Data Mapping Between Systems: Keys, Values, Relationships (with P&C Insurance Examples)

What Is Data Mapping

Data mapping solves a fundamental problem: how to translate information between systems that describe the same world differently. It creates relationships between data elements across distinct models, enabling systems to share information meaningfully despite their differences.

Every business system speaks its own language of IDs and relationships. Data mapping isn’t about translation – it’s about preserving meaning across fundamentally different dialects.

Data mapping occurs in two primary forms, each addressing different integration challenges:

Entity resolution (also record matching, data linkage) focuses on matching records that represent the same real-world entity across systems. When your CRM calls a client ABC Manufacturing Inc. with ID 1001 and your billing system calls them ABC Mfg with ID C-739, entity resolution establishes these records represent the same company. This record-level mapping preserves relationships to other data like contacts, orders, and service history. This usually means field-to-field mapping.

Schema mapping aligns the structural elements between systems – tables, fields, and their relationships. It defines how Customer.LastName in one system relates to contact_surname in another, creating translation rules for moving data between them. Schema mapping establishes the framework that enables broader system integration.

Both forms of mapping require understanding systems at multiple levels:

• Technical structure (fields, data types, constraints)
• Business semantics (what values mean in context)
• Relationship patterns (how records connect to each other)
• Quality characteristics (completeness, accuracy, standardization)

Organizations often underestimate mapping complexity, assuming it requires only simple text matching. This misconception leads to broken relationships between records, corrupted reporting, and significant technical debt. Effective mapping requires understanding the full data context in both source and destination systems.

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Business systems differ by architecture and underlying data models. Even two installations of the same system can differ due to spelling variations, operational edge cases etc.

Reality of Connected Records in Core Business Systems

Modern business systems don’t store data as isolated entries. They create intricate webs of connected records that mirror real-world relationships. A company record stored in an Agency Management System (e.g. Applied Epic, Vertafore AMS360, EZlynx, TAM, QQ, Sagitta etc.) will link to coverages, policies, producers (sales staff), and so on. These connections define how your business operates.

Each relationship relies on unique identifiers that connect records across tables called keys. When a business system records a transaction, it doesn’t duplicate the customer’s name and address. It references the customer’s unique ID, creating a relationship between the transaction and customer records.

Keys create the fundamental, invisible structure of business data. They establish which records belong together and maintain critical relationships. Compromise these connections during system integration, and you will introduce friction into your core business functions.

This approach enables powerful functionality:

• Update a customer’s address once, and all related records reflect the change
• Track every interaction with a particular account
• Analyze relationships between products, customers, and sales performance
• Analyze top-level business metrics and break them down into granularities

But these relationships also create complexity when mapping data between systems. Your data teams can’t simply match names and move on.

Your data teams must preserve the entire web of connections by fitting source records into your destination system and restoring relationships according to its business rules.

The mapping process requires both structural alignment and semantic understanding.

Typical Use Cases That Require Data Mapping

Organizations face data mapping requirements in specific operational scenarios that demand careful navigation between systems. Understanding these trigger points helps teams prepare effectively and avoid rushed implementations.

1. Merger and Acquisition Activity

M&A transactions trigger major data conversion challenges which involve data mapping – especially in insurance:

• Acquiring agencies brings client, policy, and carrier records needing migration
• Carrier lists contain overlapping but inconsistently named entities, and their bound policies
• Equivalent business processes may operate under different data models
• Compliance warrants preserving data lineage during transition

Post-acquisition integration success depends heavily on effective data mapping. Mismatched records create operational disruptions that can undermine expected acquisition benefits.

We’ve gone at lengths about Insurance Agency Acquisitions and Post-M&A Integration.

If you are interested in saving time and stress on Agency Management Systems migrations, visit our solution page for AMS data conversion projects.

2. System Migration, Consolidation, and Replatforming

When replacing legacy systems or consolidating multiple platforms, organizations must map data between old and new environments. This scenario commonly arises during:

• System upgrades requiring full data transfer from older versions
• Platform standardization initiatives eliminating departmental silos
• Legacy system retirement projects with critical historical data

Each migration demands meticulous mapping work to ensure business continuity. Records must maintain relationships and history while adapting to new data structures.

