Data quality and data health in Apricot are top priorities, but where should you start? Managing data health in Apricot includes a mix of proactive and reactive strategies. In addition, there are a set of tactics you can use to efficiently clean up your data when needed. Check out this SlideShare to learn the strategies, tools, and tips to improve the quality and health of your Apricot database. Learn how to assess data quality and take action. Watch the recorded webinar: https://youtu.be/Zvvl1B0216M Sidekick Solutions is an independent software consulting firm, specializing in Apricot software. We help new and existing Apricot users set up, streamline, and make the most of Apricot software with a range of professional services for implementation, reporting, consulting, data migration, and database audit/cleanup. We make Apricot easier to use and more capable for our clients, yielding higher return on investment in their Apricot software license.

1. How to Use Apricot Software to
Improve Data Quality

2. Hi, I’m Jeff
© 2017 Sidekick Solutions LLC2
Apricot Software Consultant
jeff@sidekicksolutionsllc.com

3. © 2017 Sidekick Solutions LLC3

4. Agenda
• Causes of poor data quality
• How to assess potential challenges
• How to clean up existing data
• How to save time as a database admin
• Key takeaways and questions
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5. Why manage data quality?
• Improve consistency and accuracy of reports
• Reduce risk of major data cleanups
• Save time with data quality systems and automation
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6. Symptoms of poor data quality
• Don’t trust your data
• Tracking data in other systems
• Inaccurate reports or unable to report at all
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7. Causes of poor data quality
1. Inadequate database structure
2. Incomplete data entry
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8. Cause: Inadequate database structure
• Collection of forms with no workflow
• Insufficient linking between unassociated forms
• Incorrect field choices (i.e. text instead of option)
• Unable to report using standard features (need other tools)
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9. Cause: Incomplete data entry
• Records saved but missing important fields
• Workflow step is incomplete
• Records are lost or slip through cracks
• No standard rules or conventions for data entry
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10. How to resolve data quality
• Step 1 – Assess causes of poor data quality
• Step 2 – Clean up existing data or abandon (set baseline)
• Step 3 – Manage data quality with systems
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11. Assess causes of poor data quality
1. Workflow review
2. Report requirements
3. User feedback
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Assessment Process
1. Take inventory (list)
2. Define vision
3. Compare inventory with vision
4. Develop corrective action plan

12. Clean up existing data
• Form and link redesign
• Manual record modifications
• Bulk updates or data migration using import tool
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13. Manage data quality with systems
1. Form-based solutions
2. Report-based solutions
3. User-based solutions
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14. Form-based: Form settings
• Hide from menu (Tier 1)
• Hide in document folder
• Record limit
• Allow copies
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15. Form-based: Field properties
• Required
• Duplicate check
• Clear on copy (Tier 2)
• Lock auto-populate fields
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16. Form-based: Link requirements
• Same folder linking
• Association limits
• Automate with wizard
• Lock/hide target links
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17. Form-based: Email triggers
• Real-time record audits
• Approvals and reviews
• Completion of workflow
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18. Form-based: Form logic
• Dynamic validation
• Unhide + Require
• Set value/Use lookup list
• Lock
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19. Report-based: Record audit
• Recurring weekly/monthly
• Creation date filter
• All fields as columns
• Export and review
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20. Report-based: Conditional validation
• Missing link
• More records than allowed
• Inactive client with activity
• Form logic gaps
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21. Report-based: Strategic reports
• Program focused
• Is output expected?
• Three why’s
• User ownership
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22. User-based: Solutions
• Guides – Use cases hosted in bulletin
• Access – Permission sets and RLA restrictions
• Training – Use cases + standard Apricot navigation
• Support– Ticketing and prioritization
• Assignment – Provide systems for self-management
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23. Key Takeaways
• Import tool and form logic are recommended features
• Build management systems and automate
• Distribute responsibility for data quality
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24. © 2017 Sidekick Solutions LLC24
• Implementation
• Reporting
• Consulting
• Data Migration
• Form Design
• User Permissions
• Database Audit/Cleanup
Our Services
www.sidekicksolutionsllc.com

25. Questions?
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