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Datenschaftler

Use case · solution pattern, not a customer claim

Data reconciliation for month-end close and controlling

Controlling and accounting export data to spreadsheets. Cost centres, entities, or property identifiers do not match, and differences often become visible only shortly before close.

Data landscape

These systems and data need to come together.

The first step is not a new platform for its own sake. It creates traceable access to the sources that shape the process today.

DATEV accounting, SAP FI or CO, Microsoft Dynamics 365 Finance, LucaNet, Diamant, Jedox, Power BI, Excel, bank statements, and specialist source systems.

First project scope

Small enough to test. Relevant enough to decide.

The starting point focuses on one entity, one account, or one reconciliation type. Deterministic rules identify differences, while classification or explanation suggestions remain subject to professional approval.

Frame for a first project
  1. 01

    Constrain

    Constrain the project to one entity, one account, or one recurring reconciliation type.

  2. 02

    Connect

    Bring posting data, reference identifiers, and comments together traceably.

  3. 03

    Review

    Have controlling or accounting approve rules and difference suggestions.

  4. 04

    Observe impact

    Observe reconciliation effort, unresolved differences, and correction postings.

The honest objection

A black box cannot make decisions during close.

The dependable response

The solution does not post autonomously. It reproduces professionally defined rules, logs data lineage, and prioritises differences for approval.

Conditions

What must be true before technology can create value.

A sound starting point needs clear data, accountability, and boundaries. Missing conditions are made visible rather than hidden.

  1. 01The selected reconciliation type has explainable business rules.
  2. 02Sources and identifiers can be traced unambiguously.
  3. 03The final decision remains with the close owners.

When personal data is processed, requirements include purpose limitation, data minimisation, access protection, and deletion rules. Works councils and data protection stakeholders should be involved early where the specific use requires it.

Portrait of Umar Qureshi

Personal accountability

10+ years of practice across data and AI.

Umar Qureshi, Data & AI Expert

The technical background spans data engineering and platform architecture through enterprise search and responsibly integrated AI systems. The assessment remains grounded in your process and system landscape.

Meet Umar and the team

Free initial assessment

Which recurring reconciliation consumes the most attention before close?

We assess data sources, rules, and one traceable first reconciliation type.

The initial consultation and joint use-case discovery are free and non-binding.