Skip to main content

Data Dictionary Overview

The Catchlight Data Dictionary defines every data field in your dashboard, explaining what each field means, where the data comes from, how it's calculated, and how you can use it to better understand and engage with your clients.

Chris Ross avatar
Written by Chris Ross
Updated over 2 weeks ago

What is the Data Dictionary?

The Data Dictionary is your comprehensive reference guide for understanding every piece of data in the Catchlight dashboard. It explains:

  • Field definitions: What each data point represents

  • Data sources: Where information comes from

  • Calculation methods: How derived fields are computed

  • Use cases: How to apply data in client conversations

  • Limitations: What the data can and cannot tell you

Why the Data Dictionary Matters

Accurate Interpretation Understanding exactly what each field means prevents misinterpretation and helps you make better decisions.

Confident Client Conversations Knowing the data sources and methodology allows you to speak confidently about insights when engaging clients.

Strategic Application Understanding how fields are calculated helps you identify the most relevant data for specific use cases.

Compliance and Privacy Knowing data sources helps you stay compliant with privacy regulations and set appropriate client expectations.

Data Categories

The Catchlight Data Dictionary is organized into these major categories:

1. Profile & Identity Data

Core identifying information about each client:

  • Names, contact information, IDs

  • Match confidence and data quality indicators

  • Catchlight system identifiers

Article: 5.2: Profile & Identity Data

2. Demographic Data

Personal and household characteristics:

  • Age, gender, marital status

  • Children, grandchildren, household composition

  • Geographic location and home details

Article: 5.3: Demographic Data

3. Professional Data

Career and employment information:

  • Current employer and position

  • Job level and tenure

  • Education and alma mater

  • Professional history

Article: 5.4: Professional Data

4. Financial Indicators

Wealth and income signals:

  • Income ranges

  • Investable asset ranges

  • Wealth segments

  • Home value

  • Projected client revenue

Article: 5.5: Financial Indicators

5. Interests & Lifestyle Data

Behavioral and preference information:

  • Interest areas and hobbies

  • Sport affinities

  • Financial interests

  • Household activities

Article: 5.6: Interests & Lifestyle Data

6. Life Events & Eligibility Milestones

Timing-based opportunities:

  • Recent life changes

  • Upcoming milestones

  • Eligibility triggers (Medicare, Social Security, RMDs)

  • Career and family events

Article: 5.7: Life Events & Eligibility Milestones

7. Catchlight Scores

Proprietary analytics:

  • Catchlight Score (engagement likelihood)

  • Social Capital Score (network influence)

  • Score components and methodology

Articles: 5.8: Catchlight Score Explained and 5.9: Social Capital Score

8. Enrichment Status

Data quality and coverage indicators:

  • Enrichment completion status

  • Field coverage percentages

  • Data confidence levels

  • Last update timestamps

Article: 5.10: Enrichment Status Values

9. Social Media Profiles

Digital presence:

  • LinkedIn profile URLs

  • Facebook profile links

  • Twitter/X handles

  • Professional network connections

Article: 5.11: Social Media Profiles

10. Characteristics & Personas

Behavioral classifications:

  • Client personas

  • Psychographic segments

  • Communication preferences

  • Decision-making styles

Article: 5.12: Characteristics & Personas

11. Acquisition & Tracking Data

Business intelligence:

  • Marketing channel/source

  • Date won/acquired

  • Contact type

  • Campaign tracking

How Data is Collected

Catchlight aggregates data from multiple sources:

Public Records

  • Property records (home value, ownership)

  • Professional licenses

  • Business registrations

  • Court records (when permissible)

Consumer Data Providers

  • Demographic databases

  • Lifestyle and interest data

  • Financial indicators

  • Marketing databases

Social Media and Web

  • LinkedIn profiles (publicly available)

  • Facebook (publicly available)

  • Professional websites

  • Published biographies

Client-Provided Information

  • CRM data you upload

  • Form submissions

  • Survey responses

  • Direct communications

Derived and Calculated

  • Catchlight Score (proprietary algorithm)

  • Social Capital Score

  • Projected revenue estimates

  • Life stage classifications

Data Accuracy and Confidence

Match Confidence Levels

  • High: 95%+ confident this is the correct person

  • Medium: 80-94% confident

  • Low: Below 80%, may need verification

Data Freshness

  • Real-time: Updated immediately (your CRM data)

