David Farrell and David Geere, from Deloitte Analytics, continue their series on using analytics in the finance function.
There are many opportunities for the finance function to use analytics, which is the practice of using data to enhance business performance by making more effective decisions and actively incorporating insights gained from data into business processes: in simple terms, ‘same questions, better answers’.
This piece explores how analytics relates to the core responsibilities of a typical finance function.
Planning, budgeting and forecasting (PB&F)
PB&F is one of the most challenging activities finance undertakes, and it affects all parts of the business. Analytics projects in PB&F can deliver process efficiency savings and more accurate forecasts can lead to better business strategy and execution.
Example – driver based forecasting
Enhanced forecasts can improve decision making and give businesses an edge. Whether it’s targeting customers and growth, improving financial asset management or simply redirecting business strategy, knowing what’s going to happen next and planning the business response is critical.
The enhanced forecast framework (shown below) outlines the key components for improving forecasts. All these components must be in place to sustain improvements in forecasting – this is especially important as moving to a driver based planning model gives great benefits in decision making and scenario planning, but also places much greater demands on forecasting capability.
Financial reporting
Accurate, timely financial reporting is often taken as a given by the business. However, achieving this is not often straightforward.
Finance functions typically need to simplify, streamline and industrialise the reporting process in order to be able to move onto value adding analysis of the numbers.
One example of how to achieve this is leaner reporting, which entails delivering increased quality (accuracy of reports) with minimal waste. Value can usually be obtained without purchasing new technology.
Analysis could cover:
- aligning the general ledger data structures for every legal entity / business division, using the same cost centre and activity code hierarchy. Cost centre creation should be controlled to avoid proliferation, and once a cost centre is closed, not reopened
- minimising manual inputs. One way to start would be by challenging all non material manual apportionments – are they really required every month?
- building quality processes that perform every required activity once only. For example, group adjustments entries are often added to reports late in the month and then input into the general ledger the following month, which can be wasteful. There can be too many meetings in close-to-report processes. Meetings should only be held to make decisions (such as approving reported numbers).
Treasury
The Deloitte Q3 2011 CFO survey found that current market volatility is a key concern for CFOs and is placing businesses on a defensive footing. Market volatility can impact business margins, liquidity and balance sheets. It should therefore be a key concern for treasury departments.
Analytics in financial risk management
The treasury function can use analytics to enhance financial risk management to adapt to and exploit market volatility. This can be supported by specialist toolsets, or an extension of the driver based forecasting model discussed above, and possibly extended to assess the impacts of external market volatility on financial results.
Key areas to address include:
- Funding risks: sensitivity analysis of revenue to loan covenants breach should be a critical metric and highlight the most vulnerable time periods. Creditors can be assessed for their exposure to European debt and overall liquidity, to ensure that the treasury is focusing its relationships with a diversified portfolio of providers.
- Hedging: forecasting, management and monitoring of net exposure to foreign currencies, interest rate and commodities may require more frequent analysis and intervention.
- Cash flow: customer profitability analysis (CPA) can identify the most important customers and can be complemented by tracking the % concentration of revenue to the business relationship managers / sales force. It can be used to highlight key internal employees and avoid overconcentration of revenue in a few employees.
Business partnering
Business partnering is a broad area. The type of analytics opportunity in business partnering will be influenced by the business function that finance is partnering with:
Business partnering by function
| Partnering business function |
Example analytics opportunity |
| Sales / marketing |
Customer relationship analytics – augmenting traditional CRM and customer profitability analysis with additional analytics tools such as:
Sentiment analysis
Relationship manager
Performance management fees and fee waiver optimisation
Network analysis |
| Supply chain, manufacturing, infrastructure support, security |
Fraud and risk analytics – building predictive models to enhance fraud prevention and increase transaction throughput. |
| Human resources |
Workforce optimisation – a multi-faceted discipline that employs analytics techniques across:
Strategic capacity and capability planning
Resource demand forecasting
Performance management
Workforce scheduling |
Technology
Inevitably, an analytics project will require some use of technology but this doesn’t mean that an expensive new system is always required. Many organisations already have ERP systems to provide the base data, and the software vendors have extended their footprint to include tools for planning, budgeting and forecasting as well as more advanced analytics.
However, not all companies are following the ERP aligned route. Other vendors provide a full financial analytics suite separate to ERP, and many other companies provide best of breed analytics tools.
More important than the choice of analytics tools is ensuring that the data is structured in a model that facilitates the required analysis, with sufficient quality. There are a variety of specific tools to manage data definitions and quality, both within the major suites and also as point solutions.
In terms of visualising the data, there are many tools that can do the job. However, in certain domains (such as the web), open source and / or agile approaches to deployment are more commonly being adopted, because they are usually more cost effective and faster in terms of obtaining results.
Conclusion
As a data focused function, finance is well positioned to leverage the power of analytics to enhance business practice. However, before undertaking detailed analysis, it is important to adopt a structured approach (like the example articulated in the previous article).
Analytics projects should be aimed at answering specific business questions, with measurable business objectives. The results of any analysis must be incorporated into business practice in order to derive any value from it.
The next article will examine cross industry case studies of analytics projects that have followed that approach and the benefits they achieved. For further information on Deloitte Analytics, please contact Gillian Bishop (gibishop@deloitte.co.uk).
Links
Analytics: exploiting the power of data
CIMA in business