AMS Data Health is a reporting and data integrity tool within the Understanding platform. It helps agencies assess the quality, completeness, and accuracy of their AMS (Agency Management System) data. By identifying gaps, inconsistencies, and missing information, agencies can take corrective actions to improve their data health, which directly impacts reporting, forecasting, and decision-making.

Key Functionalities and How Agencies Can Use AMS Data Health

1. Data Quality & Reporting

• Purpose: The Data Quality report helps agencies track the integrity of their AMS data.
• How It Works: The Data Quality table improves based on the agency’s work on five key cards that address various data quality aspects.
• Usage:
  • Track missing data or inconsistencies in production and book of business records.
  • Use sorting and filtering options to view data by billing carrier, account manager, producer, branch, line of business (LOB), etc.
  • Click the "Details" button to review upload status and see what was sourced from the latest AMS data upload.

2. Date Parameters

• Purpose: Provides visibility into the timing of data uploads and how they correlate with system reporting.
• How It Works: Agencies can hover over the information icon (i) in the top left of the screen to view date details.
• Usage: Ensure that the data being analyzed aligns with the latest AMS upload.

3. Actions in AMS Data Health

• Purpose: Each tab or data card within the AMS Data Health tool includes recommended actions that guide agencies on steps to resolve data inconsistencies.
• Usage: Agencies should regularly check and complete these actions to maintain data accuracy.

4. Identifying & Correcting Data Issues

Several components in the AMS Data Health tool help agencies identify and fix specific data issues:

a. Unbilled Policies

• Definition: Policies that have not been invoiced and have $0 revenue or premium associated.
• How It Works:
  • Agencies can click the Unbilled Policies card to filter and analyze policies that may require attention.
  • The table shows data broken down by account manager, producer, branch, billing carrier, bill method, and LOB.
• Usage:
  • Identify policies that should have generated commission.
  • Investigate discrepancies and update records accordingly.

b. Zero Premium Policies

• Definition: Policies where premium data is missing in the AMS book of business.
• How It Works:
  • Agencies can filter and analyze policies where premium information wasn’t recorded.
  • Helps identify potential issues like missing downloads, incorrect manual data entry, or canceled policies that weren’t transacted properly.
• Usage:
  • Ensure premiums are recorded accurately to avoid reporting issues.
  • Use this data for retention dashboard insights.

c. Incomplete Writing Carriers

• Definition: Writing carriers that are either blank or match the billing carrier, which indicates missing or incorrect data.
• How It Works:
  • Agencies must find the true writing carrier and update their AMS records.
• Usage:
  • Agencies should review dec pages and update the writing carrier information in their system.
  • A new AMS upload will correct the inconsistency.

d. Unmapped Billing & Writing Carriers

• Definition: Billing or writing carriers that have not been mapped properly in AgencyKPI’s system.
• How It Works:
  • Mapping ensures consistency by standardizing data fields across sources.
  • Agencies or networks must complete mapping tasks to resolve issues.
• Usage:
  • Agencies should verify whether their original AMS carrier records are mapped correctly.
  • Once mapped, carriers will be moved out of the "unmapped" list.

e. NAICS Code Classification

• Definition: Identifies customer accounts without an assigned NAICS code, which helps classify business types accurately.
• How It Works:
  • NAICS codes can come from AMS records, Data Axle (third-party classification), or manual entry in Clarity.
• Usage:
  • Agencies should review unclassified NAICS codes and update them in their AMS or Clarity.
  • Sorting by premium size can help prioritize accounts that need classification.

5. Data Visualization & Color Coding

• Purpose: AMS Data Health includes color-coded indicators to help agencies quickly assess data quality.
• How It Works:
  • Data Quality Table:
    • 80-100% = Green (Good)
    • 60-79% = Yellow (Needs Attention)
    • Below 59% = Red (Poor)
  • Top Cards (General Data Insights):
    • 0-5% = Green
    • 6-15% = Yellow
    • 15%+ = Red
• Usage: Agencies should focus on improving red and yellow indicators by addressing data issues.

6. Three-Step Monthly Guide for Data Maintenance

To maintain data accuracy, agencies should follow these three steps each month:

Step 1: Fix Unbilled Policies

• Identify policies with $0 premium or revenue and correct them.
• Determine whether commissions were received or if policies need to be canceled.
• Based on the typical agency's book of business, this percentage should be approximately 8% (1/12), reflecting the time gap between policy renewal and commission receipt. However, the ideal percentage may vary depending on the agency’s billing method (cash vs. accrual) and the ratio of agency bill to direct bill policies.

Step 2: Correct Zero Premium Policies

• Ensure premiums have been properly recorded in AMS.
• Address potential download issues or manual entry mistakes.

Step 3: Verify NAICS Codes

• Classify unassigned NAICS codes to improve reporting accuracy.
• Use AMS Data Health or Clarity to update missing codes.

Conclusion

AMS Data Health provides agencies with a structured approach to identifying and fixing data inconsistencies in their AMS. By following the recommended actions, regularly reviewing data quality metrics, and implementing a monthly data maintenance routine, agencies can ensure more accurate reporting, better forecasting, and improved operational efficiency.