Data Maintenance Agent Performance Metrics: Monitoring and Optimising Results

Data Maintenance Agent Performance Metrics: Monitoring and Optimising Results

Understanding how your Data Maintenance agent performs is essential for maintaining a healthy database and ensuring your outreach and automation strategies remain effective. The Diagnostics section of the Data Maintenance Agent provides a comprehensive overview of your database’s health, identifying stale records, enrichment opportunities, and data quality distribution. Below is a breakdown of the key elements presented in this view:

🔵 Overall Health

Description: A score out of 100 that reflects the general health of your database, based on:

• Data quality (accuracy and formatting)

• Completeness (presence of key fields)

• Freshness (how recently records were updated)

A score of 70, for example, means your data is in “Good” condition, but there’s room for improvement.

📊 Total Database Records

Description: Displays the total number of records being managed by the AI Agent. It also shows a breakdown of record types:

• Candidates

• Contacts

• Leads

This metric gives you a high-level view of database volume.

🟠 Stale Records

Description: Indicates how many records haven’t been updated in over 90 days. These records may contain outdated or irrelevant information, which can hinder communication and decision-making.

💡 Enrichment Opportunities

Description: Represents the number of records with missing or incomplete data that the AI Agent can enrich by filling in gaps using external sources.

💰 Estimated Credits Needed

Description: Projects the credit cost (or dollar amount) required to enrich all the records identified as having enrichment opportunities.

✅ Data Quality – by Record Type

Breaks down data quality for each type of record: Candidates, Contacts, and Leads.

Candidates Data Quality

Description: Evaluates the integrity and completeness of candidate records. A score of 80 indicates “Excellent” data with very few issues.

Contacts Data Quality

Description: Measures how complete and usable contact information is. A score of 60 reflects “Good” quality but shows there are some gaps.

Leads Data Quality

Description: Focuses on how well-prepared your lead records are for outreach. A score of 70 represents “Good” but still leaves room for enrichment.

🔍 Missing Data Analysis (Bar Chart)

Description: Visualises the number of records missing critical information such as:

• Skills

• Location

• LinkedIn

• Email

• Phone

• Job Title

This allows you to prioritise which fields to enrich first. For example, missing skills might represent the biggest opportunity (e.g., 5276 records).

📉 Stale Records Analysis (Bar Chart)

Description: Shows when your records were last updated:

• Last 30 days (Fresh)

• 31–90 days (Aging)

• 91–180 days (Stale)

• 180+ days (Very Stale)

Records older than 90 days are flagged as stale and may need updating to maintain accuracy.

🏆 Top Enrichment Opportunities

Description: Highlights high-priority enrichment targets, broken down by record type. For example:

• Candidates: 373 records missing LinkedIn

• Leads: 128 records missing LinkedIn

• Contacts: 26 records missing LinkedIn

Each entry includes a priority score to indicate urgency.

📈 Data Quality Distribution (Pie Charts)

Description: Pie charts show what percentage of records fall into the following quality tiers:

• Excellent: Fully complete, no issues

• Good: Mostly complete with minor gaps

• Fair: Several missing or inaccurate fields

Separate charts are provided for Candidates, Contacts, and Leads to help you focus your cleanup efforts accordingly.

By monitoring these performance metrics, you can ensure your AI Agent continues to deliver value through cleaner, more actionable data. This empowers your team to make informed decisions, improve engagement rates, and streamline operations.