Finance chiefs must perform a balancing act. They have to maintain a constant flow of checks and balances, run the most efficient and effective business possible, and drive economic value for the company — all while working with peers across the business to execute the business strategy. One way that leading CFOs today are pursuing this mixture of goals is by leveraging analytics to make more insight-driven decisions.

Brian McCarthy

Brian McCarthy

CFOs and finance organizations have access to a tremendous amount of data — for instance, transactional data produced from PoS systems or bank deposits, consumer behavior data, operational data (e.g., sensor data) generated throughout a supply chain, and more — that can contain valuable insights to enable decision-making. By pursuing a data-driven approach, CFOs and their teams can more easily benchmark activity and compare results. In that way, they can determine the most effective way to allocate resources such as talent, capital, and expense dollars (e.g., marketing).

The quicker CFOs can uncover insights, the quicker they can make insight-driven decisions and take actions that can improve the company’s performance. But while technology is getting faster (and cheaper), some companies are struggling to move their organizations at an equivalent pace.

One of the best ways to overcome this challenge is by implementing an agile analytics operating model. With greater analytics agility, a CFO can help his or her company transform into an insight-powered enterprise that can defend, differentiate, and disrupt in its market.

Agile Analytics

CFOs can be change agents within companies and help to shape their organizations’ analytics operating models. Following are three elements of an effective agile analytics operating model that CFOs can use to drive high performance in a finance organization — and consequently, more broadly across the enterprise:

1. Idea generation. CFOs can spark performance-enhancing idea generation in a number of ways. For instance, they can help to set up an open-innovation model — an approach in which a business leader creates new relationships across the internal organization and outside the company, with academic institutions and startups for, example, to collaborate and generate new ideas for applying analytics to business problems. The premise is simple: More minds can create more ideas and opportunity. A consumer packaged goods company, for instance, applied this innovation model to determine a more effective product launch strategy. Based on the larger team’s inputs, the company developed an integrated analytics model and a new machine learning algorithm to support the launch objectives. Using a short testing period, employees uncovered analytics insights that enabled the company to make data-driven decisions on sales-force allocation, product pricing, and inventory volumes to optimize revenue and the profitability of the new product launch.

It’s also important for an organization to democratize its data and analytics and place the insights in the hands of a variety of users, including CFOs, finance managers, and others within the organization. Tools that help accomplish this democratization include data visualization technologies and advanced analytics applications. For instance, an insurance company could enable field agents to view insights remotely on mobile devices. That could help them prioritize which small-business customers to contact with proposed changes to the customers’ insurance coverage.

Of course, such new approaches could be counter-cultural for finance organizations accustomed to focusing on control and accounting precision rather than democratization.

 2. Maximizing the value from the ideas. Once good ideas are uncovered, the next step is to maximize their value. To do this with maximum effect, CFOs and other top executives should share the analytics capability goals. Plus, CFO should infuse finance talent on each of the analytics projects’ interdisciplinary teams with a stated goal supported by such things as data models and visualization. The teams should look to discover analytics insights with a view to solving complex problems speedily, taking a “test and learn” approach to target and attain value.

3. Measuring and tracking performance. For CFOs to have clear visibility into the effectiveness of their agile analytics operating model, they need have baselines for an activity’s performance, target its improvement, and then test the activity via pilot programs. In that way, finance chiefs can learn whether the targeted result was attained. (For instance, a bank might have a baseline of 2.2 average of products per customer but want to drive a cross-sell capability to achieve what it has found to be an industry best practice of greater than 3).

The ability to course-correct can be a huge competitive advantage. CFOs can play a key role in influencing the business to stress certain initiatives or switch gears when greater impact and value can be achieved. A finance team’s forecast scenario-planning results may, in fact, provide CFOs with the insight needed to make data-driven business recommendations. For instance, if analytics insights show that a company will be experiencing lagging sales, inventory overstocks, or another undesirable business situation, CFOs could suggest a specific course correction to improve the business result. The could reallocate sales force to other activities, alter marketing spend, or adjust the product pricing or discounting strategy.

Real-time analysis can be key for retailers during the holiday season. They can, for instance, view the sales outcomes and dynamically adjust the product promotions and store salesforce to help improve sales on a daily or more frequent basis (as in online sales of constrained inventory). As the results prove that the analytics capabilities can drive value, the ability to quickly scale the benefits across the organization will be key.

Raising the Bar

There are many ways CFOs and financial organizations can use the insights made possible by data analysis to improve performance in an agile way.

One area is talent management. Talent in financial organizations today is primarily skilled in the areas of financial management and planning. With the opportunity that data insights can offer a business, CFOs could look to transform this talent pool into a digital workforce. Such a shift means that the finance staff could spend more time on decision support, predictive analytics, and performance management.

For instance, the skillsets of the existing financial planning and analysis workforce could be expanded through training on analytics methods and tools such as time series forecasting, Monte Carlo simulation, and gamification

Another area is risk management. Analytics can help CFOs manage compliance, control, and risk in a more nimble way. By time stamping and tracking data, the finance organization grants the company greater opportunities for transparency and securities.

Time-stamped and tracked data can be analyzed to identify fraudulent behavior, for instance, so decisions and actions can be taken to stop the activity and protect the business. One example I know about: Through a data-discovery project, a bank found that the speed at which fields were filled out on its online forms was highly correlated to fraudulent activity. The company was then able to embed machine-learning algorithms in its online and mobile channels to predict and prevent fraud and the associated losses.

As a company’s overall strategy shifts, the finance function’s strategy will also have to do the same, and quickly. When CFOs are more agile and can make insight-driven decisions, they are better equipped to help the finance organization—and the entire company—improve its performance, adapt and adjust resources where needed, and drive greater economic value over time.

Brian McCarthy is a managing director for Accenture Analytics and the analytics advisory services lead for the consulting firm.

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2 responses to “A Manual for the Data-Driven Finance Chief”

  1. Analytics are great and important; but CFOs tell me that they do not trust their data. Given they are left manually fixing and consolating data. Without timely, trustworthy, and consolidated data; analytics have no value in finance or the business.

    • I hear that too but I find that if they have a general understanding of the source’s Data Model and related processes, trust is no longer an issue. It then becomes a question of making sure the analytics accord with the Data Model and processes.

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