Enterprise Dimension Management

Almost all enterprise systems provide individual functionality to create and update the dimensions that they consume - dimensions that classify and categorize operational, transactional and analytical systems. The trouble is, these tools are not designed to align dimensions across a disparate systems environment, and in most cases changes can only be conducted by seasoned IT veterans. Without this alignment, analysis and reporting cannot be reconciled with confidence across the enterprise.

Oracle Hyperion Data Relationship Management provides a change management platform built specifically to support a complex data environment. With Oracle Hyperion Data Relationship Management, organizations can easily enforce high-level standards and controls, seamlessly support departmental and business unit flexibility, and, through careful alignment, deliver greater agility and financial reliability.



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 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.


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 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.


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


Complex organizations with the goal of dimensional excellence have found success in managing their dimensional information across a wide spectrum. Some organizations have implemented scattered management in home-grown applications siloed across the organization; others have achieved governed models where changes are orchestrated through the spectrum of business systems consuming the data.


Maintenance process:
Scattered, local processes for each instance of each application, usually conducted by IT for each Silo application.

Data governance & control:
Not possible, no consistent workflows

Dimension mis-alignments surface as reporting inconsistencies

  • Fear of regulatory non-compliance
  • Duplicated effort
  • Frequent reporting reconciliation issues
  • Responsibility for changes unclear
  • Ad hoc processes to support mergers


Maintenance Process:
Ad hoc spread-sheets, home grown applications, and MS Access DBs for specific domains. Maintenance involves business users, but IT needed to integrate changes.

Data Governance & Control:
Limited governance for some subject areas, inconsistent workflow for each solution

Some point-to-point integration of dimensions using ‘black box’ ETL

  • Duplicated effort, but business experts involved
  • Reporting inconsistencies & reconciliation issues
  • Simple audit trails possible


Maintenance Process:
Central repositories force synchronization for groups of systems, maintained by Technical administrators (business or IT)

Data Governance & Control:
Centralized control enforced by tools Multi-step approval processes

Complex point-to-point integrations to align dimensions

  • duplicate maintenance (eg. between EPM & ERP systems)
  • Local business rules enforced
  • Compliance enforced by complex processes


Maintenance Process:
Central repositories recognize varied needs of participating systems, maintained by groups in central functions (eg. Corporate Finance)

Data Governance & Control:
Central functions apply governance, complex workflows to ensure data quality

Central integration 'hub' using 'open' ETL processes

  • Centralized & complex workflows lengthen close cycles
  • Automated integration with major enterprise systems
  • Rapid assimilation of acquisitions


Maintenance Process:
Unified, controlled environment for all changes, maintained by Local business experts ('data stewards')

Data Governance & Control:
Governance delegated to business experts, simplified request & approval mechanism

Single 'system of entry' ensures alignment

  • Enterprise-wide alignment
  • Intrinsic compliance
  • Fully devolved governance
  • Full audit trails
  • Business rules enforced automatically


Oracle Hyperion Data Relationship Management is a unique dimension management solution built to enable financial and analytical reference data management in dynamic, fast changing business environments.

With Oracle Hyperion Data Relationship Management, organizations can:

  • Streamline reference data changes among complex hierarchical structures across enterprise systems
  • Engage business users as direct contributors to dimension management
  • Codify the knowledge that resides in experts into automated, repeatable business rules
  • Enable data governance and compliance across the enterprise by enforcing standards and controls

As a result, Oracle Hyperion Data Relationship Management enables organizations to build enterprise-wide consistency in terms and definitions across systems and stakeholders; generate trustworthy insights based on high quality reference data; dramatically reduce financial reconciliation times; streamline data maintenance; comply with governance rules and regulations.


Discover the Impact of Integration

Business challenges:


To reconcile corporate information assets across IT systems from acquired companies into the larger enterprise and create an on-ramp for swiftly integrating acquired information assets.

Key Oracle Hyperion Data Relationship Management capabilities:

  • Compare, analyze and blend structures in a unified environment
  • Manage the hierarchy and system consolidation process
  • Test the impact of future reorganizations with “what if” scenarios

Deployment scenarios:


Large companies that are acquisitive or plan to reinvent themselves through restructuring, divestitures or spin-offs.


Global and local alignment

Business challenges:


To align financial reference information (e.g., accounts, cost centers, legal entities, etc.) across transactional and analytical applications to enable effective financial and management reporting, simplify reconciliation and restore trust across a broad scope of enterprise systems.

Key Oracle Hyperion Data Relationship Management capabilities:

  • Enable reporting rollup and mapping to local and global levels
  • Manage the Corporate Chart of Account along with the Decentralized Charts of Accounts
  • Compare Charts through time to report and analyze differences
  • Map local or legacy ledgers to corporate standards

Deployment scenarios:


Companies who are looking to reconcile business views for financial and management reporting across ERP Systems/Business Analytics Solutions, and Financial Data Warehouses.


Migration and co-existence

Business challenges:


Align financial reference information (e.g. accounts, cost centers, legal entities, etc.) across multiple ledger instances (e.g. legacy and modern ledgers, old and new versions of ledger systems).

Key Oracle Hyperion Data Relationship Management capabilities:

  • Align parallel General Ledger environments; redesign Charts of Accounts; map General Ledger segments to other reporting segments
  • Discover and socialize the impact of changes without impacting production systems
  • Streamline and facilitate governance activities using a complete audit log of all changes that have occurred

Deployment scenarios:


Companies who are either looking to buy or have recently purchased a new ERP/General Ledger solution, are upgrading/migrating a legacy version for a modern ledger, or wish to co-exist multiple ledger instances to extract continued value.


Manage any hierarchical data within the organization

Business challenges:


Inconsistent measures and results across operational and analytical environments; lack of flexibility and agility across reporting and analysis environments.

Key Oracle Hyperion Data Relationship Management capabilities:

  • Engage stakeholder input through approvals and workflow, version control, and granular level security
  • Synchronize across business systems
  • Maintain complete history of changes made, and roll back changes if necessary

Deployment scenarios:


Large and complex organizations who need an enterprise effort, not just departmental, to manage financial reference data across their extended operations.


Oracle Hyperion Data Relationship Management was foundational for our Business Intelligence and Data Governance initiatives at NetApp, enabling business users to be direct contributors of reference data changes. Business has the power to change reference data with no IT intervention required.

Dongyan Wang, Senior Director, Enterprise BI and Data Management, NetApp


This was a really big opportunity for us because we have a number of Essbase cubes that are used for management reporting, and keeping all the metadata and master data aligned is certainly a challenge without a tool such as Oracle Hyperion Data Relationship Management.

Peter Debartolo, Senior Director, Decision Support Technology, Pfizer