How to Map Legacy Data

The average enterprise rewrite takes 18 months, yet 70% of these projects fail to meet their objectives or exceed their timelines entirely. The primary culprit isn't a lack of talent or modern tooling—it is the "Black Box" problem. When you attempt to map legacy data, you aren't just moving bits; you are performing digital archaeology on systems that often lack documentation (67% of legacy systems have none) and whose original authors left the company during the Obama administration.

Manual data mapping is the single biggest bottleneck in modernization. It currently takes an average of 40 hours per screen to manually audit, document, and map data flows from a legacy UI to a modern API. At Replay, we’ve seen this reduced to 4 hours.

TL;DR: To successfully map legacy data without the risk of a "Big Bang" failure, you must shift from manual documentation to Visual Reverse Engineering—using actual user workflows as the source of truth to generate modern data contracts and React components automatically.

The $3.6 Trillion Technical Debt Trap#

Global technical debt has ballooned to $3.6 trillion. For most Financial Services and Healthcare firms, this debt is concentrated in "zombie systems"—monoliths that work but are impossible to change because no one knows exactly how the data is structured under the hood.

When you try to map legacy data using traditional methods, you usually choose between three high-risk paths:

The "Big Bang" fails because it relies on assumptions. Developers look at a legacy SQL schema or a COBOL copybook and assume they understand the business logic. They don't. The true logic lives in the interaction between the user and the interface.

Why You Can't Map Legacy Data Without Visual Context#

In a regulated environment—like an insurance claims portal or a government benefits system—the data schema often hides complex conditional logic. A field labeled

might behave differently depending on whether a user clicked "Save" or "Submit" on a specific legacy screen.

If you map legacy data by only looking at the database, you miss the "Shadow Logic" embedded in the frontend. This is why manual mapping is so slow. Architects have to sit with legacy users, watch them work, and try to guess how the UI translates to the backend.

The Archaeology Problem#

Most enterprises spend 60% of their modernization budget just trying to understand what the current system does. This "documentation archaeology" is a waste of senior engineering talent. Instead of building the future, your best architects are deciphering 20-year-old spaghetti code.

The Replay Methodology: Visual Reverse Engineering#

Replay changes the paradigm. Instead of reading code to understand data, we record the application in motion. By capturing real user workflows, Replay observes the data as it is entered, transformed, and transmitted.

Step 1: Workflow Recording#

A business analyst or end-user performs a standard task in the legacy application (e.g., "Onboard a New Wealth Management Client"). Replay records the DOM mutations, network requests, and state changes.

Step 2: Automated Data Extraction#

Replay's AI Automation Suite analyzes the recording. It identifies every input field, validation rule, and data type. It then generates a clean, modern data contract.

Step 3: Generating the Modern Component#

Once the data is mapped, Replay generates documented React components that mirror the legacy functionality but utilize a modern tech stack.

typescript
// Example: Generated Data Contract from Replay Extraction
// Source: Legacy "Claims_Portal_V3" - Screen: PolicyUpdate
export interface PolicyUpdateContract {
  policyId: string; // Extracted from hidden field ID_99
  effectiveDate: ISO8601String; // Transformed from MM/DD/YYYY legacy format
  coverageAmount: number; // Sanitized from string with currency symbols
  isActive: boolean; // Derived from 'Y'/'N' legacy flag
  
  /** 
   * Business Logic Note: 
   * In the legacy system, if 'is_high_risk' was checked, 
   * the 'underwriter_ref' field became mandatory.
   */
  underwriterRef?: string;
}

// Generated Modern React Component using Replay Library
import { useForm } from 'react-hook-form';
import { Input, Checkbox, Button } from '@/components/design-system';

export const ModernPolicyForm = ({ initialData }: { initialData: PolicyUpdateContract }) => {
  const { register, handleSubmit } = useForm({ defaultValues: initialData });

  return (
    <form onSubmit={handleSubmit(d => console.log("Modern API Payload:", d))}>
      <Input {...register("policyId")} label="Policy ID" readOnly />
      <Input {...register("effectiveDate")} type="date" label="Effective Date" />
      <Input {...register("coverageAmount")} type="number" label="Coverage Amount" />
      <Checkbox {...register("isActive")} label="Policy Active" />
      <Button type="submit">Update Policy</Button>
    </form>
  );
};

Bridging the Gap: Mapping Complex Business Logic#

The hardest part of learning how to map legacy data is handling the "invisible" transformations. For example, a legacy system might store a user's name as a single 100-character string, but your modern microservice requires

, , and .