3. System Integration and Interoperability

Organizations rarely operate within a single, all-encompassing system. Enterprise environments often rely on specialized systems that serve different functions, but exchange data regularly:

• Business systems sharing data with accounting systems
• Policy Administration Systems feeding data to Claims Platforms
• Rating Engines consuming data from Customer Information Files
• Document Management Systems linking files to Core Transactions

These integrations require ongoing data mapping to maintain accurate translation between different data models. What one system calls client_id might be customer_number in another – with entirely different value spaces.

4. Data Warehouse and Analytics Implementation

Building effective analytics capabilities requires consolidating data from multiple sources:

• Creating unified customer views across disparate systems
• Standardizing product data from multiple departmental sources
• Normalizing transaction records for meaningful trend analysis
• Building historical performance metrics from operational systems

Analytics projects often reveal the full scope of data inconsistency across an organization. The same entity might appear under multiple names, formats, and hierarchies – requiring extensive mapping work before meaningful analysis becomes possible.

There are two approaches to building a data mart when dealing with multiple systems, you may want to read our article about Simple Data Analytics for Insurance Brokers. It’s well applicable in other industry contexts.

Understanding Record Relationships – P&C Insurance Example

Let’s look at what happens in insurance. Agency Management Systems organize insurance data through intricate webs of relationships. These connections mirror the real-world complexity of insurance operations where policies link to carriers, clients, coverages, and claims.

Database keys anchor these relationships. Primary keys uniquely identify each record while foreign keys create connections between tables. This architecture enables the complex queries that drive insurance operations.

A typical AMS implements these relationships through several critical tables, among many:

• A carrier table with primary key carrier_id holds insurance company information
• A policy table references carriers using foreign key carrier_id
• A coverage table links to policies through foreign key policy_id

This structure maintains data integrity. When you write a new policy, you must select an existing writing company. The system prevents orphaned records that would corrupt your data.

When mapping between systems, these relationships become critical. Applied Epic might call a carrier Philadelphia Indemnity Insurance Company with ID 324567, while AMS360 uses Phil Indemnity with ID C18922. Simply matching the names falls short – you must establish these records represent the same entity while preserving all their relationships to policies, coverages, contacts, and many more.

The challenge deepens when systems use fundamentally different data models. Applied Epic organizes carriers in a flat structure, while AMS360 implements a hierarchical parent-child relationship for issuing papers. These architectural differences add substantial complexity to data mapping during system conversions or integrations.

The Data Mapping Challenge

Each system maintains its own ID space – the universe of unique identifiers it uses to track entities. These IDs rarely align between systems, even when they represent identical real-world concepts. Since IDs don’t match, you need to recognize entities by other fields. Often all you will have will be just similar names – and text variations mask identical entities.

Consider these variations for the same insurance company:

• Philadelphia Indemnity Insurance Co.
• Phil Indemnity
• PIIC
• Philadelphia Indmnty Ins

A single lengthier company name like this may easily have hundreds of case insensitive permutations with all flavours of abbreviations, prefixes, suffixes, additional words added, and misspellings. Humans are very creative in naming things when no common industry standard is in place.

Simple text matching fails to catch these variations. Exact matching only works for perfectly standardized data, which rarely exists in the real world. Fuzzy matching improves results but introduces false positives and misses context-specific patterns. Edge cases further complicate matching.

Let’s layer a challenge on the entire thing to break it even further. What if two mapped entities don’t share even a remotely similar name? Your business system most likely doesn’t track industry changes. Very common in insurance, by the way.

When carriers merge or rename – like Marquette National Life Insurance Company becomes Nassau Life Insurance Company of Texas – your data team must update records while preserving policy connections and reporting accuracy. This critical maintenance task often goes unnoticed until you need to match company names. Most brokers lack systematic ways to monitor carrier evolution in the market.

Industry knowledge is critical here, a simple deterministic matching algorithm won’t help here.

Data quality issues make it convoluted even further. Missing fields, inconsistent abbreviations, duplicate records, and data entry errors create noise that obscures relationships and entities.

Data mapping requires human oversight, expertise, and domain knowledge. It remains largely manual, often marginally supported by tools offering bare functionality.

