How finance can promote data-led decision-making

Data-led decision-making has become increasingly important, particularly during the coronavirus pandemic. It is most useful when executive teams apply it well and ensure the integrity of the underlying data.

Finance is at the centre of the rising demand for data. A 2020 Accenture study suggests that about half of finance functions have received more requests for financial data, price and trade-related data, sentiment analysis, and risk and compliance data. The demand for data is even higher for finance functions in high-growth organisations.

“As CFOs, our decisions make a big impact across our entire organisations, and those decisions should be supported with timely, relevant, and highly accurate data,” said Kayla Waters, CFO of TPN, a global agency that uses data and insights to execute brand experiences, create brand affinity, and optimise growth for manufacturers and retailers around the world.

As the demand for data increases, CFOs play a greater role in data usage in their organisations.

“It’s crucial as a CFO to ensure you’re aligned with your leadership team and help them understand how and why specific data should be informing company decisions,” Waters said. “Buy-in and alignment from your leadership team — paired with meaningful data — allow for sound decision-making.”

Metrics that matter

Data and data analysis hold the promise to increase revenue, decrease costs, mitigate risk, and help a business compete more effectively. To achieve this promise, a Deloitte report suggests, executives must carefully think through what they want to accomplish with analytics, the organisational culture must allow decision-makers to ask the right questions, and processes must be in place to generate predictive insights from data.

Finance leaders should map out the unique drivers of their own business’s financial performance, said Brett Kaplan, the CFO of Oneflare, an Australian online marketplace for local services such as plumbing and electrical work.

He checks key metrics daily. He has access to a data analytics team whose members have piped raw business data from various data warehouses into a data visualisation tool from Tableau that informs him, the leadership team, and other end users such as product managers.

“[It] gives me custom charts and tables covering various volume and efficiency metrics that I open as a matter of practice every morning when I get in,” he said. “I can drill down and filter any of these charts and tables if I need more specific reporting.”

He’s mindful of the danger of averages and makes sure he delves into the data to understand what he calls “sub-drivers”.

“Our business, for example, tracks just under 200 service categories that, on average, can look quite different from the underlying individual categories,” Kaplan said. “That is why a periodic review of the sub-drivers is essential.”

Guardians of good data

Being data-driven in decision-making is a central feature of how CFO roles have evolved, Waters said. “There is a big operational aspect of the job, which requires CFOs to really work hand-in-hand with other members of the executive and leadership team.”

This means CFOs are being asked to interpret the business’s intrinsic workings, structure, and performance — beyond traditional financials. The demand for these insights can create added pressures around data integrity.

“There are always risks when data is involved,” Waters said. “Essentially, if you are pulling inaccurate or outdated data, then you’re putting yourself and your company at risk to be making uninformed decisions. My recommendation is to always make sure you have accurate inputs. And it’s critical to remember that every single person at a company is involved and responsible for these inputs.”

To enhance and integrate data sources in the finance function, Deloitte suggests that CFOs:

• Define the goal. Decide what insights you need to run the business, what questions you need answered, and what metrics help answer those questions. All key parties need to agree on what will be measured, how it will be defined, who owns it, who will be accountable for producing it, and the business mandate being addressed.
• Start small. Test a concept in one market or line of business and start with no more than ten business questions so you can create visualisations of important results and explore relationships across data points.
• Fail fast. If something’s not working, fix it or move on quickly so you don’t lose momentum.
• Focus on customers. Involve the people who will be using the new capability you plan to introduce early on to ensure it meets their needs.
• Promote adoption. Socialise your idea to gauge support and have a rollout strategy that encourages people to adopt the new solution.
• Address the operating model. Involve all your key partners as you define new roles, processes, ways of working, and skills needed post-implementation. A data ecosystem based on next-generation digital technologies will likely demand new or enhanced workforce skills and capabilities, such as storytelling with data, problem-solving using advanced analytics, and business partnering.

— Luke O’Neill is a Sydney-based freelance writer and the owner of Genuine Communications. To comment on this article or to suggest an idea for another article, contact Sabine Vollmer, an FM magazine senior editor, at Sabine.Vollmer@aicpa-cima.com.