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Your finance function is ready for change. Are you?

Top-performing finance functions are keeping automation high on their agendas, but it’s just the start of a bigger push for performance.


Why it matters

When teams from finance and business focus on performance, they can reach any goal: increase future profits, break into new markets, protect against new entrants or increase the sustainability of earnings. Together, their data analysis uncovers insights that drive the enterprise forward.

In food and beverage companies, for example, finance teams can pair historical sales data with all sorts of data—from the number of children in the household to weather to traffic to trending menu items—and personalize offers to increase same-store sales. Spotting where and when to revitalize or replace menu items becomes clearer, too, as teams begin to hypothesize where tastes are going next.

Getting management support for investing in your competitive differentiators helps to balance short-term profitability with long-term value creation. Yet we know from studying how companies drive their strategies that only 13% of business leaders believe the few most important capabilities are reflected in the company’s management processes. And these are reflected in budgets, leaders taking on responsibility for building those capabilities and the management team reviewing progress toward building them.

What’s working

Close collaboration among finance, IT and business teams is necessary to get agreement on the underlying building blocks in place that will drive competitive differentiators. Typically dynamic data models, these working models are evolving to get faster, more secure or more efficient with automated data cleansing and intelligent automation. Uses of artificial intelligence or even blockchain are part of the value creation, if there’s a business case using these technologies.

Outside of data or technology choices, there are two further crucial aspects that lead to success:

1. Skills in hypothesizing: A winning hypothesis articulates assumptions, then it needs data to back it up, otherwise there’s no basis for action. For the finance function, backing up assertions with numbers isn’t new. What’s new is the mind-set for creating granular hypotheses about what could give the business an edge and testing it with people who can act on it.

At KAYAK, the travel search-engine provider, the CFO seeks to identify this mind-set in the hiring process. The team looks for people who can be put in front of large data pools and work with software engineering teams to find insights that matter. They’re given the freedom to state their own hypotheses and they’re asked to be critical of their own progress against what they think they can find.

Delivering insight is a far cry from generating hundreds of pages of static reports. The goal is to zero in on key questions for where the business is now, and what’s needed to move forward.

2. Getting out the story in the data: Delivering insight is a far cry from generating hundreds of pages of static reports. The goal is to zero in on key questions for where the business is now, and what’s needed to move forward. This may mean shaping dynamic self-service dashboards to show comparisons, automate flags or ranges or link more detailed data so that business users can get more context (like being able to drill into social media posts from dynamic dashboards).

Voice search tools are helping to drive this shift as well. Instead of having to pull down a report, users can ask basic questions like: Can you send me a balance sheet? What’s selling in China? In leading companies, finance has anticipated some common questions for voice search to help the business be nimbler and faster.

Dynamic reporting means you may need to get used to different insights as models evolve. As more unstructured data gets incorporated into data models, and AI is layered in to analyze it all, your teams will need to understand new patterns and trends—and their implications on the business.


  • Select finance performance pilots to hook early adopters. Start with specific use cases that have a good chance of adoption in the organization and pilot them. Pinpoint on the types of data you need. For example, in an existing revenue model, you could start layering in unstructured data coming from social media platforms to track customer sentiment.


  • Self-service financial data must equal better service. Self-service dashboards drive nimble decision-making, but business teams still need support from finance to draw conclusions or validate decisions. Keep collaboration strong with regular touchpoints so that both finance and business teams have the same interpretation of the data and the same basis for measuring performance.

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Robert Bishop

Consumer and Industrial Products & Services Finance Leader, PwC US

Christopher Dimuzio

Finance Transformation Leader, PwC US

Ed Shapiro

Director of Finance Effectiveness, PwC US

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