So, who is left to deal with data mapping work, and where’s the source of their problems?

The Silent Heroes: Data Conversion Teams and Administrators

Data Conversion Analysts and Business Systems Analysts form the backbone of system integration projects. These specialists tackle the most complex aspects of data relationships while working against impossible deadlines.

For every data conversion project, someone worked until 2 AM mapping records through Excel sheets and vendor conversion portals you never knew existed. These silent teams keep your business running while staying invisible to leadership.

The Reality of Data Conversion Beyond Technology

Data conversion involves far more than technical knowledge. It demands a unique combination of skills, resilience, and attention to detail.

Data conversion specialists must master two complex systems simultaneously. They need deep understanding of data models, business rules, and edge cases in both environments. Few roles require such comprehensive technical knowledge across multiple platforms.

The decisions they make carry lasting consequences. A single mapping error can misalign hundreds of policies, creating downstream problems that remain hidden for months. The stakes increase dramatically with scale.

Data mapping isn’t just about matching text. It’s about preserving complex webs of relationships that define how your business actually operates.

Their work environment remains trapped in the past. While most business functions benefit from modern tools, data conversion specialists work with Excel spreadsheets and vendor portals designed a decade ago. They lack collaboration features, advanced validation capabilities, and even filtering – let alone intelligent assistance that other business functions take for granted.

The Gap in Data Mapping Creates Organizational Impact

This technical and process debt creates significant organizational costs.

Late nights become normal during critical project phases. Data conversion teams regularly log in at 2 AM because the tools and processes can’t meet project demands.

Burnout affects your most valuable specialists, leading to turnover that removes institutional knowledge at the worst possible time. The people who understand your data best face the greatest pressure.

Talent acquisition becomes a persistent challenge. Hiring experienced conversion specialists takes 3-6 months, with compensation requirements of $100-140K for senior roles. Organizations struggle to justify these costs for teams viewed as “support functions.”

You can’t bridge the gap in data conversion by throwing more bodies at mapping records. When your tools and workflows remain broken, additional staff just means more people being miserable in parallel.

Organizations that properly support their data conversion teams see faster migrations projects, quicker time to value post-M&A, more accurate data integration, and significantly reduced human costs.

Investing in better conversion tools and processes delivers clear business value.

What Data Migration Teams Need to Succeed in Mapping

Data Conversion Specialists need tools built specifically for their workflows. The current process wastes precious project time waiting for technical prerequisites that don’t directly contribute to mapping quality.

Often a full database backup restoration of the source system is needed before any mapping can happen. Then there’s the entire issue of having to match pairs between source and destination record by record.

Data Migration teams and analysts need interfaces designed for filtering records, and handling data relationships, not just text comparison. Current tools treat data as isolated records rather than connected entities with meaningful relationships. Excel, VLOOKUPs and XLOOKUPs are definitely not tools for data matching at scale!

Your analysts need collaboration capabilities that support team workflows. Most current solutions assume a single person working in isolation rather than teams coordinating complex mapping decisions.

They need intelligent assistance that learns from their expertise. The systems should become smarter through use, recognizing patterns that human experts identify and suggesting similar matches in the future.

Data migration teams don’t just need better tools. Bare tech and AI gimmicks won’t fix a workflow so profoundly broken.

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Your most valuable analysts and specialists need a completely reimagined process.

The entire process of data conversion remains fundamentally unchanged despite decades of technological advancement in every other area of business.

Organizations that properly support their data conversion teams see faster system migrations, more accurate data integration, and significantly reduced human costs. Investing in better conversion tools and processes doesn’t just make technical sense – it makes business sense.

The Solution Side of Effective Data Mapping

Effective data mapping requires more than just matching algorithms. A complete solution must address several critical areas.

Remember, these are prerequisites for improving Data Mapping by reorganizing the overarching process (i.e. Data Migration, Data Conversion).

Bi-directional API integration with your core system – your entity resolution platform should be able to easily integrate with your systems for read and write operations.

Human oversight is essential despite automation. The most effective solutions combine algorithmic matching with human judgment, particularly for edge cases and high-stakes mapping decisions.

