In-House Scripts vs Enterprise Data Migration Tools for Insurance 2026: A P&C Carrier’s Binary Decision Guide

Why the in-house vs enterprise decision still trips up P&C carriers in 2026

The data engineer I spoke with at a US P&C mid-tier carrier - $850M GWP, two lines, mostly personal auto - had a specific complaint: “My CIO read a blog post about how enterprise migration tools save 30 to 40 percent, signed a $600K contract, and now I am 7 months in spending most of my time writing custom Python anyway because the tool does not understand ACORD.” That carrier had bought an enterprise tool and was still writing in-house scripts. They had both the license cost and the engineering cost, with neither approach actually closing the program.

The binary decision - in-house scripts versus enterprise data migration tools - is, in my experience, the most poorly made decision in insurance data migration. It is poorly made because the two options are usually presented as a single binary, when the actual choice for most mid-tier P&C carriers is a four-way decision (in-house, enterprise ETL, iPaaS / cloud-native, insurance-native platform). The broader 4-category picture is covered in the insurance data migration tools pillar article. This sister article goes deep on the in-house-vs-enterprise subset of that question, because it is the subset where carriers most often choose wrong.

Three patterns explain why the binary still trips carriers in 2026:

1. ‍The cross-industry consulting numbers cited everywhere are not insurance numbers. Carriers see generic claims like “30 to 40 percent faster with enterprise tools” or “246% ROI over three years” - both from cross-industry studies in vendor marketing. They are not wrong for the sectors they measure; they are just not insurance. Insurance data has ACORD, NAIC retention, policy lifecycle states, and claim adjudication enums that generic ETL platforms handle at the technical layer but not at the business layer. The savings shrink fast when the business layer is rebuilt by hand. In my experience watching mid-tier carriers cite these numbers in their business cases, the gap between projected savings and actual savings is the first surprise of the program.
2. ‍The “we already have an enterprise ETL license” pattern. Finance bought the platform two years ago for the data warehouse. The migration team is asked to use it. The team spends 6 months building insurance-specific transformation logic on top of the tool, which is the work scripts would have done anyway - except now there is also a license cost.‍
3. The “our data team is strong, we will script it” pattern. Engineering pride is real. Senior data engineers know they could build the migration. What they do not always know is that an insurance migration at $1B GWP scale is 18 months of work, not 4. By month 6, the team is in over their head; by month 9, the carrier is starting over with an enterprise tool.

This article is the framework I would walk through with the data engineer in that opening anecdote. It is written for CIOs, enterprise architects (Daniel-types), and lead data engineers at US P&C carriers between $500M and $5B GWP. For the broader insurance data migration challenges that drive the tool decision, the pillar guide is the strategic read.

In-house scripts vs enterprise data migration tools - direct answer

In-house scripts vs enterprise data migration tools is a binary subset of the broader insurance data migration tool decision. In-house scripts (hand-coded Python, SQL, PowerShell) work for portfolios under 250,000 policies on a single line of business with well-documented source systems. Enterprise data migration tools (generic ETL platforms like Talend, Informatica, IBM DataStage as category examples) work for cross-industry data integration at scale but handle only the technical 70% of insurance migration - the remaining 30% (ACORD reconciliation, policy lifecycle states, claim adjudication enums, regulatory continuity) gets rebuilt by hand. For mid-tier P&C carriers between $500M and $5B GWP, the honest answer is usually neither in pure form: a hybrid model (enterprise tool plus targeted custom scripts) or an insurance-native platform is the right call. The 8-question decision tree in Section 8 resolves the binary into a working answer.

The short version of the binary framing:

• In-house scripts are not “free.” The engineering cost typically lands between $800K and $2.5M for a mid-tier P&C carrier migration.
• Enterprise tools are not “complete.” Generic ETL handles 70% well; the 30% insurance-specific layer is rebuilt by hand regardless.
• The honest pattern for $500M-$5B GWP is hybrid - tool plus targeted scripts - or insurance-native.
• The wrong question is “scripts or tool?” The right question is “which combination of scripts, tool, and platform fits my portfolio, my team, and my timeline?”

The next sections expand each side of the binary, then bring them together in the decision tree (Section 8) and the hybrid model (Section 7).

