THE COST OF BUILDING
Building your data model: a never‑ending project?
Connectors, modelling, maintenance, dashboards… Many companies realise too late that they wanted to run their business, not manage an infrastructure.
The launch is only the beginning.
Comparing the initial cost of development with the price of a subscription is not enough.
Building an infrastructure that includes connectors, a semantic layer, historisation and business logic can take 12 to 24 months and more than €200,000. It then has to be maintained, secured and continuously evolved.
The "build it yourself" iceberg.
The €200,000 and the 12 to 24 months are the visible part. What lies beneath gradually undermines everything you have built.
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Maintenance
Every update to your ERP, payroll software, or CRM can break a connector. Your teams monitor, fix, and re‑test continuously.
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Support
Incorrect data at 6pm, the eve of a Board meeting: who do you call? Your data developer, if they are available.
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Governance
Who validates that data is reliable? Who arbitrates when accounting and the CRM give contradictory figures? Governing an internal model is a full‑time role.
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Security
GDPR, encryption, sovereign hosting, traceability: these requirements evolve with regulation. Who tracks them in your organisation?
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Innovation
Your internal tool is frozen at the version from the day you stopped funding it. Every new standard demands a new budget.
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Expertise
Building and maintaining a finance + data model requires profiles who master both data engineering and financial logic. These profiles are rare, expensive, and difficult to retain.
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Historisation
Setting up a robust SCD, managing versioning by close period, faithfully reconstructing any past situation: this is one of the most underestimated workstreams. It is also one of the most fragile to maintain.
Your value lies in the decision, not in the infrastructure.
If you build your own data infrastructure, you do not become a data‑driven company. You become a software company that also does finance.
Your team was hired to manage financial performance, not to maintain data pipelines. What your shareholders expect from you is the decision, not the engineering.
The question is not "can we build this?" (technically, yes). The question is: "do we want to be responsible for making it work, evolve, and stay secure, year after year?"
What you take on, depending on the choice you make.
| Dimension | Build in‑house | EMASPHERE |
|---|---|---|
| Time to production | 12 to 24 months | A few weeks |
| Initial investment | €200,000 + | Predictable subscription |
| Connector maintenance | Your team, continuously | Included : 250+ connectors maintained by EMASPHERE |
| Support in case of incident | Depends on your internal team | Specialist finance support |
| Data governance | To build, document, and maintain | Auditable model, traced rules, native historisation |
| Security and compliance | Your responsibility | Sovereign hosting, encryption, traceability included |
| Innovation (AI, MCP, new standards) | You must fund every evolution | Automatic: you benefit from every platform update |
| Finance + data expertise | To recruit and retain | Built on 10,000+ clients, available immediately |
| Scalability | Limited by internal resources | Multi‑entity, multi‑currency, cross‑domain native |
| Technical debt risk | High : the model ages without continuous investment | None : roadmap and updates managed by the publisher |
Three approaches, three levels of commitment
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Build in‑house
You control everything. But you become responsible for everything: maintenance, security, governance, innovation, support. Your model will be relevant on launch day. In 3 years, it will have fallen behind on AI, on new standards, on your business needs. You will pay to bring it up to date, or you will make do.
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Using the ERP with a generalist AI
Your ERP covers your transactional data. An AI on top can extract insights from it. But the AI queries raw, unconsolidated data, with no semantic layer. It confuses entities, ignores your consolidation rules, and does not understand your analytical axes. You get fast answers on a fragile foundation.
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Deploying EMASPHERE
A native finance intelligent data warehouse, cross‑domain, with semantic layer, native historisation, and pre‑configured business enrichments. Deployed in weeks, not months. Continuously maintained and enriched: support, innovation, security, and governance are included in the subscription (not extras). AI‑callable via native MCP, so that any AI queries trusted data, not raw data. 10,000+ companies have chosen it because they understood: their value is in the decision, not in maintaining infrastructure.
Run your business, not your infrastructure.
Every hour spent maintaining connectors, correcting data, and managing access rights is an hour taken away from strategy and decision‑making.
Deployment in weeks · 250+ connectors maintained · No Data team required