Build Your CFO Analytics Foundation

A core finance foundation, supported by the right data management tools, creates a trusted, auditable, and traceable source of all things financial. Read more.

David Rosal
David Rosal
31 août 2021 4 min de lecture
CFO Analytics and Building a Data Foundation

According to Accenture, “76% of CFOs agree that without ‘one version of the truth’ across business units, their organization will struggle to meet its objectives.”

Our research confirms this, showing that over 80% of a finance user’s time is spent on data acquisition and only 20% is spent on true analytics—mainly due to the lack of a trusted source for finance and accounting data.

Without a single source of truth for data—or a core finance foundational layer—finance teams struggle to provide real-time decision support to the business. That’s why creating this foundation isn’t a “maybe one day” aspiration—it’s a prerequisite for driving business value across the organization.

Finding the answer to the data problem

A digital financial ecosystem is crucial for solving many of the issues CFOs face today, including:
  • Data quality issues
  • Complexity and number of data sources
  • Repetition of similar projects due to data silos
  • Difficulty providing analytics on a timely basis
  • Error-prone and time-intensive manual processes
  • Lack of business-friendly tools for analytics, reporting, and visualization
  • Increasing complexity of regulatory and compliance requirements
To address these challenges, CFOs need to become information brokers and deploy a system that: 
  • Ensures all finance and accounting data is trusted, auditable, and traceable
  • Enables access to finance and accounting data across the entire enterprise
  • Provides the tools and level of detail needed to drive relevant, timely business decisions 
A core finance foundational layer does all that and more, providing team members and departments across the organization with the information and tools needed to analyze data from any angle.

Building ‘one source of truth’ with a core finance foundational layer

With a core finance foundational layer, users get access to answers that are timely, relevant, trusted, and can provide benefits to everyone across the business—including management, operations, marketing, customer experience, and human resources.

This equips potentially thousands of new users with data, reports, dashboards, and analytics—all provided by the CFO office.

A trusted core finance foundation layer also empowers leaders to provide new business value and digitally-driven analytics that focus on predictive and prescriptive insights—while continuing to provide traditional, historical, and descriptive analytics.

The four parts to a successful core finance foundational layer

The CFO Analytics Framework is the four-part key to building a successful core foundational layer, or data foundation. Each component is required for developing and sustaining this “single source of truth” for analytics.

1. Integrated financial insights

The secret to a strong data platform is bringing in all the data. By integrating general ledger and sub-ledger data, users across the organization get detailed insights on accounting and finance data. This requires master and reference data management, a finance-focused logical data model, and the ability to integrate across multiple ledgers and enterprise resource planning (ERP) platforms. The result is a single, trusted view—or “golden record”—of financial data.

This golden record enables accounting and financial analysts to deliver business outcome-driven analytics, including CFO key performance indicator (KPI) dashboards, procure to pay, order to cash, and indirect and direct spend.

Integrated insights also provide a greater level of detail—one that promotes drill-down and drill-up capabilities. Storing the results in-database makes the results available to a wider audience, including downstream models and applications.

2. Super ledger

Need a way to tie accounting issues back to their operational “source”? A super ledger makes that possible by integrating operational and transactional data into your financial data platform. This enables better traceability from source to report and opens new dimensions and insights into financial reconciliations, operational reporting, customer segmentation, products, and revenue assurance.

When combined with integrated financial insights, a super ledger provides the basis for detailed dimensional analytics and reporting.

3. Multi-dimensional profitability

Want to report on the exact costs to serve specific customers? That’s where multi-dimensional profitability (MDP) comes in.

Building MDP into your core foundational layer creates the ability to model and allocate financial components down to the lowest level of detail. MDP includes operating revenue (product and non-product revenues, rebates, reimbursements, etc.), direct and indirect expenses, unit costing, risk components, capital costs, and other profitability attributes, and typically leverages advanced financial modeling tools.

MDP enables analyses that provide new insights into profitability at any level: customer, product, location, channel, etc. The results also feed downstream models and applications like price optimization, customer segmentation, customer lifetime value, and product rationalization models.

4. Advanced finance modeling

Advanced finance modeling brings everything together, making it possible to leverage data from the integrated financial insights, super ledger, and MDP to create a finance-driven analytical ecosystem.

The data model is the key driver in this process, integrating data from multiple platforms into the golden record. It also plays a crucial role in integrating transactional and other non-financial data that might be required for more sophisticated analytics.

From silos to driving business value with data

The CFO Analytics Framework creates a data foundation that goes beyond descriptive analytics. It enables the use of advanced predictive and prescriptive modeling tools, and artificial intelligence and machine learning models that provide more value-added analyses—especially in audit automation, compliance, and financial forecasting.

Just like any other construction project, a solid foundation is needed before building an analytical “house.” The core finance foundation, supported by the right tools—data models, master data management (MDM), reference data management (RDM), modelling tools, and more—creates a trusted, auditable, and traceable source of all things financial. With these tools, finance teams can provide more strategic and accurate insights to the business, faster.


À propos de David Rosal

David Rosal is a Senior Industry Consultant for Teradata, focusing on helping customers through financial transformations including numerous Fortune 100 companies in Financial Services, Retail, Hospitality, Travel & Transportation and Manufacturing. He provides thought leadership on how to leverage integrated financial and non-financial data to drive innovative insights to improve performance and profitability through the use of data and analytics.

David has more than 35 years of experience in the finance and management accounting space including technology, food service, retail and banking. He possesses a unique mix of finance, operational and technical skills across multiple industries and excels in the development of strategies and solutions that improve profitability and performance through the power of data. He has a BS in Accounting, MBA in Finance and is a registered CPA in the State of Illinois.

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