When in-house scripts work for insurance - 6 specific scenarios

In my experience, in-house scripts can be the right answer for a specific subset of insurance migrations. Below are the six scenarios where I have seen scripts work cleanly. If the carrier matches all six, scripts are likely the right call. If the carrier matches 4 of 6, the answer is more nuanced.

Scenario 1: Portfolio under 250,000 policies

Below 250K policies, the data volume is manageable with hand-coded extracts, the test cycle fits in a sprint, and the cutover window is short enough that a focused team can run it end-to-end. Above 250K, the script approach starts straining at the seams.

Scenario 2: Single line of business, or two closely related lines

Personal auto only. Or personal auto plus motorcycle. Multi-line carriers - auto plus home plus commercial plus workers’ comp - have data shapes diverse enough that scripts become a per-line workstream, and the coordination overhead exceeds the cost savings.

Scenario 3: Source system has a documented, modern schema

If the source is a 2015-era SQL Server with documentation, scripts can handle it. If the source is a 1996 mainframe COBOL system with no documentation, scripts can handle it - but the documentation work eats the savings.

Scenario 4: Engineering team includes 2+ senior data engineers full-time

A senior data engineer is not a data engineer who can write SQL. It is an engineer who has done 2+ migrations before, knows the difference between optimistic-locking and pessimistic-locking semantics, can read execution plans, and has built parallel processing pipelines. Most mid-tier P&C carriers have 0 to 1 such engineers. Scripts require at least 2.

Scenario 5: No CAT season constraint

For US P&C carriers writing property lines, June through November is off-limits for cutover. Scripts work fine outside that window. The constraint is on the team capacity question - the scripts approach has thinner reserves to handle slippage that pushes cutover into CAT season.

Scenario 6: Historical data retention scope is limited

If the regulatory retention window for in-scope data is under 5 years, the volume is manageable. If it is the full 7-10 year NAIC retention, the historical reconciliation work compounds - and the script approach has less built-in support for retention archive integration.

If all six are true, in-house scripts can finish in 3 to 6 months at $300K to $800K. I have advised three sub-$500M GWP carriers in the last 4 years to take this approach. All three finished on time.

If 4 of 6 are true, the answer is more cautious. If 3 or fewer are true, scripts are probably not the right call. The 8-question decision tree in Section 8 formalizes this.

When in-house scripts fail catastrophically - 10 anti-patterns

I will state this directly: the patterns below are the ones I have personally watched kill carrier script-based migrations. They are recurring and they are predictable. In my experience advising carriers in this category, the failure pattern is almost always one or more of these ten - and almost always discovered too late to recover cheaply. The broader insurance data migration challenges pillar guide covers the strategic-level patterns; this section is the tactical version specific to the script approach.

Anti-pattern 1: Underestimating the volume by 5-10x

The team estimates the data work based on the in-force policy count. They forget cancelled policies (typically 2-3x in-force volume), claim history (5-10x), endorsements and audit transactions (3-5x). Total record count ends up 10x higher than initial scope.

Anti-pattern 2: No data dictionary, ever

Scripts get written without a formal data dictionary. By month 6, the team cannot answer “what was the business rule that maps source field X to target field Y.” The scripts become tribal knowledge that walks out the door when an engineer leaves.

Anti-pattern 3: Edge case discovered at month 3 is actually a major domain

Engineering optimism is the cause. “We will handle that edge case later.” At month 3, the edge case turns out to be 40% of historical claims. The script architecture cannot stretch to cover it without a redesign.

Anti-pattern 4: ACORD reconciliation built ad-hoc

Scripts do not have built-in ACORD validators. The team writes ad-hoc validators in Python. At cycle 2 testing, the validators miss 30% of the cases that the formal ACORD AL3 spec would have caught.

Anti-pattern 5: No audit trail per record

The scripts produce data but not the source-to-target traceback for every record. State DOI examination 18 months later asks for the audit trail. The team scrambles to produce it post-hoc from logs that were never designed to support it.

Anti-pattern 6: No automated rollback

The scripts run forward. Rolling back means rerunning the legacy load from backup, which takes 12-24 hours. This breaks the 4-hour rollback budget that production migrations need.

Anti-pattern 7: Test environment built with production data

To “get realistic results,” the team copies production PII into test. This is a NAIC #668 / NY DFS 500 violation. State DOI examination flags it as a finding.

