In most Swiss private banks, Core Banking Systems such as Avaloq, OLYMPIC, Temenos, Wize or Efficience are powerful engines for daily operations. They ensure transaction execution, regulatory compliance, and client servicing. But when it comes to steering the business, these systems fall short. The “raw” data they produce is not directly usable for decision-making.

This is not a technical issue — it is a structural one.

Core Banking data: complete, yet not usable for analytics

Private banks today possess more data than ever before. But operational data is rarely suitable for analytical needs. It is often:

  • Fragmented across modules and systems
  • Context-dependent with different interpretations per team
  • Difficult to reconcile across the organisation
  • Not designed for analytical or strategic use

As a result, instead of enabling decisions, data fuels internal debates and inconsistencies. Teams end up asking the same recurring question: “Which number is correct?”

Key performance indicators don’t mean the same thing for everyone

Across many private banks, even the most essential KPIs lack a unified definition:

  • AuM
  • NnM
  • LiA
  • Revenues
  • RoA
  • Margins
  • Costs
  • Retrocessions

Each department applies different rules, filters, exclusions and time horizons. Uncontrolled Excel extractions multiply discrepancies and create internal “data wars”, with multiple versions of the truth circulating at once. Without a proper analytical data model, misalignment becomes the norm.

The solution: an analytical layer integrated into the Core Banking System

To be truly useful for decision-making, an analytical layer must perform three essential functions:

1. Standardise definitions

A shared data dictionary covering KPIs, dimensions, business rules, exceptions and periods. This is the foundation of governance and comparability.

2. Ensure full traceability

Decision-makers must understand where each number comes from, which transformations were applied, and how calculations were performed. Without transparency, trust in the data collapses.

3. Deliver data continuously, not as “one-shot” extractions

Analytics must be connected, refreshed and integrated into operational and managerial workflows — not produced manually through Excel.

What we observed in a Swiss private bank

The primary outcome was not “one more dashboard”. The real impact was the end of internal debates over which version of the number to trust.

When data governance becomes solid, teams regain time, clarity and alignment — and the organisation finally focuses on performance instead of reconciliation.

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