistency (in AI360 you can use the “Groups Management” page to edit existing groups and the “List Processor” to cross generate new groups).
• Stop and take stock of current database. Run an export to Microsoft Excel or an overview report to review existing data to evaluate the areas that require the most urgent data clean up. For instance, are duplicate individual records giving you inaccurate search results? If so, run a report or search to isolate these records (in AI CM/PM5 you can use the “Duplicate Similarity Finder” system report). 
• After prioritizing changes to dataset create a plan. Your database plan should contain three phases.
1. Database clean up – addressing priorities you established in your database review with set timelines for completion.
2. Written guidelines for database management to maintain clean dataset – this should include notes on work flow and be made available for anyone working in the dataset.
3. Identify areas for growth – isolating where you can develop your database goals and what steps are necessary to get achieve each goal.