Data-driven finance: The burden of the head-start curse?

Many companies think their finance department is ahead of the curve when it comes to working with data. But is that actually true? In many cases it turns out the finance department barely works data-driven at all. In this blog I'll tell you more about data-driven working within finance.

A finance department without data is simply unthinkable. In many companies it's precisely the finance department where people have been working with data for years, with the rest of the organization lagging miles behind when it comes to data use. That doesn't mean, however, that the finance department therefore works data-driven — in fact, in many cases what occurs here is the law of the head-start curse: because finance started working with data a long time ago, it's costly and complex to bring this situation up to the latest state of technology. And why would you? With Excel combined with your accounting system, you have everything you need to meet the reporting requirements, right? No doubt. And the benefit for a finance department of working data-driven usually doesn't lie in the quality of the reporting anyway.

Definition of working with data

First, a matter of definition: working with data (however reliable and accurate) is something different from working data-driven. When working with data, or "data-informed" working, you use available data to substantiate your decisions, and you try to steer your processes by analyzing the available data. If you truly work data-driven, you go a step further: all regular decisions are then no longer made based on data, but (automatically) carried out based on logical choices that follow from the supplied data. With data-informed working, you make every decision yourself, with the help of data. With data-driven working, the decision becomes part of the process, and you focus your attention only on the exceptions to the standard process.

An example: a company aligns production with sales. In a data-informed organization, based on higher-than-expected revenue, management will make the choice to increase production, and hire an extra production employee for this. In a data-driven organization, the software analyzes whether the increased revenue still fits within the calculated productivity, and whether it concerns a cyclical increase. This automatically checks whether an employee is really needed to increase production, or whether production capacity could perhaps be shifted from another product instead.

In the first scenario, the basic decision lies with management, and alongside a number of facts, a good dose of intuition will also play a role in the decision. In the second scenario, in the ideal situation, management isn't even bothered with the production question, because it can simply be resolved within the existing parameters.

The step toward data-driven working

Back when most finance departments started working with data, this was still a thing of the future. Having data available for reporting was the first step, and although this has gone well in many cases, the step toward data-driven working is still a big one for many companies.

That's why it's important to be honest about the business case: if your emphasis within finance is on record-keeping and reporting, there isn't much to gain by making the switch to data-driven working. Of course you can save yourself some time and effort by automating those reports, but that doesn't deliver truly decisive benefits.

The role of finance within your organization

To really gain an advantage from data-driven working within the finance department, it's necessary to shape the role of finance within your organization differently. Of course, reporting and accountability are an integral part of finance's tasks, but it's certainly just as important to start using finance as a navigation aid for the decisions ahead of you. A data-driven finance department can help an organization spot opportunities and bottlenecks in processes, help determine risks for new initiatives, and warn of trend breaks long before they become visible in the historical data. Investing in a data-driven finance department gives your organization a sharper picture of the future and the decisions needed, without requiring an army of analysts to calculate everything. That transition — precisely because of that head-start curse — isn't always easy. The organization's reporting needs can't simply be put on pause, and the fact that the books need to stay accurate is also beyond dispute.

So what then?

It's important to describe in advance exactly which applications you see for data-driven working within your finance department, and what's needed for that. By starting with such a "data strategy for finance," you get a picture of the needs and possibilities within your data landscape. This way you avoid a scenario where you only start noticing benefits once you've already replaced all the tools in your finance landscape. You avoid investing in tools you might no longer need.

As always, it's important to make the transition to data-driven working feasible and scalable, so that data-driven working within finance doesn't have to be a cost item. A good data strategy helps you clearly outline the benefits of your new approach so you take the right smart steps in the right order. This is how you work toward a truly data-driven finance department.

Written by Louis de Roo
Data Strategy Leader
E-mergo