Manual mapping requires writing complex ETL (Extract, Transform, Load) scripts and hoping you caught every edge case.

💡 Pro Tip: Use Replay's Flows feature to visualize the state transitions. If a user enters data that triggers an error message in the legacy UI, Replay captures that validation logic as a requirement for your modern API contract.

Technical Debt Audit#

Before you map a single field, you need to know what is worth keeping. Replay provides a Technical Debt Audit that identifies:

• •Dead fields (fields that exist in the UI but are never sent to the backend)
• •Redundant workflows
• •Security vulnerabilities in data handling (e.g., PII being sent in plain text)

💰 ROI Insight: By identifying "dead" UI elements and data fields, one telecommunications client reduced their API surface area by 34% before writing a single line of new code.

From Black Box to Documented Codebase#

The goal of mapping legacy data isn't just to move to a new database—it's to ensure the system is maintainable for the next decade. This requires documentation that doesn't rot.

Because Replay uses Video as the Source of Truth, the documentation is inherently linked to the actual behavior of the application. If a developer wonders why a certain data mapping exists, they can watch the original recording of the legacy system to see the context.

Step-by-Step Guide to Modernizing a Legacy Screen#

1. •Capture: Use the Replay recorder to capture a full user session.
2. •Analyze: Use the Blueprints editor to review the extracted fields and data types.
3. •Refine: Map legacy field names (e.g.,
   text

   USR_01_LNAME
   ) to modern equivalents (
   text

   lastName
   ).
4. •Export: Generate the React components, TypeScript interfaces, and E2E tests (Cypress/Playwright).
5. •Validate: Run the generated E2E tests against the legacy system and the new modern component to ensure data parity.

typescript
// Example: Generated Playwright Test for Data Parity
import { test, expect } from '@playwright/test';

test('Data Parity: Legacy vs Modern Mapping', async ({ page }) => {
  // 1. Record legacy data submission
  await page.goto('https://legacy-system.internal/form');
  await page.fill('#USR_01_LNAME', 'Smith');
  const legacyPayload = await interceptLegacyRequest(page);

  // 2. Validate against Modern Contract generated by Replay
  expect(legacyPayload.USR_01_LNAME).toBe('Smith');
  
  // 3. Ensure the mapped modern field matches
  const modernMapping = mapLegacyToModern(legacyPayload);
  expect(modernMapping.lastName).toBe('Smith');
});

Security and Compliance in Regulated Industries#

When you map legacy data in Financial Services or Healthcare, you cannot simply upload your data to a public cloud AI. You are dealing with SOC2, HIPAA, and GDPR requirements.

Replay is built for these environments:

• •On-Premise Deployment: Run the entire extraction engine within your own VPC.
• •PII Masking: Automatically redact sensitive data during the recording and extraction process.
• •Audit Trails: Every data mapping decision is logged, providing a clear trail for compliance officers.

⚠️ Warning: Never attempt to map legacy data containing PII using unvetted LLMs or public AI tools. The risk of data leakage is high, and most legacy systems contain sensitive data in unexpected fields.

Frequently Asked Questions#

How long does legacy data extraction take with Replay?#

While manual mapping takes ~40 hours per screen, Replay reduces this to approximately 4 hours. For a standard enterprise application with 50 screens, you are looking at weeks instead of years.

What about business logic preservation?#

Replay captures the "Observed Logic." By recording the inputs and the resulting outputs (network calls/UI changes), Replay identifies the functional requirements. If the legacy code has "hidden" logic that never results in a state change or network call, it is often redundant and can be safely ignored during modernization.

Does Replay support green-screen or mainframe applications?#

Yes. As long as the application is accessible via a browser or a terminal emulator that can be rendered in a web context, Replay can record the interactions and map the resulting data flows.

How does this handle API contracts?#

Replay automatically generates OpenAPI (Swagger) specifications based on the intercepted network traffic during the recording phase. This ensures your frontend and backend teams are working from the same source of truth from day one.

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Ready to modernize without rewriting? Book a pilot with Replay - see your legacy screen extracted live during the call.