The best of data governance: What makes good data governance?

This article is part of The best of data governance.

Common terminology
Without a common language, data cannot communicate successfully across systems and people can’t be optimized to improve organizational processes. When dealing with multiple, disparate applications — regardless of the number of software vendors or products involved — terminology matters. It’s meant to link processes and applications, but that linkage is not always easy to accomplish. Business terms referring to accounts, products, customers, employees, geography and other domains may vary across the transactional or BI applications. Additionally, some systems may have a finer level of granularity, adding more complexity to building clean analytics.

Companies need a common language for systems to adopt in order to relate. As information is shared across enterprise performance management (EPM) and transactional ERP systems, this common language — defined by agreed-upon hierarchies and attributes — creates business context for how a company performs analytics. It is acceptable to have different terminology for various applications, but the differences must be linked through hierarchies or mappings to provide effective business analytics.

Integrated applications
ERP, EPM, BI applications and a customer-specific data warehouse all serve different business functions. But as part of the integrated technology platform, you need to establish governance and define the hierarchies that will be used across different applications in order for them to work together. For example, total revenue by month for external reporting should relate balance to total revenue by location and product for that same month. Furthermore, alternate hierarchies for data should be shared with the appropriate applications that require the analysis.

Oracle functions
In the Oracle ecosystem, Oracle DRM can serve as the host and origination of the chart of accounts for ERP systems. However, this is not an absolute mandate; the transactional system can be the originator. It does not always matter which system hosts the chart or the employee or the sales structure, as long as there is a means to manage alternate hierarchies of the business for analytics, and that the appropriate system attributes are captured for each application and process to effectively communicate. Additionally, there should be a single point of entry, with controls over the entry process, and automation between transactional systems and the data governance solution.

The use of master hierarchies in a data governance solution can reduce maintenance and improve analytics for disparate EPM and BI applications. Specifically for Oracle Hyperion-based applications that predominately report financial trial balance information, a data governance solution can be used as the shared library to store all business dimensions for the vendor, customer, product and chart of accounts. The Hyperion-specific dimension repository manager provides the application-specific libraries, using the same hierarchies for summary trial balance and journal detail across multiple EPM and BI applications, or other databases can be deployed.

An important concept to note: For prebuilt BI applications where the data warehouse is provided out of the box, there are account groupings created for balance sheet and income statement reporting. Grant Thornton uses an accelerator with DRM to reuse financial account groupings so that hierarchies are managed once in DRM for use across multiple EPM and BI applications. This enables varying applications to have the same structures of data even if they go to a finer level of detail. This approach provides for increased transparency, reduced data reconciliation effort and a high level of auditable data.

Explore your options
Consider your business structure and complexity, the systems and processes already in place, and your reporting needs. One approach might be to source all hierarchies and dimensions from a data governance solution that pushes to transactions, BI, EPM, etc. Another option is to have ERP host new accounts or products, then determine and load net changes into DRM. DRM augments the hierarchies, adds hierarchies, provides attributes, and then shares information with downstream applications (data warehouse, EPM, BI).
Data governance integrates applications
Most companies desire a single version of truth and try to achieve that objective through deploying function-specific applications that provide a certain level of content that expires with the passing of time. Other companies have built data magnets or data warehouses to store large volumes of detail and summary data. Grant Thornton recommends a paradigm shift to consider the use of data governance processes and technologies as the mechanism to obtain a single version of truth for multiple stakeholders.

While you can buy the latest and greatest software, without a data governance solution or a light version of governance, the applications won’t connect to each other. After a labor-intensive, manual process to synchronize the data — a short-term solution — you can have the truth for only a point in time. You need the right integrated applications, methodology and governance process to control the data and reduce risk.

Mapping relates to differences in names (cost center, department, accounts, product, etc.) between applications, which occurs for a number of reasons:
  • Learn more about mapping in Finding common ground for private equity dataConverting or upgrading from one ERP to another
  • Redefining a new corporate chart of accounts
  • Acquiring a new company
  • A subsidiary entity is not part of the standard ERP
  • Applications are developed for business unit driver specifications

In many cases, the mapping or scrubbing of names is bundled under a transformation process called extract, transform and load (ETL) procedures, which is owned by IT. However, the names and organization of the new members are known best by finance, sales management, product management or other functional areas of the business. Organizations have a real opportunity to enable the business to define the terminology and structure that supports business analytics, which can then be shared with IT systems. This opportunity is particularly important for audit and government regulations in providing controls on changing certain types of data.  

Oracle functions
Oracle provides a data load and data transformation mechanism that is typically used by finance administrators called Oracle Financial Data Quality Management (FDQM). This application typically manages the data load and, if necessary, the data mapping of multiple general ledger accounts and related systems. The benefit of the FDQM application is that finance departments have a flexible means to load and map data for external financial reporting purposes. However, this mapping is purpose-specific and would need to be recreated for other uses. Other business applications would need to set up processes to remap the data. Oracle provides a robust ETL tool called Oracle Data Integrator for financial and nonfinancial mapping and transformation of data across the enterprise. In this case, the IT professional has to perform the definition and code updates for the remapping.

Regardless of the technology used, the data relationship and mapping of business dimensions can and should be defined by functional business teams, with controls in place for executive approval, while providing the logic to IT to automate. To avoid the remapping process, Grant Thornton recommends using DRM to define the business logic and provide the mapping definition and controls, which are then automated for use by FDQM, Oracle Data Integrator, or any transformation technology.
Example of mapping
Data governance should distinguish between and provide specific processes for both (a) dimension or chart of accounts values, and (b) hierarchies. The values have specific characters that are required for use within a customer relationship or ERP transactional system, where hierarchies provide the information for reporting. Hierarchies give you the context to understand data — big or small. Businesses have many applications that serve different purposes: planning and budgeting, workforce planning, capital asset planning, sales force analytics, HR retention analytics, general ledger, accounts payable analytics and inventory reporting. These applications may have different hierarchies and levels of detail for good business reasons and application-specific best practices.

Below is an example of a product hierarchy where the corporate application shows the level for analysis and Lidoderm, and the transactional system shows the stock keeping units and detailed values that map to the parent product.
Product hierarchy
Change management and workflow
The core of data governance is change management through human workflow. The workflow aspect of governance allows requestors to participate directly with the change management process. Without automated workflow, end users typically communicate to data stewards via email or phone calls or Excel templates. Automated workflow as part of governance formalizes the change request process, allowing the rules and flows to be codified and reused.

However, governance is not just workflow; it also includes the concept of the RACI model to help identify roles and responsibilities during a change process.

RACI is an abbreviation for:
R = Responsible: the person or group that owns the data
A = Accountable: the person or group that must approve the requested change
C = Consulted: the person or group that has information that is necessary to complete the data
I = Informed: the person or group that should be informed about the data change

The figure below illustrates the workflow stages that Oracle supports in the RACI model (the “I” in RACI is supported via notifications at each stage).
Workflow stages - RACI
Oracle DRM provides a module to configure business-driven workflow so that the right business audience has the information at the right time to approve, while providing the required IT controls to pass strict audit and regulatory requirements.

Back to the main page: The best of data governance
Read more: Data governance in action