THE PURSUIT OF DIMENSIONAL EXCELLENCE
Dimensional Excellence describes the goal of an enterprise to effectively and efficiently manage its enterprise dimensions as core assets across multiple systems. Since these dimensions describe how an organization chooses to classify and to analyze ‘value’, enterprises need to ensure that these dimensions are aligned, accurate, and leveraged to their fullest extent. Usually, this includes providing a single ‘system of entry’ or ‘system of record’ in which business experts are responsible for the maintenance of their own hierarchies.
Deploying an effective enterprise dimension management system is a challenging process with serious risks. Such a system must be scalable and flexible and should meet current and perpetually evolving future business requirements. Those future requirements should be assimilated through simple configuration, rather than requiring complex custom coding. The true total cost of ownership favours the adoption of a proven, configurable, best-of-breed tool for which the costs and implementation timescales are predictable, and through which risks are minimized.
THE HIGH COST OF RELIABILITY AND FINANCIAL CONTROL
The demands of specialized legal reporting and deep performance insight come with a price – one that places a high cost on reliable information and financial control. Problems emerge when different business functions or regions invest in separate system silos, metrics and measures that mean different things to different people. As a result, managers quickly begin questioning the integrity of the analyses provided to them from across the extended operation.
Maintaining this coalition of systems – or even migrating them to a single global standard – places a burden on the Finance and IT organizations to develop solutions that facilitate rationalization and maintenance of the enterprise chart of accounts, as well as management/financial reporting structures such as dimensions and hierarchies. However, standardization alone cannot be the solution. Flexibility and agility in business decision-making require technologies that provide IT control, are compliant with internal controls and risk management policies, yet still provide business users with a common language to express their unique business perspectives and gain visibility.
THE CRITICAL NATURE OF FINANCIAL REFERENCE DATA
Financial reference data – the financial and analytical definitions and their corresponding hierarchical structures that support financial reporting, Business Intelligence, and performance management – has distinctive characteristics:
- It is critical in ensuring accurate and consistent financial reporting at multiple levels of an organization
- It is used throughout the organization (not just the finance function) and is the foundation for enterprise-wide data governance and data management strategies
- It is an integral part of product management and profitability analysis
- It might need to support multiple global accounting standards (such as GAAP and IFRS) and compliance with new and emerging regulatory requirements
- It is often structured in complex, deep hierarchies, and in many cases alternate hierarchical structures are needed to support the differing reporting and regulatory requirements of the business
- Cross-dimensional mappings between different hierarchies (for example, between a product and an associated General Ledger account) are often leveraged to define important inter-relationships or to delineate valid transactions
- It is frequently used to support mergers, acquisitions and divestitures
THE CHALLENGE OF REFERENCE DATA MANAGEMENT
The process of maintaining financial reference data is different from the processes used to maintain other reference data such as customer or product information. It is critical that the financial reference data is 100% accurate and consistent - errors or inconsistencies can quickly lead to inaccurate financial statements that can have serious consequences for any large organization.
The quality of many types of reference data can be managed and improved with highly automated tools that might identify errors, omissions, and duplications (for example, the automatic de-duplication and address cleansing of a multi-million record customer database). By contrast, critical financial reference data is often carefully manipulated by business experts, who are aware of the major impact upon an enterprise’s financial statements that might be caused by a very minor change to a single item in a chart of accounts. In the financial realm, maintenance is very much a hands-on process.
Business rules enforce the accuracy of financial reference data changes. Some business rules are straightforward (for example, leaf-level and summary cost centres in a hierarchy must not share the same parent summary cost centre) but other rules may be much more complex, involving cross-dimensional mappings and validations. The solution for managing financial reference data must provide an easily configured framework for enforcing these specific financial accounting and reporting rules.
THE NEED FOR DATA GOVERNANCE
Data governance refers to the overall management of the availability, usability, integrity and security of the data employed across the organization. It is implemented to improve data quality through control processes that assign rights and responsibilities for the stewardship of these data resources.
In addition, data governance processes can be enhanced through software tools that enable collaborative management of shared data assets through assignment of responsibility for data maintenance to responsible parties – who enrich the data management process by adding both their business knowledge and their understanding of how the data sets are leveraged across the enterprise.
Technology is a key enabler, allowing these parties to be assigned specific responsibilities for the maintenance of a hierarchical data structure, providing capabilities that include:
- Automated business rules to validate data quality
- Detailed audit logs of all changes to the financial data
- Default values for members of data hierarchies to enhance data quality and simplify maintenance
- A versioning model for archiving hierarchies used in each reporting period to allow future comparative analysis