Anti-pattern 8: Documentation written “after we finish”

It never gets written. The migration succeeds technically, but six months later when the carrier needs to migrate from staging to production parallel-run, the institutional knowledge is gone.

Anti-pattern 9: Single point of engineering knowledge

The “we will script it” approach often concentrates in one or two senior engineers. When that engineer takes vacation in week 14 of execution, the program pauses. When that engineer leaves the company in month 8, the program enters crisis.

Anti-pattern 10: The carrier underestimates the cost of the 30% gap

Scripts do the same 70% / 30% split as enterprise ETL. The 30% is insurance-specific business logic. The carrier estimates the 70%; the 30% is what eats the timeline. Total effort lands 2-3x initial estimate.

I have personally watched four mid-tier P&C carriers go through this pattern. In each case, the script effort ran to month 9-12 before the carrier bought an enterprise tool or insurance-native platform - which then required 4-6 months to backport the existing data and another 6-9 months to finish. The total program cost was 2-3x what a tool-first approach would have cost.

When enterprise tools win on insurance data

Enterprise data migration tools (Talend, Informatica, IBM DataStage, Microsoft Azure Data Factory, AWS Glue as category examples - I am not recommending any specific one) are real tools that solve real problems. In my experience evaluating these platforms against insurance migration needs, the question is which problems each tool actually solves, and how well, for insurance data specifically. The broader insurance data migration challenges pillar guide covers the strategic-level reasons insurance is hard; this section focuses on what enterprise ETL handles within that.

What enterprise tools do well

The capabilities below are areas where enterprise ETL outperforms hand-coded scripts:

• Connector libraries. Pre-built connectors for 100+ source and target system types. Worth significant engineering time saved versus building connectors by hand.
• Visual data mapping interfaces. Business analysts can review and validate mappings without reading code. This is real value for the regulatory continuity workstream.
• Audit logging at the technical layer. Every job run, every record, every transformation - logged automatically. The audit trail is not free, but the foundation is built.
• Parallel processing. Horizontal scaling across nodes for large data volumes. Hand-coded parallelization takes 4-6 weeks; in tools, it is configuration.
• Job orchestration. Scheduling, dependency management, monitoring, alerting - all out of the box.
• Recovery and retry semantics. When a job fails partway through, enterprise tools support partial restart. Scripts often have to rerun from the beginning.

For a carrier whose primary data work is operational ETL into a data warehouse, enterprise ETL is the right answer. For carriers whose work is insurance data migration specifically, enterprise ETL covers part of the problem - which is where Section 6 picks up.

For the underlying insurance data migration best practices that any tool needs to support, the best-practice article covers the practice-level framework.

When enterprise tools struggle on insurance data

I will be honest about where enterprise ETL platforms fall short for insurance migrations specifically. In my experience watching teams force-fit generic enterprise ETL onto insurance data shapes, the struggles below are not deficiencies of the tools; they are mismatches between tool design (cross-industry general-purpose) and the work (insurance-domain specific).

Struggle 1: ACORD reconciliation is not built in

Generic ETL maps source field to target field. It does not validate against ACORD AL3 or check that an in-flight transformation preserves ACORD-compliant output. That validation is built by hand on top of the platform. The build effort is typically 8-16 weeks for a mid-tier carrier.

Struggle 2: Policy lifecycle states require custom modelling

A policy is not a row. It is a temporal entity with quote-to-application-to-in-force-to-endorsement-to-cancellation-to-reinstatement-to-renewal-to-claim states. Generic ETL treats policy as a flat table. The lifecycle modelling has to be rebuilt in business rules layered on top.

Struggle 3: Claim adjudication enums require explicit dictionaries

Legacy claim systems accumulate 50-200 status enums over 20+ years. Mapping them to a target system requires explicit enum-to-enum dictionaries, with business owner sign-off. Tools do not bring these dictionaries; they have to be built per carrier.

Struggle 4: Regulatory reporting reconciliation is custom work

NAIC Schedule P loss reserves, state DOI rate filing data, Schedule F reinsurance reporting - all need to reconcile through migration. Generic ETL does not know what these are. Reconciliation logic is custom per carrier.