Clean, end-user focused interface is a must. Your data migration teams and data admins lack usability. This alone

Generation of mapping files, your ideal solution should be able to produce a data mapping file or other form of output required for data conversion (e.g. OldNew file for Applied Epic or SmartMap export / push for Vertafore’s AMS360).

Data relationship preservation to ensure that connections between records survive the mapping process. The solution must understand how records relate to each other and maintain these relationships during integration.

Validation before changes protects production data. The solution should preview mapping results and allow review before implementation, preventing errors from reaching live systems.

Scalable performance becomes critical as datasets grow. The solution must handle large volumes of records without degrading performance or requiring excessive resources.

Machine Learning in Data Mapping

Machine Learning offers a promising approach to data mapping challenges, providing capabilities that traditional methods lack.

Pattern recognition across variations allows ML to identify matches that rule-based systems miss. By learning from examples, ML models can recognize that Phila. Indemnity Ins. Co. and Philadelphia Indemnity refer to the same entity.

Learning from past decisions enables continuous improvement. As users confirm or reject suggested matches, the system becomes more accurate over time, adapting to specific data patterns in your environment.

Confidence scoring helps prioritize human review. By indicating how likely suggested records could be correct matches, ML systems can direct human attention to the cases most likely to need intervention.

Human control remains central to effective ML-based mapping. The best systems use machine learning to augment human judgment rather than replace it, presenting suggestions for review rather than making autonomous decisions.

Edge case handling improves with domain-specific training. Models trained on insurance data perform better on insurance mapping tasks than generic models, recognizing industry-specific patterns and abbreviations.

At the same time, Machine Learning is only awesome when it builds on great end-user experience.

Usability, Workflow, and ML in Data Mapping, Featuring RecordLinker

RecordLinker tackles these problems head-on, because we realized that data migration / conversion teams are an underserved group of end-users.

We provide a specialized, insurance-first platform for Data Mapping in post-M&A system consolidations. Our solution lets your Data Migration Specialists and Business Systems Analysts handle tasks at scale while maintaining data quality.

RecordLinker is more than tech, because we reframe how Data Conversion and Admin Teams work. Your team needs a multiplier to their capabilities.

Real Integration That Respects Your Core System

We partner with Applied Systems and Vertafore to provide bi-directional API integration. RecordLinker can sync with your Applied Epic or AMS360 in real-time, letting your data team work with live data while preserving your core system as the source of truth. Changes made in RecordLinker flow back to your AMS seamlessly.

Data Mapping Support for Busy Data Migration Teams

RecordLinker helps Data Conversion teams handle agency acquisitions efficiently. Instead of waiting weeks for database restoration, teams can start mapping data immediately using basic exports – which effectively means saving 8 weeks on just the wait times. Our platform provides:

• ML-powered suggestions for company mapping.
• Integration with vendor conversion tools and compliance with required file formats.
• Project management features for conversion tracking.
• Data quality reports for your AMS and acquired source data.
• Library of Data Conversion formats supporting conversions from various types of systems into your Applied Epic or AMS360.
• Features that also support Ongoing Data Governance (e.g. configuring 300 new employees, bulk updates).

The platform costs less than hiring a single Data Conversion analyst while transforming how your entire team works. It’s a massive lever that increases throughput across your entire existing data team.

Suggested Reading About Data Mapping and Migrations

Check our recommended reading list with articles and pages discussing insurance’s problems. We want to help you understand why your data teams struggle, and what to do about it.

Taking care of your AMS data quality and post-M&A migrations is a process that requires a shift in thinking. Technology in P&C insurance can both fail and bring benefits – ultimately it’s about how that technology addresses the needs of the end-users and whether it contributes meaningfully to their work.

• Data Conversion Roles and Responsibilities in Insurance
• In-House, Outsourcing, and Tech in Data Conversion
• Insurance Agency Acquisitions – AMS Data Migration
• [Solutions] Data Conversion to Applied Epic
• [Solutions] Data Conversion to AMS360
• Simple Data Analytics for Insurance Brokers
• Machine Learning in Entity Resolution with Industry Examples

To learn more about how RecordLinker can help you improve the quality of your data, request a free demo!