Top-performing finance functions are keeping automation high on their agendas, but it’s just the start of a bigger push for performance.
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.
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.
Many of our clients are competing with agile start-ups, so the investment is based on a desire to change an existing operating model. There’s a strategic need for finance to have a different dialogue with the business, because that’s what helps the business become more agile.
We’re helping make the connections from the strategy and business plans down to specific investments that are linked to them. This creates a new view of the business and new perspectives on how the business is performing. When it’s tied to planning and data and analytics, it’s a very powerful new area of insight.
When we partner with the business, we help them understand how cost allocations really work—and we can come together on understanding the drivers of cost. We also help business leaders avoid management decisions that end up being numbers games that mask other issues.
It’s an opportunity. Finance can step up and own performance measures. For example, if you are measuring back-office performance by cost per revenue or HR cost per employee, finance should own those metrics, publish them and ensure their quality. They should also report what the drivers of those costs are.
People data is a way to get a consistent look at human capital across any organization. Both functions should want standardized, well-governed data. It provides a comparable way of looking at how the whole enterprise hires, evaluates, promotes, and rewards employees.
Finance should be moving away from producing P&L statements and toward management information and management reporting that’s rich in analytics and insight. All this relies on data and processes that are strong and common across the business.
Bring finance use cases closer to revenue-generating use cases, because that’s where the value is. For example, if treasury gets an earlier predictive signal on the availability of working capital, it can lead to a higher rate of return. Business is more likely to invest in such use cases.
Consumer and Industrial Products & Services Finance Leader, PwC US
Finance Transformation Leader, PwC US
Director of Finance Effectiveness, PwC US