Struggle 5: CAT season blackout is a business rule, not a tool feature

No enterprise ETL platform has a built-in concept of “do not run cutover during June-November for P&C.” This is a business rule layered on top, enforced by the program team.

The summary: enterprise ETL handles roughly 70% of an insurance migration well. The remaining 30% is built by hand. For the team building that 30%, the choice is the same one this article opened with - in-house scripts or insurance-native platform. The recursion is real.

The hybrid model - enterprise tool plus targeted custom scripts

This is the pattern I see at the most successful mid-tier P&C carrier migrations: a hybrid model that pairs an enterprise tool for the 70% with targeted custom scripts for specific 30% transformations. Done well, this captures the connector library and orchestration value of the tool while handling the insurance-specific logic in scripts that the team owns. In my experience, the hybrid model is the answer for maybe 40-50% of mid-tier P&C migrations - more than carriers initially expect.

Four hybrid patterns I have seen work:

Hybrid pattern 1: Enterprise tool as orchestrator, Python for transformations

The enterprise tool runs the pipeline. Each transformation step is either a tool-native operation (for the technical 70%) or a Python script invocation (for the insurance-specific 30%). The Python scripts handle ACORD validation, enum mapping, lifecycle state derivation, reserve calculation.

This pattern works when: the team has Python skill, the enterprise tool supports external script invocation cleanly (most do), and the audit trail captures the Python step as a first-class operation.

Hybrid pattern 2: Enterprise tool for source-to-staging, scripts for staging-to-target

Stage the data using enterprise ETL with minimal transformation. Run insurance-specific business logic in Python scripts against the staging environment. Load to target with enterprise tool.

This pattern works when: the staging environment can hold full production volume, the script run between stages has clear input/output contracts, and reconciliation reports run against the target only.

Hybrid pattern 3: Enterprise tool plus insurance-native validation layer

The enterprise tool handles extract and load. A separate insurance-native validation layer (purpose-built or third-party) handles ACORD compliance, NAIC reporting reconciliation, and regulatory continuity checks.

This pattern works when: the carrier has access to insurance-native validation tooling, the contract between the two systems is well-defined, and the operational responsibility is clear.

Hybrid pattern 4: Phased - enterprise tool for early, scripts for tail

Early migration cycles (highest-volume, most-standard data) run on enterprise tool. Later cycles (lowest-volume, most-bespoke data) run on custom scripts. This is the pattern that fits carriers with a “long tail” of unusual policies or claims that do not fit the standard pipeline.

Hybrid governance

The hybrid model fails when no one owns the boundary. I recommend a named hybrid architect (typically Daniel-type enterprise architect) who owns the contract between the tool side and the script side, with sign-off on every boundary change. Without that ownership, the hybrid becomes a frankenstein and the program runs into governance gridlock at month 8.

The 8-question binary decision tree

The 8 questions below resolve the in-house vs enterprise binary into a working answer for a specific carrier. Score each yes / no honestly. The tree resolves at the bottom.

How to read the score

• Scripts score 4+ (out of 5 from Q1-Q5): In-house scripts may work cleanly. Validate Section 3 scenarios match.
• Tools score 4+ (out of 5 from Q1-Q5): Enterprise tool category is the working starting point. Validate Section 5 capabilities match needs.
• Mixed score 2-3 each: Hybrid model (Section 7) is likely the right answer. Pick the pattern that fits your team and timeline.
• Insurance-Native score 1+ (from Q7 or Q8): Consider the insurance-native category specifically. The broader 4-category framework in the insurance data migration tools pillar article covers that decision, and the insurance data migration challenges pillar guide covers the strategic context.

This 8-question tree is deliberately shorter than the 15-criteria matrix in the main insurance data migration tools article. The two artefacts work together: use this tree for the binary subset (scripts vs tools), then use the 15-criteria matrix for full vendor evaluation once the category is decided.

Mid-program switching - when to abandon scripts

Sometimes the carrier starts with in-house scripts and discovers, mid-program, that the approach is not going to finish. The honest version of this conversation is what I would tell a CIO who calls me 7 months in.

