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A comprehensive and exhaustive inventory: the art of aggregating, controlling, and consolidating everything


Transform your scattered data into a comprehensive repository


The Know & Decide Data Management engine automates what teams have been trying to do manually for years: to aggregate, reconcile, control, and validate your data to produce a consolidated inventory and an aligned CMDB, with quality indicators, explained discrepancies, and a concrete action plan.

Why a Data Management engine is essential ?


An IT fleet cannot be managed “by instinct”. The more your environments are hybrid (cloud, on-premises, virtualisation, SaaS), the more fragmented the data becomes, and the more the gaps between tools become invisible. A Data Management engine allows you to measure, consolidate, ensure reliability, and industrialise your repositories (inventory, CMDB, security, procurement) in order to make decisions based on factual, traceable, and sustainable information.

Auto-discovery solutions are useful… but they remain one source among others. They “see” what they can see, with their own logic, their own coverage, and their own limitations. As a result: you often get partial visibility, incomplete attributes, and data that is difficult to keep consistent over time.

A tool can be very good on endpoints, less so on servers, weak on the cloud, or blind to certain segments (isolated networks, specific environments, subsidiaries…). Without a multi-source approach, there is always a part of reality that is missing.

Workstations, servers, VMs, accounts, applications, licences, equipment… Each domain has its tools: ITSM/CMDB, security, MDM, virtualisation, cloud, IAM. No single source possesses both the fleet and all the necessary attributes.

Even when auto-discovery detects an asset, the attributes are often incomplete or unreliable (owner, location, status, criticality, link with contracts, backup tags, security compliance…). Without consolidation, you have lists… not an exploitable repository.

Main features

Manage the quality of your IT data, end to end

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Count and compare

The first step is to measure the actual coverage of your data: we count the assets present in each source relying on unique identifiers.

In a few seconds, you can see how many assets each tool detects and, above all, why the figures are not the same from one source to another.

Garantir la conformité des référentiels

After consolidation, the objective is to measure and secure the coverage between your sources. Specifically, we identify what should be present everywhere… but isn’t: missing servers on the antivirus/EDR side, VMs without backup tags, assets absent from the ITSM or poorly referenced.

Build a consolidated inventory

Once the checks have been completed, we build the K&D inventory by defining, attribute by attribute, which source is authoritative and in what order of priority.

This rules engine is then based on a library of 50 enrichment functions to validate and complete the data: detection of synonyms, removal of extraneous characters, standardisation of formats, completion of missing fields, normalisation of labels…

Decommission the "ghost" assets

Once the coverage is measured, we identify the assets to be decommissioned: those that appear in only one source and whose last activity date / "last seen" is too old.


Contrôler la qualité des données

Beyond volume and coverage, we check the quality of each attribute to ensure that your repositories are truly usable.

For each source, we measure completeness (filled fields), identify anomalies (empty values, inconsistent formats, naming conventions not followed), and highlight the key points to correct.

Fiabiliser et alimenter la CMDB

First, we compare the consolidated inventory to the CMDB in order to measure completeness (compliant / missing / present in only one repository) and to immediately identify discrepancies. Then, we apply the same quality controls to the CMDB as we do to your other sources: mandatory fields, formats, uniqueness, expected values, attribute consistency…

Once the data is validated, the update becomes industrialisation: scheduled synchronisation via API / CSV / SQL of the assets and the relationships (impacting/impacted), application of your "source of truth" rules and continuous normalisation.

 

The importance of aggregation


Aggregation refers to the process of summarising data from various sources.


The approach involves breaking down silos and establishing a cross-functional and comprehensive management of your data in order to feed, through all your available data sources, a global, reliable, and exhaustive inventory of your IT assets.

Manual entries increase errors, such as incorrect field assignments, typographical errors, or non-compliant statuses.


To address this, we have developed a flexible rules engine, configurable in natural language, that handles the most complex cases. Half a day's training is enough to become self-sufficient.

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A no-code & agentless solution

Our solution is designed to be easy to use, without requiring large technical teams.

  • No-code, to remain autonomous: add rules, controls, and new attributes via a simple language interface (filters contains, starts with, AND/OR operators, conditions, priorities).


  • Support for advanced cases: for more complex needs (regular expressions, specific rules, bespoke scenarios), our consultants are by your side to secure the configuration and industrialise best practices.


  • Agentless, to limit the load:No agent is required on the terminals. It is enough for your data to be accessible — API, exports, imported files — to feed and ensure the reliability of your repositories.


  • A co-constructed roadmap: The solution evolves continuously thanks to field feedback, with improvements and features guided by customer needs.

Scope and Mode of Operation


We maintain a live inventory: consolidated collection at night for data ready each morning.

These data feed dashboards for fleet management, obsolescence, and re-invoicing; anomalies are detected and corrected automatically or upon validation.

Upon ingestion, quality controls and exclusion rules prevent duplicates and false positives, then the sources are cross-referenced to reveal discrepancies.

Thanks to the "Golden Values", each attribute is fed from its most reliable source, with preference rules. The model is customisable: we start with around 15 key attributes, then we expand. Alerts, reports, and tickets are distributed at the desired frequency and integrate with your ITSM/CMDB.