Data-driven decision-making by board members

Before any successful data dive can commence, it’s important to understand the specific questions the board is trying to answer. It might have high-level concerns about what’s driving profitability or want to understand which segments have the greatest opportunity to drive greater efficiencies. If this is the case, these are the types of questions they will likely include: Who makes up a significant portion of our customer base? How profitable are those customers?

Alternatively, the board might be seeking greater insight into a pressing business challenge, such as employee turnover. Questions that might be asked in this regard include: What are the costs to hire and train a new employee, and are we doing enough to retain our current staff? How satisfied is our workforce with their roles and potential to grow within the company? What is the turnover by region in key areas of the business that are labor intensive?

Starting with a question will allow finance teams to create more targeted dashboards that can drive meaningful insights. Without such guidance, data dashboards can quickly become shiny objects that look nice but aren’t particularly useful.

Once finance knows the questions it needs to help answer, it’s easier to identify the key performance indicators (KPIs) that will help reveal the answer and focus the board’s attention on the right data. But it’s still important to understand the audience and what information would be meaningful to them when selecting the right metrics.

If the board, for example, has a strong focus on profit and cash flows, the KPIs to measure—and highlight when there are significant fluctuations—may include metrics like gross margin, liquidity, and cash flow trends over time. On the operational side, tracking the relationship between customer retention rates and gross margin may yield new insights about opportunities to enhance customer satisfaction.

Even so, finance teams have historically been inundated with such information requests, which can require significant time pulling the relevant data, leaving no time for actual analysis. Automation can help here—by eliminating repetitive, high-volume, rules-based tasks. To the extent such time-saving solutions can be deployed, individuals formally tasked with downloading reports from various sources, combining the information in a consolidated summary, and adding formulas and other formatting can focus instead on what comes next, which is the analysis.

The more that finance teams can offload data collection, the more time they will have to study the results of the collection efforts and the linkages between different sets of KPIs.

They might consider linking sales and marketing KPIs with their financial KPIs to identify relationships and trends that they may not have been aware of before. For instance, there might be a relationship between customer retention rates and gross margin, creating an opportunity to improve some internal processes to strengthen customer satisfaction and, ultimately, boost sales and profits.

When analyzing data, it’s important to put the data into context. As an example, if revenue is down 3%, but the market trend has shown a decline of 10%, this means your company has outperformed your competitors—or there may be other external factors driving fluctuations in performance.

Finance should also challenge itself to move beyond descriptive analytics, which helps explain the company’s current performance, into the realm of predictive analytics, which provides more insights about future outcomes and trends. Already, FP&A teams are turning to natural language generation to draft responses to what caused differences between budget allotments and actuals for a given reporting period. Along similar lines, machine learning tools can be deployed to determine the best course when boards face big strategic decisions.

This might be a big leap for some organizations and therefore may require finance leaders to supplement their team’s skill sets with those required to clear the gap.

How finance shares information with the board is critical to getting the right information across. Most people are able to process information better visually—a big reason data visualization (representing data through statistical and informational graphics) is starting to make inroads in C-suites and boardrooms.

Instead of including typical spreadsheet formatting, which can make it difficult to highlight what’s relevant to the board, segment the information that’s most relevant, and use visuals that help answer the original question. Today, custom dashboards can segment information to help board members drill down through data dashboards as they wish. For example, in a visualization depicting collection trends among customers, the user should be able to click on an underperforming customer to get more information, such as invoices by date and outstanding balances.

In the end, finance should be able to generate the kinds of insights from data that can provide measurable value to the business. To do this, the dashboards presented should consider the audience, the type of data to be visualized, and which charts/graphics will communicate the message most effectively. The challenge is to tell the story, and that means presenting the information in a way that doesn’t bury the lead. That means it’s important to summarize key findings and projections and make it as easy as possible for the reader to find the substantiation.

This is another area where, from a skills perspective, finance may need a new skill set that it typically hasn’t had, which is telling stories. For instance, if the board made a big change operationally two years ago, they would want to see the impact it has had on the company, including on its profitability, as well as recommendations from finance about what kinds of changes might lead to further improvements. It’s all about building a narrative—from beginning to middle to end—that is to the point and doesn’t waste space or the board’s time.