The five symptoms that signal a mid-program switch is needed

• Symptom 1. Initial 6-month timeline has already slipped to 12 months, and the team cannot articulate when month 12 will be.
• Symptom 2. ACORD reconciliation effort is consuming more than 40% of engineering time, with no clear path to completion.
• Symptom 3. The audit trail does not exist in production-quality form, and the team has no plan to build it before cutover.
• Symptom 4. Two or more senior engineers have left the program in the last 6 months. Replacement ramp time is 8-12 weeks each.
• Symptom 5. State DOI examination is scheduled within 18 months, and the team cannot confidently produce the migration documentation an examiner would ask for.

If 3 or more of these are present, the script approach is not going to finish at the original budget or timeline. The honest move is to switch.

What the switch costs

The mid-program switch from scripts to enterprise tool or insurance-native platform costs roughly:

• 4-6 months to backport the existing partial migration into the new tool’s data model.
• 6-9 months to complete the migration on the new tool.
• $500K-$1.5M in tooling, services, and engineering time.

Total switch cost: 10-15 months and $500K-$1.5M, on top of what has already been spent on scripts. Painful, but smaller than the cost of finishing on scripts that are not going to land.

What the switch buys

• A tool or platform that handles the audit trail, reconciliation, and regulatory continuity that scripts were not delivering.
• A program timeline that can be defended at board level.
• A migration outcome that can be presented to state DOI examiners with confidence.

I have advised three mid-tier carriers to make this switch in the last 4 years. All three finished the migration within 18 months of the switch. None of them would have finished on scripts.

For the broader readiness picture that prevents the mid-program switch from being needed at all, how to prepare for insurance data migration covers the readiness phase that determines whether scripts are the right call before execution begins.

Decerto reference - why Generali got custom tooling, not enterprise ETL

The most direct reference for the binary decision in this article is the Generali Group Poland data migration delivered by Decerto. The decision Generali and Decerto made - and why - is the working example for the framework above.

The setup

When Generali Group Poland acquired another insurance company, the migration required moving all insurance products, business processes, and customer data from the acquired company’s IT architecture into Generali’s infrastructure. The acquired data was, in Generali’s own published case study, of poor quality - requiring validation, correction, and standardization.

Why neither pure scripts nor pure enterprise ETL was the answer

Run the 8-question decision tree against this scenario. Generali is a multi-line carrier (Q2 no), with complex legacy schema (Q3 no), with full historical retention scope (Q5 no), and with ACORD-equivalent cross-line reconciliation needs (Q8 yes). Pure scripts would not have worked at this complexity. Pure generic enterprise ETL would have left the 30% insurance-specific logic to be built by hand.

The answer in Generali’s case: a custom-built migration tool that combined the orchestration and pipeline capabilities of an enterprise platform with the insurance-specific validation, correction, and reporting of a purpose-built solution. From the published case study, the tool handled six functions in one platform: import, validation, correction and standardization, transformation, export, and reporting.

What the decision bought

The cutover ran over a single weekend. The published outcomes: balance of financial transactions remained intact, no data loss occurred, and data quality improved through validation and correction. The decision to invest in custom tooling - rather than force-fit a generic enterprise ETL with bolt-on insurance logic - is what produced the outcome.

For mid-tier US P&C carriers, the same trade-off applies. The 8-question tree above is the working filter. The Migration Architecture Review (Section 12) is where we translate the tree to your specific portfolio and capacity.

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FAQ

When should I use in-house scripts vs enterprise data migration tools for insurance?

The 8-question decision tree in Section 8 resolves the question. As a short rule: in-house scripts work for portfolios under 250K policies, single line of business, well-documented source systems, and 2+ senior data engineers full-time. Above any of those thresholds, the tool or hybrid category becomes the working answer.

What are the risks of in-house data migration scripts for insurance carriers?

The 10 anti-patterns in Section 4: volume underestimation, no data dictionary, edge cases that turn out to be major domains, ad-hoc ACORD reconciliation, no audit trail per record, no automated rollback, production PII in test environments, missing documentation, single point of engineering knowledge, and the 30% insurance-specific gap consuming most of the timeline.

How do I know if I need an enterprise migration tool for insurance data?

If your portfolio is over 250K policies, you write multiple lines of business, you have less than 2 senior data engineers full-time on the program, or your regulatory retention window is the full 7-10 years, enterprise tooling becomes increasingly necessary. Score the 8-question decision tree honestly.

Can I combine in-house scripts with enterprise tools for insurance migration?

