Data silos: hindrance or foundation? What Data-Driven companies really think

In the discourse on digital transformation, we often hear that data silos are enemies to be torn down. But in the reality of large organizations, they are neither an anomaly nor a defect to be corrected at all costs. Rather, they reflect the natural diversity of businesses, the complexity of information systems and regulatory requirements.

Why silos are inevitable and sometimes necessary

Each department builds its own vision of data according to its own objectives. Marketing focuses on interactions, sales on opportunities, finance on flows. So it's not surprising that the same customer is represented differently in a CRM, ERP or e-commerce database.

Some silos are also inherited from the company's history: critical business software, systems resulting from acquisitions, or separation imposed by compliance requirements such as RGPD or sector-specific regulations .

Removing these silos would be tantamount to denying operational reality. What's needed is to learn how to make them interact.

What holds companies back: lack of interconnection

The problem is not the existence of silos, but the inability to connect them when necessary. As AI, BI and data governance projects multiply, the need for cross-data explodes:

• To understand a customer's profitability, CRM data, orders, invoices and costs need to be reconciled.
• To drive a relevant AI model, data must be consolidated and cleansed.
• To make regulatory reporting more reliable, duplicates, discrepancies and structural errors need to be identified.

Without bridges, decisions are based on fragmented data. And governance becomes theoretical.

Reconciling data rather than destroying silos

This is where an effective strategy comes into play: reconciliation. This involves linking entities spread across different systems, without forcing them to adopt a single model. This work relies on several levers:

• powerful reconciliation algorithms (fuzzy, phonetic and pattern matching)
• continuous supervision of data quality (observability)
• remediation workflows designed in no-code, for use by business users
• data lineage to ensure traceability of each transformation

Rather than imposing a single vision, this approach respects the diversity of sources while guaranteeing a consolidated view.

Use case - Reconciling dispersed data prior to a strategic project

As part of a project to migrate to a new CRM solution, a company needed to make reliable a history of data spread over several systems: old CRM, ERP, business files, etc. Each source presented a different view of the customer, with format discrepancies, duplicates, missing fields and inconsistent data.

The aim was not to eliminate these silos, but to make them interact intelligently to prepare a unified database.
An automated audit detected anomalies at various levels (cell, row, column). Fuzzy matching rules were used to identify duplicates between databases, then business rules were applied to homogenize formats, enrich certain values and make the whole system more reliable.

Thanks to continuous supervision, the business teams and the IT Department were able to monitor changes in quality, correct them as they went along, and significantly reduce the risks associated with the project.
The result: a secure migration, consolidated data, and improved collaboration between silos.

📄 Read the full use case: Data reliability before migration to a new tool

What this means in concrete terms

A cross-silo reconciliation strategy offers tangible benefits:

• Businesses keep their tools and autonomy.
• IA and BI projects gain access to consolidated data.
• Governance becomes operational, traceable and measurable.
• Silos, far from being obstacles, become controlled components of an orchestrated system.

Tale of Data: govern your silos, without destroying them

Tale of Data enables companies to intelligently connect their existing systems, monitor data quality in each silo, correct identified errors via simple, visual rules, and document every action in a governance logic.

It's another way of making data a strategic lever: not by rebuilding everything, but by reconciling better.

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