IBM Intelligent Mapping for Finance: The proactive approach to finance transformation

Imagine driving a car along a highway full of nails and glass, stopping each time to get out and remove the debris. The time it takes to reactively address all these obstacles is a painstaking and ineffective approach to travel. But unfortunately, this approach too often describes system implementations.

As much as teams can try to plan for roadblocks, the complexity of enterprise resource planning (ERP) systems means there will always be unpredictable functional and technical issues that arise during build and test. If there were a feasible way to survey the roadway in advance of your journey to proactively plan your route and remove obstacles ahead of time, this would help immensely.

Introducing IBM® Intelligent Mapping for Finance (IMF): IMF clarifies the path to standard by helping implementation teams predict and clear potential roadblocks as early as the discovery phases. In this blog post, we’ll explain how IMF can help teams shift toward the proactive approach to finance transformation, with a focus on the chart of accounts (CoA).

TruQua and IBM recently partnered with a leading automotive company on their SAP S/4HANA finance transformation with Central Finance, leveraging the IBM Intelligent Mapping for Finance solution throughout the course of the engagement. By using IMF, this client was able to improve the speed and efficiency of their implementation, which allowed the project team to focus on their design and process strategy from the beginning rather than spending days, weeks or even months on error resolution and fixing mapping errors.

IMF contributed to a significant reduction in general ledger mapping and master data errors in the initial load process. In previous comparable projects, the master data related errors accounted for 25% of all errors, while with IMF, the errors have been reduced to account for only 0.05% of the total errors.

Functional benefits

As part of SAP S/4HANA Central Finance projects, finance teams must move to one central CoA, often from a disparate source landscape with multiple operating CoAs. By conducting analysis on the charts of accounts and other master data during the pre-implementation phases, teams can eliminate uncertainties and gain an edge on accurate scoping and timeline for the implementation phases.

During the implementation phases, teams can visualize mappings and ensure they are valid to design an effective target chart more easily. IMF gives teams greater flexibility to iterate on changes without introducing unknown issues as well as experiment with multiple versions of the CoA to standardize more easily. Benefits of standardization include:

• Elimination of account proliferations by leveraging S/4 dimensionality (function, region, department and others)
• Use of smart numbering and naming conventions
• Limiting the number of manually posted accounts

Technical benefits

During SAP S/4HANA Central Finance projects, the initial load and replication processes must consider the technical constraints surrounding attribute consistency in general ledger account master data between source and target systems. The key attributes (for example, Open Item Management indicators, Balance in Local Currency Only indicator, and Reconciliation Account Type) have technical restrictions in addition to implications for an organization’s business processes that may be in scope for an S/4HANA transformation.

The activity of aligning or “harmonizing” the key technical attributes is often a tedious, manual and reactive process that can take upwards of weeks or months over the course of the initial load of transactional data from source ERP systems to Central Finance. The traditional approach to this process involved analyzing errors resulting from attribute conflicts in general ledger master data during the historical transaction data load or ongoing replication to Central Finance, manually applying corrections within the source or target system and tracking in offline spreadsheets.

With IBM Intelligent Mapping for Finance (IMF), this process gains a great degree of automation, shifting from a reactive to a proactive approach to error resolution that can be carried out before the project team ever logs into the organization’s systems. By shifting the mapping exercise and future state CoA design to this earlier phase in the project, implementation teams can see an enormous time savings with respect to this once heavily manually process. By centralizing the mapping and CoA design within the tool, IMF also becomes a living repository for finance master data throughout the project and the application lifecycle.

Preparation

The first step to using IMF is to collect and prepare the master data necessary to execute a project in the tool.  Each IMF project requires legacy source system data from any number of source systems as well as a reference or prototype of the future state CoA master data. This reference future state CoA can be based on a merge of the legacy CoA, a best practice reference CoA, an existing operational CoA, or some combination of the three. The master data can be ingested into the tool through flat file uploads of table extracts or through remote pulls of the data by way of integration with the systems.

Map company codes

After the reference master data has been loaded into IMF, the next step is to map the legal entities or company codes in scope for the implementation. This can be done by configuring automated attribute-based mapping rules or through a flat-file upload if the mappings are known. After the company code mappings are finalized, the G/L mapping and target CoA refinement process can begin.

Map G/L accounts: Create and refine CoA

There are technical constraints and business process considerations when designing the future state CoA for a Central Finance or S/4HANA implementation. Addressing these constraints proactively and with a great degree of automation is where the solution really differentiates itself with its intelligent G/L mapping capabilities, saving time and effort for the project team and ensuring confident chart design.

One or more attribute-based mapping rules can be configured by using an intuitive UI on any G/L master data characteristic, including account numbers, text descriptions or a common group/consolidations account. These rules can be relatively simple or incorporate considerable complexity by leveraging natural language processing, exact text matching, ‘starts with/ends with’ criteria, and more.  The system will then automatically map the accounts or suggest proposed mappings for the user to review, reject, or approve using IMF’s workflow capabilities. If the result is not as expected, the mapping rules can be easily reconfigured and rerun.

While the mapping capabilities of IMF are powerful on their own, the built-in logic to harmonize these mappings by detecting and addressing technical conflicts is even more impressive. Leveraging the implementation experience of the TruQua and IBM project teams, as well as SAP’s guidance on master data harmonization, the system checks mappings as they are created for any invalid attribute combinations between the source and target systems. For instance, non-reconciliation accounts cannot map to reconciliation accounts. At any point during the mapping exercise, these conflicts can be viewed and iteratively addressed either by remapping, aligning the target attribute value from source when possible, or by automatically generating new ”replication” accounts.

So-called “replication accounts” are accounts that are created to resolve inconsistencies between other mapped accounts, particularly where remapping or attribute changes are not possible. This approach is useful in case the relevant accounts or company codes are mapped N:1, because switching the target setting would just flip the conflict to other source objects. The creation of a replication account in the target system’s CoA resolves this issue by moving the mapping in conflict to a new account with harmonized settings.

Once complete, the user can export both the master data and mappings in the template structure required by the S/4HANA Data Migration Cockpit and the Central Finance mapping upload framework. This template export functionality provides a substantial time savings in manual and offline data preparation for these load processes.

In addition to the product-mapping capabilities around general ledger master data, similar functionality is being developed for other financial master data objects and processes such as:

• Cost Center and Profit Center
• Vendor, Customer, Business Partner
• Multidimensional mapping

IBM Intelligent Mapping for Finance helps implementation teams work together to reach a standard and common definition for their target CoA when implementing SAP S/4HANA Central Finance, especially with a disparate source landscape. IMF features robust mapping and data harmonization capabilities that help teams predict and clear roadblocks as early as the discovery phases.