Yes. The hybrid model in Section 7 covers four working patterns: enterprise tool as orchestrator with Python for transformations, enterprise tool source-to-staging with scripts staging-to-target, enterprise tool plus insurance-native validation layer, and phased - enterprise tool for early cycles, scripts for tail data.

When should I switch from in-house scripts to an enterprise migration tool mid-program?

When 3 or more of the 5 symptoms in Section 9 are present: timeline slipped beyond clear recovery, ACORD reconciliation consuming 40%+ of effort, audit trail not in production form, 2+ senior engineers have left, or state DOI examination scheduled within 18 months. The switch costs 10-15 months and $500K-$1.5M but is smaller than the cost of finishing on a script approach that is not going to land.

Are in-house scripts cheaper than enterprise data migration tools for insurance?

In-house scripts have zero license cost but typical engineering effort of $800K to $2.5M for a mid-tier P&C carrier migration. Enterprise tools have license costs of $400K to $900K over 3 years plus implementation work. Total cost is often similar; the difference is where the cost falls (capex vs opex) and what is in scope (tool capability vs custom build).

What is the team capacity needed for in-house scripts at insurance scale?

At minimum: 2 senior data engineers full-time for 12-18 months, 1 lead data architect for the duration, 1 QA lead from month 4, and 1 business analyst per major data domain (policy, claims, party, financial). Most mid-tier P&C carriers do not have this depth and have to hire or contract for it.

Do enterprise migration tools handle ACORD reconciliation for insurance?

Generic enterprise ETL platforms do not include native ACORD support. ACORD validation, AL3 format handling, and ACORD-to-ACORD translation must be built on top - typically 8-16 weeks of custom work for a mid-tier carrier. Insurance-native platforms include native ACORD support; this is one of the main reasons carriers select that category over generic ETL.

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Talk to Decerto about the binary decision

If you read one section of this article on in-house scripts vs enterprise data migration tools, I would point you here.

The binary decision compounds. Carriers that decide right finish at the original budget and timeline. Carriers that decide wrong either start over at month 9 (with the wrong tool) or scramble at month 12 (with scripts that are not going to land). Both outcomes cost 2-3x what the right decision made once would have cost.

A free 30-minute Migration Architecture Review with me (Janusz Januszkiewicz). Vendor-neutral. I will not tell you to buy Decerto’s Data Migrator if scripts or a different category fits your scope better. I bring 15+ years of insurance migration experience and a senior Decerto architect. You bring your current state and your top three binary questions. We leave with a scored 8-question decision tree for your portfolio, an honest recommendation for the right combination of scripts, enterprise tool, and insurance-native platform, and a written list of the risks you face on the current path.

Decerto Data Migrator is built for mid-tier P&C carriers between $500M and $5B GWP. For sub-$250M GWP single-line carriers, in-house scripts can be the right call - we will tell you that directly. For $5B+ enterprise carriers running on Guidewire ClassicSuite, Guidewire’s own services partners are the better fit. The honest sweet spot is mid-tier P&C, multi-line, ACORD-heavy, with a team that is good but not deep enough to script the whole thing.

The framework in this article is the same one used on the Generali Group Poland acquisition migration, on the Warta agent platform consolidation, on the BNP Paribas Cardif claims handling centralization, and on US mid-tier engagements that remain under NDA. The 8-question tree is the working filter that resolves the binary into a real recommendation.

Sources and citations

1. NAIC. (2024). Insurance Data Security Model Law (#668). National Association of Insurance Commissioners.
2. NAIC. (2024). Standards for Safeguarding Customer Information (Model Law #672). National Association of Insurance Commissioners.
3. NIST. (2024). Cybersecurity Framework 2.0. National Institute of Standards and Technology.
4. ACORD. (2025). ACORD Standards - AL3 and XML reference documentation.
5. NY DFS. (2024). 23 NYCRR 500 - Cybersecurity Requirements for Financial Services Companies. New York Department of Financial Services.
6. Gartner. (2025). Magic Quadrant for Data Integration Tools.
7. Forrester. (2025). Wave: P&C Insurance Solutions.
8. McKinsey & Company. (2025). State of Insurance 2025.
9. Deloitte. (2025). 2026 Insurance Industry Outlook.
10. Aite-Novarica Group. (2024). P&C Insurance Core Modernization Report.