How CCH Tagetik Defines and Scopes the Modern Financial Close

CFO responsibilities are expanding. Finance leaders are now expected to coordinate planning, risk, and capital allocation while incorporating AI and navigating regulatory pressure. Those expectations are rising faster than the systems that support them.

Most finance environments still rely on fragmented data, batch processes, and manual reconciliation. That gap between expectation and execution shows up most clearly in financial close. What should provide a consistent view of enterprise performance is often constrained by how data is collected, aligned, and validated across entities.

CCH Tagetik’s Financial Close & Consolidation software is designed to address these constraints by integrating data, workflows, and reporting into a single system.

The Technical Constraints Behind Financial Consolidation

Financial consolidation depends on consistency. Data must be aligned before it can be combined into a single view. That process becomes more complex as organizations operate across currencies, accounting standards, and reporting structures.

Most delays come from preparing data, including reconciliation, intercompany matching, and validation across systems. These steps are often manual or supported by disconnected tools, which introduces latency and inconsistency.

Reconciliation is the primary constraint. Finance teams must ensure transactions match, balances align, and discrepancies are resolved. In fragmented environments, this requires comparing multiple versions of data across systems.

The same data must also support different timelines. Statutory reporting follows fixed cycles, while management reporting requires faster, iterative views.

As a result, consolidation performance depends heavily on how reliably data is aligned and validated across entities, systems, and reporting requirements.

CCH Tagetik and the Modern Financial Close

CCH Tagetik’s Financial Close & Consolidation software approaches consolidation as a data and workflow problem. The platform is designed to operate on a unified data model, where financial and related data can be collected once and reused across consolidation, reporting, and adjacent processes. This reduces duplication and limits the need to reconcile separate data sets at each stage.

The system organizes consolidation through structured workflows. Tasks such as intercompany matching, validation, and consolidation adjustments are managed within defined processes, supported by audit trails and approval steps. This introduces control over how data moves from collection through consolidation to reporting.

Automation is focused on preparation and validation. AI is applied to transaction matching, anomaly detection, and data alignment tasks. These functions address the points in the process where delays and inconsistencies are most likely to occur.

The platform also extends consolidation into related activities. Reconciliation, reporting, and disclosure are handled within the same environment, allowing financial and non-financial data to be managed together. This reflects a broader shift in how consolidation supports both regulatory reporting and internal analysis.

Evidence from Swissbit and Trevi Implementations

In practice, improvements in consolidation come from automation and standardization, but outcomes depend on how these changes are implemented.

At Swissbit, consolidation and planning processes had relied heavily on spreadsheets and manual data handling. The company implemented CCH Tagetik to automate data integration, replacing manual file uploads and aligning data across systems.

Consolidation logic was standardized across 10 entities, and workflows were structured to support validation and reporting. These changes resulted in a roughly 25% faster monthly close, along with more consistent and timely group financials.

Trevi Finanziaria Industriale faced a different set of constraints, centered on heterogeneous data and limited process structure. The company implemented CCH Tagetik to introduce ETL-based data integration and workflow-driven processes that improved traceability and coordination across teams.

Consolidation, reporting, ESG, and disclosure were managed within a single environment, which improved data quality and reduced manual input. The result was a significant reduction in closing times and a more structured, controlled consolidation process.

Across both cases, the improvements came from automating data integration, standardizing processes, and introducing workflow controls that reduced manual intervention and improved data consistency.

What This Means for SAPinsiders

• Consolidation is becoming a coordination system. Financial consolidation is increasingly defined by how teams coordinate data, not just how systems calculate results. The ability to align workflows, responsibilities, and validation processes across functions now shapes consolidation performance as much as underlying technology.
• Close modernization requires upstream discipline. Modernizing financial close creates an opportunity to standardize data structures and processes earlier in the cycle. Organizations that invest in integration, governance, and ownership models alongside automation are better positioned to sustain improvements in speed and accuracy.
• AI in consolidation is applied at the data layer. AI in consolidation is being applied to matching, validation, and anomaly detection rather than decision-making. These use cases align directly with reconciliation bottlenecks, making them practical entry points for improving close performance without introducing significant risk.