  • Daily: Social media profiles, some web data

  • Monthly: Demographic updates, lifestyle data

  • Quarterly: Financial estimates, wealth segments

  • Annual: Home values, income ranges

Coverage Expectations

Not every field will be populated for every client:

  • Core Identity: 95-100% coverage expected

  • Demographics: 70-90% coverage typical

  • Professional: 60-80% coverage (varies by employment type)

  • Financial: 40-70% coverage (estimated ranges)

  • Interests: 30-60% coverage (varies widely)

  • Social Media: 40-70% coverage (LinkedIn higher than others)

How to Use the Data Dictionary

When Reviewing Profiles

If you see an unfamiliar field or unexpected value, consult the Data Dictionary to understand what it represents and how it was derived.

When Building Filters

Reference the Data Dictionary to understand exactly what each filter option includes or excludes.

When Explaining to Clients

If a client asks how you know something about them, the Data Dictionary helps you explain data sources appropriately.

When Planning Outreach

Use the Data Dictionary to identify which fields are most actionable for your specific campaign or outreach strategy.

When Troubleshooting

If data seems incorrect, the Data Dictionary explains limitations and confidence levels to help you assess accuracy.

Understanding Field Formats

Text Fields

Standard text values (names, addresses, job titles)

Categorical Fields

Predefined options (wealth segments, job levels, enrichment status)

Numeric Ranges

Approximate values (age range, income range, investable assets)

Boolean Fields

Yes/No or True/False (has children, owns home, eligible for Medicare)

Date Fields

Specific dates or date ranges (date won, last job change, birthdate/age)

Score Fields

Numeric scores with defined ranges (Catchlight Score 0-100, Social Capital Score)

URL Fields

Links to external profiles (LinkedIn, Facebook, Twitter)

List Fields

Multiple values (interests, hobbies, characteristics)

Ethical Use of Data

Transparency Be prepared to explain to clients how you obtained information about them. The Data Dictionary helps you do this accurately.

Privacy Respect Just because data is available doesn't mean you should use it inappropriately. Reference sensitive information respectfully.

Accuracy Acknowledgment No data is 100% perfect. The Data Dictionary helps you understand confidence levels and limitations.

Regulatory Compliance Different data types have different regulatory considerations. Understand what data is subject to FCRA, GLBA, or other regulations.

Common Questions

Q: Why is some data missing for my clients? A: Data coverage varies based on public availability and client characteristics. See 8.2: Understanding Empty Data Fields

Q: How often is data updated? A: Different fields update on different schedules (daily to annually). Check individual field definitions for update frequency.

Q: Can I trust estimated financial data? A: Financial estimates are modeled predictions with varying accuracy. They're useful for segmentation but shouldn't be treated as exact values. See 7.1: Data Accuracy and Limitations

Q: Where does Catchlight get personal information? A: From public records, consumer databases, social media, and your uploaded CRM data. All sources are legally permissible and comply with privacy regulations.

Q: Can I correct inaccurate data? A: You can override fields with your own data or flag inaccuracies. Contact your Catchlight administrator for data correction procedures.

Related Articles

All Data Dictionary articles (5.2 through 5.12) provide detailed information about specific field categories. Start with the category most relevant to your immediate needs.

Quick Reference:

  • Looking up a specific field? Use Ctrl+F to search within category articles

  • Need to understand a score? See articles 5.8 and 5.9

  • Confused by empty fields? See article 8.2

  • Want to know data sources? Check the relevant category article (5.2-5.7, 5.11)

Navigation Tips

Each Data Dictionary article follows a consistent structure:

  1. Field name and definition

  2. Data source and collection method

  3. Format and possible values

  4. Use cases and applications

  5. Limitations and considerations

  6. Examples

Use the table of contents in each article to jump to specific fields quickly.

Related Articles

  • 5.2: Profile & Identity Data

  • 5.3: Demographic Data

  • 5.4: Professional Data

  • 5.5: Financial Indicators

  • 7.1: Data Accuracy and Limitations

  • 8.2: Understanding Empty Data Fields

Related Articles
Lead Quality Metrics
Profile & Identity Data
Enrichment Status Values
Combining Catchlight with CRM
Understanding Empty Data Fields
Did this answer your question?