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CFO Analytics: What Is It and Why Should You Care?

CFO Analytics: What Is It and Why Should You Care?
What is CFO Analytics?  CFO Analytics is all about leveraging advanced analytical tools and techniques to drive new, forward-looking insights and enable data driven views of the business. This definition is supported by research performed by leading organizations like Accenture, which noted in their “Unlocking Value with Analytics” paper; “This is the central challenge to the modern CFO—developing the data and insights that is focused on where the organization should go, rather than where it has been.” 

When people traditionally consider finance and accounting, they tend to think of general ledgers, P&L’s, annual reports, 10-K’s, regulatory and statutory reporting, budgets, forecasting and other forms of descriptive analysis. These reports provide snapshots of historical or lookback views of the enterprise and are used to help management understand the current state of the business. 

However, the user requirements are changing. According to McKinsey, 41% of the finance department’s output is tied to non-finance related analytics. In addition, the Harvard Business Review’s white paper, “Advanced Analytics and the CFO,” states, “finance’s ability to continue to increase its value contribution [to the enterprise] will depend on its ability to make use of advanced analytics.”  

So, how do you teach an old dog new tricks? Start by shifting the paradigm of what is delivered from finance from a reporter of the past to delivering strategic insights through analytics. These analytical opportunities can be broken down into three streams – descriptive, predictive and prescriptive analytics.
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Descriptive Analytics looks at what happened. Many finance organizations are stuck in this stage due to process and technical inefficiencies in data acquisition and data quality.    

Multiple ledgers, multiple COAs, inability to link detailed sub-ledger and transactional data back to the summary level ledgers and P&Ls present challenges for finance analytical users. The result is, according to Teradata customer research, more than 80% of allocated time is spent on data acquisition and data quality, and only 20% is spent on true analytics. In addition, much of this data is off-loaded into Excel, making it difficult to socialize with downstream users, models and applications. 

To solve this, a core financial foundational data model is required. This helps leverage in-database analytics based on a unified view of finance data, which is easily accessible to all users and analytic models. The source data and result set(s) are in the same location and provide a trusted source of financial information. This includes summary level and underlying detail, which is reconciled back to the source and provides the foundation for deeper analytics. In addition, the ability to persist historical information provides access to years of data that can be used to identify trends at detailed levels.

Descriptive analytics can include CFO KPI Dashboards, Spend Analytics (Direct and Indirect), Treasury, Order to Cash, Procure to Pay and several others. 

Predictive Analytics is the ability to leverage historical data and develop models that can forecast future results and enable data-driven insights. A strong foundational data layer provides the historical results to use as the basis for predictive models. This is useful for areas such as budgeting and forecasting, spending activity, revenue and working capital predictions, and provides a greater level of confidence in projecting future results.  Eventually, these predictive models will leverage machine learning tools to train models and improve the accuracy of results. 

Although the manual portion of some finance functions may never completely be replaced, the ability to provide the business users with a pre-populated forecast that is based on historical results and leverages external factors to better predict future results will improve accuracy.

Prescriptive Analytics provide recommendations or actions to take based on model insights. These suggestions are based on business rules that provide the next best action to take given a specific or desired result. This is commonly seen in customer service centers where the analyst has a list of actions to take based on the specific problem, customer, value and cost to resolve. In finance, similar models are built to solve common problems faced by accountants, auditors and business users. 

For example, prescriptive analytical models leverage real or near real time data to alert accounting analysts to take specific actions as unusual events occur. One use case focuses on indirect spend and identifies whether purchases are made that are “out of compliance” and then sends alerts as the transaction occurs (versus the end of the month when it may be too late fix the issue). Audit automation also leverages prescriptive analytics, sending alerts to auditors for transactions that fall outside of “normal” transactions. Other use cases that leverage prescriptive analytics identify trends or paths that lead to certain events (e.g. – potential fraud, excess spend, improved revenue and more). 

Why should you care?

The ability to leverage modeling, business intelligence and visualization tools which can seamlessly connect to the foundational data layer creates an analytics ecosystem that improves speed to value, enables the socialization of results and feeds downstream analytical models. CFO Analytics provide future driven insights, enabling data-driven and analytical decisions by the business users. This reduces the need to repeat projects and creates the opportunity to drive additional insights in the business. CFO Analytics transforms finance / accounting from a reporter of historical results to a strategic advisor who can predict future outcomes and proactively recommend the best path or action based on financial data.


A word of caution – don’t invest in analytics without an end goal in mind. Define the business problem(s), what is needed to resolve them and the business value improvement opportunity to help prioritize the analytic roadmap.
As mentioned, the key to success is establishing a core finance foundational layer. In the next CFO Analytics blog, we will take a deeper look at why a core finance foundational layer is a critical prerequisite to providing great CFO Analytics.
 
Portrait of David Rosal

(Author):
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.

View all posts by David Rosal

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