In multi-site organizations, data exists. It is produced, updated and used locally. But when data is generated in a heterogeneous way, without a shared framework, it cannot serve as a basis for consolidated decision-making. 

And it is precisely this structural gap that weakens the robustness of the insurance program. 

The issue is not data collection but heterogeneity. 

Each site operate with own reference point. Financial data comes from accounting systems, technical information is built close to operations, and loss histories often come from external stakeholders. These sources coexist, but they are not always structured according to the same logic. 

On top of this, local practices vary: different levels of detail, different update frequencies, and differing interpretations of the same asset categories. 

Asa result, two comparable sites can produce values based on entirely different methodological foundations, without this being visible at group level.

Two comparable sites can produce values based on different foundations, without this being immediately visible at group level. 

This observation is widely documented by PwC which highlights that siloed or heterogeneous data directly limits the quality of analysis and the ability to produce reliable reporting at group level. 

Grant Thornton, in its work on data quality in insurance, also emphasizes that data governance has become both a competitiveness and compliance driver, and that internal controls must cover the entire data lifecycle, starting from collection. 

An unstructured process: tangible impacts on the insurance cycle 

Value declaration campaigns still often rely on largely manual setups: shared files, questionnaires, successive follow-ups, and informal validations. While these approaches make it possible to gather information, they make it extremely difficult to ensure consistency over time. 

The effects are well known to risk managers: 

• Gaps between sites that are difficult to explain and impossible to substantiate 

• Valuation assumptions that are not based on consistent foundations from one site to another 

• Partial data traceability, making any historical view fragile 

• Consolidation processes that require frequent rework, consuming time and increasing the risk of errors 

These situations reflect the absence of a common framework from the point of data collection. 

Yet heterogeneous data, even when available in large volumes, introduces uncertainty at every stage of the insurance cycle. It complicates the analysis of exposed values, weakens internal decision-making, and makes it more difficult to justify values in discussions with insurance partners. 

Structuring data collection: what it changes in practice 

Structuring data collection does not mean changing local teams’ operational practices, but organizing the process within a common framework applicable across the entire scope. 

This implies: 

• Standardized input formats, adapted to the different types of values: assets, inventories, gross earnings, losses 

• A clear definition of roles and responsibilities at each stage of the campaign 

• Centralized data flows, with validation statuses visible in real time 

• The ability to track data over time, from one campaign to the next, without any break in historical continuity 

Within this framework, data collection becomes a managed process. Data gains in clarity, comparability, and reliability. 

Gross earnings require particular attention in this context. Depending on regions, practices may differ and directly impact declared values. 

Gross earnings or net profit? 

Standardizing value collection makes it possible to build structured and traceable data on this item, fundamentally changing the nature of discussions with insurers. 

It’s not the amount that makes the difference. It’s the quality of the upstream data and the ability to justify it. 

Structured data as a credibility driver with insurers 

Even in a soft market, insurers are increasingly scrutinizing how declared values are built; the quality of upstream data has become a prerequisite. 

At the 33rd AMRAE Conference in February 2026, data transparency and the reliability of information provided to insurers were identified as key priorities for risk managers. The more structured and verifiable the data, the more insurers can take it into account favorably in underwriting conditions. 

An insurer is not looking for a perfect value. They are looking for a value that is consistent, structured, and objective. 

Declared values that are consistent, traceable over time and comparable across sites enable risk managers to maintain a strong position at renewal. 

Regain control over value data 

Companies have access to a significant volume of data. The challenge is to structure it, make it readable, and ensure it can be reliably used over time. 

Revisiting how data is collected, organized, and tracked over time often helps clarify situations perceived as complex and provides a more robust understanding of the insured assets. 

This is typically where the difference lies between a quantified figure and a declared value that is truly under control. 

COLLECT, SENOEE’s solution, structures this process end-to-end: 

• The collection of asset values, inventories, and gross earnings, 

• Standardization at group level, 

• Real-time monitoring of data quality, 

• The production of an objective reference framework for insurers and brokers. 

Looking to structure value data collection across your group?  Request a personalized demo of the solution