How to redesign finance data with a common information model

A blog post by Beth Kaplan, managing director, Deloitte & Touche LLP

The core of controllership, and an entire finance organization, is finance data. Chart of accounts, finance data, managerial data, and operating data, and the structure around that data is the foundation for an organization's desired capabilities as part of its finance journey. This finance data, referred to here as the common information model (CIM), supports an organization's regulatory, transactional, management, tax, and financial reporting requirements if the structure of the model is well-designed and organized.

Establishing a well-structured CIM is an integral step in achieving finance transformation outcomes by providing a single source of financial truth in real-time with sufficient granularity. This model enables more accuracy, timeliness, predictability, transparency, and flexibility in financial data, all of which are the main drivers of quality in financial information— a core consideration of a new CIM model. Given the rapid transformation in finance, it is also essential to develop a model for sustainability into future technologies, such as in-memory and cloud computing, to increase the potential for financial data to drive more value across the enterprise.

Developing a better finance data model is possible, but organizations often encounter challenges when transforming their finance data model to align with the current and transformative finance environment. What are these challenges? What does a well-designed finance data model look like? And how can companies design a better CIM that sustains finance data now and in the future?

To answer these questions, we need to first define and understand the components of a well-designed finance data model. From there, we can identify common challenges in the current model and design a new CIM that can address these challenges; improve efficiency; enhance insights; and offer sound governance, compliance, accuracy, and controls.

Define and understand the importance of a well-designed finance data model

A CIM can be defined as the data elements required to plan, record, report, and measure performance consistently across the enterprise. In scope, it covers a broad range of systems such as transactional systems, enterprise resource planning (ERP), and analytical systems—and it ties that together through a common set of business definitions, hierarchies, and relationships.

Financial information quality is a core component of a well-designed data model

The question is, what defines the quality of information? There are some considerations and potential starting definitions of attributes that can help define what quality data and information may look like in a well-designed CIM.

Accuracy: data represents a true picture of performance in accordance with GAAP and information is consistently classified and presented across the organization

Timeliness: internal requests are met with a quick turnaround and there is a rapid response time to external filings after each period ends.

Predictability: performance aligns with stated expectations, while continuous forecasting allows for updated expectations when there are changes with key drivers.

Transparency: information is simplified and standardized, making it easy to understand across the enterprise and giving management a direct line of sight to business drivers and their impact.

Flexibility: underlying information that can be easily organized in different ways to meet requests and also be reorganized based on business changes and transformation across the enterprise.

Common challenges organizations may face with the structure of their data model

Organizations often face several recurring issues caused by a CIM design that fails to meet the financial and nonfinancial stakeholders' needs. Some of the common challenges include a CIM that is out-of-date and no longer relevant due to acquisitions and changes to business operations; multiple technology platforms with inconsistent data definitions and rules; and manual and time-consuming processes that are designed based on past system constraints, and often fail to accommodate a myriad of new data sources, including data from information maintained outside of source systems.

Governance issues also offer many challenges to data models, including a lack of data governance structure, a failure to understand data impacts across the organization, and reconciliation issues between finance-controlled data and reporting and operational reporting outside the jurisdiction of finance. This often leads to new Chief Data Officer roles in finance and accounting—one of the solutions to data governance emerging from transformation.

	Finance transformation vision • Enhanced business insights • Increased efficiency and automation • Stronger governance, compliance, and controls
	The vision is enabled by • Cognitive computing • Cloud and robotic process automation (RPA) • In-memory computing

How to plan and think through the redesign of your data model

The first step when thinking through a redesign of your CIM involves getting to the core of what an organization needs to enable the finance vision, including requirements to sustain that vision in the future.
This step in the new CIM journey will require addressing some specific questions across the organization.

Answering tough questions such as:

• How do we want to look at the business, generate insights, and increase the quality of reporting around decisions?
• What is the level of data required to support our desired capabilities and finance vision?
• How do we develop in a way to facilitate sustainability into an increasingly unpredictable future?
• How will we drive and enforce common data standards across the organization? 
• What will be our ERP optimization strategy? 
• Who are the sets of stakeholders we need to consider as part of the design process?

A path forward as you construct your new common information model

Key considerations for developing a new sustainable data model

Some considerations and takeaways for developing a sustainable CIM include:

Build for the future: Protect your CIM redesign from old ways of thinking. Consider how your system landscape will change over time, particularly from mergers and acquisitions or reorganizations.

Keep the CIM clean: Determine that data elements have a single unique definition and purpose and avoid mixing multiple uses of data in a single field.

Meet reporting requirements: Design your CIM to support an organization's financial, management, local statutory, and tax reporting requirements.

Develop your workforce: Cultivate a workforce to manage the new model by establishing roles and responsibilities, organization structure, and clear ownership for data management practices.

Establish governance: Develop guidelines and principles for enforcing master data and management standards, which outline how data is created, modified, and maintained, and also includes processes and governance designed for business insights and outside decision making.